AI Security

Agent observability makes an agentic system legible after the fact. State, decisions, tool calls — captured, replayable, auditable.

The vocabulary is borrowed from distributed systems: traces, spans, W3C Trace Context. The bending happens when you apply it to language model calls, because those are non-deterministic.

That single property breaks most of what traditional observability assumes.

In a conventional distributed system, the same input produces the same path. You debug by finding the divergence from expected behavior. With an agent, two runs of the same request may legitimately take different routes — different tools, different order, different number of steps.

So the question shifts. Not “did this behave as specified” but “what did it actually do, and can I reconstruct why.”

That reconstruction requirement is why this article exists. It is the shared dependency underneath every AI control worth having, and it is the one most commonly assumed rather than built.

Key Takeaways

• Agent observability is the unstated dependency underneath every other AI control. Detection, attribution, incident response, compliance evidence and cost attribution all fail without it. The OpenTelemetry GenAI semantic conventions are widely described as standardized. They are not. As of 21 August 2026 the dedicated conventions repository marks them Development with no official release. Two of the most visible LLM observability brands were acquired within a single quarter. Instrument to the convention, not to a vendor. A tool that logs prompt and response pairs is shipping log search. Real agent observability emits spans with tool calls, correlation IDs and full reasoning chains. In the disclosed lab containment failures of mid-2026, two of three affected organisations had not detected the activity at all.

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Why Agent Observability Breaks Old Assumptions

Four specific assumptions fail when you move from services to agents.

Fixed call graphs. Traditional tracing assumes a service topology you can draw. An agent decides its own path at runtime, so the trace shape is an output rather than a design artefact.

Errors as the signal. In conventional systems, failures throw exceptions. An agent can complete successfully while doing entirely the wrong thing. A 200 response tells you nothing about whether the action was correct or authorized.

Latency as the metric. Response time matters, but token consumption, tool-call count and reasoning depth matter more for both cost and correctness.

Sampling by volume. Standard practice samples a percentage of traffic to control cost. For agents, the interesting traces are the rare ones — the long chains, the unusual tool sequences, the sessions where something went sideways. Uniform sampling systematically discards them.

The practical consequence: an observability stack that works well for your microservices will produce confident-looking dashboards about your agents while missing the failure modes that actually matter. This is the distinction between systems that generate output and systems that take actions.

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The Four Signals Agent Observability Must Emit

The OpenTelemetry GenAI conventions define a minimum span shape. Grouped by what they let you answer, four signals matter.

Signal 1 — Model calls. Which model, which provider, which operation. The canonical attributes are gen_ai.provider.name, gen_ai.operation.name, gen_ai.request.model and gen_ai.response.model. Requested and served model can differ under routing, and that difference is worth capturing.

Signal 2 — Token usage. gen_ai.usage.input_tokens and gen_ai.usage.output_tokens, per call. This is the foundation of cost attribution, and per-call granularity is what allows cost per completed task rather than cost per month — the distinction that makes inference cost per token actionable.

Signal 3 — Tool calls. gen_ai.tool.name, plus arguments and results. Each tool invocation becomes a child span. This is the security-relevant signal: it is where an agent’s intentions become actions against real systems.

Signal 4 — Agent and correlation context. gen_ai.agent.name, gen_ai.agent.description, and W3C Trace Context correlation IDs binding the chain together across services and across agents.

The fourth signal is the one most implementations skip, and it is the one that makes the other three useful. Without correlation context you have a pile of individually well-formed spans and no way to reconstruct the sequence.

Agent-specific conventions covering tasks, actions, memory and agent-to-agent communication were drafted in 2025 and moved into experimental status through 2026. Framework-specific conventions for CrewAI, AutoGen, LangGraph and Semantic Kernel remain in active development.

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The Agent Observability Standard Is Not Finished

This correction matters if you are planning around the standard, because a great deal of published material overstates its maturity.

The GenAI Special Interest Group has developed these conventions since April 2024. The semantic-conventions repository cut v1.40.0 in February 2026. In June 2026 the project moved GenAI, provider-specific and MCP conventions into a dedicated repository so they could version independently.

That repository marks the GenAI conventions as Development, and as of 21 August 2026 it has no official release. The gen_ai.* namespace remains experimental.

Two things follow, and they point in opposite directions.

Adopt anyway. OpenTelemetry itself graduated within CNCF in May 2026, which removes the project-maturity objection even if this particular namespace is unstable. Major vendors have already implemented — Datadog added native support in v1.37, Grafana collects LLM traces in Loki. The conventions are the closest thing to a neutral vocabulary that exists.

Pin your versions. Treat the conventions as a versioned contract rather than a stable API. Attribute names in an experimental namespace can change, and silent data breakage — where your dashboards keep rendering while the underlying field stops populating — is the failure mode to guard against.

The churn is concentrated at the edges: multimodal content, agent graphs, and MCP. Core model-call and token attributes are comparatively settled.

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The Sorting Test for Agent Observability Tools

The term is widely misused, and there is a short test that sorts the market.

A tool that logs prompt and response pairs is shipping log search. That is a legitimate product and it is not agent observability. It cannot show you what tools were called, in what order, on whose authority, or where a chain went wrong.

Real agent observability emits OpenTelemetry-compatible spans with the GenAI conventions applied, supports multi-step trace reconstruction, and correlates across services.

Three questions to ask a vendor:

Do you emit OTel-compatible spans, or import them only? Import-only means you are locked in at the layer where portability matters.

Can I reconstruct a full agent chain, including sub-agent delegation? Multi-agent systems break most trace models, and this is where the gap shows.

What happens to my data if I switch backends? If the answer involves re-instrumenting your application, you instrumented to a vendor rather than a standard.

The platform landscape as of 2026 includes LangSmith from the LangChain team, Arize AI with its open-source Phoenix project, evaluation-first Braintrust, and AWS Bedrock AgentCore Observability. Datadog pulled LLM workloads into its existing APM footprint.

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Why Every Control Assumes Agent Observability

Here is the argument that motivated this article. Take any AI control you have read about this year and follow it to its dependency.

Prompt injection defence assumes you can see what entered the model’s context and which tool call followed. Without per-call traces, a successful injection is indistinguishable from normal operation.

Containment. In the disclosed lab incidents of mid-2026, two of three affected organisations had not detected the activity at all. The evidence that reconstructed those events came from the labs’ own evaluation logs, not the victims’ monitoring — a pattern set out in how five labs lost containment.

Identity and attribution. Distinct agent identity only produces value if actions are logged against it. A perfect identity architecture with no trace layer answers “who could have done this” and never “who did,” as covered in why shared credentials are the real exposure.

Least privilege. Scoping permissions requires knowing which permissions are actually exercised. Teams without tool-call telemetry over-grant because they cannot see what would break.

MCP security. OWASP includes insufficient logging in its Top 10 precisely because most clients and servers log almost nothing by default.

Red teaming. Measuring attack success rates requires observing outcomes across many attempts. Without traces, you are counting your own attempts rather than measuring the system.

Compliance evidence. Contemporaneous, tamper-evident logs are the strongest available evidence tier. Policies and documentation rank below them.

Cost control. Cost per completed task requires per-trace token attribution. A monthly bill tells you what you spent, not whether it was productive.

The pattern is consistent. Research indicates roughly 47% of deployed agents are actively monitored, which is the constraint underneath the AI agent security gap. Every control in the list above is being recommended into environments where roughly half the agents emit nothing.

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Agent Observability Costs You Have to Plan For

Three costs surprise teams, and each has a standard mitigation.

Storage volume. A single agent task can generate dozens of spans with full prompt and completion payloads. Traces are large compared to conventional application logs, and volume scales with reasoning depth rather than request count.

The mitigation is tiered retention: keep full payloads briefly, keep span metadata and structure far longer. Structure without payload still reconstructs the chain.

Evaluation cost. Running LLM-as-judge scoring over production traffic means paying for inference twice. Common practice samples 10–20% of traffic for evaluation, which balances quality coverage against spend.

Privacy exposure. Prompts and completions routinely contain personal data. Sending them to a third-party observability backend creates a data-protection question your instrumentation decision has already answered by default.

The mitigation is to sanitize at source — automated scrubbing inside the instrumentation wrapper, before the span leaves your process. Scrubbing at the backend means the data already crossed the boundary.

One design note worth stating plainly: sample for evaluation, not for observability. Evaluate a subset; trace everything. Dropping traces to save money removes exactly the rare sessions that justify having the system.

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The Vendor Consolidation Problem

In January 2026 ClickHouse acquired Langfuse. In April, Cisco announced its intent to acquire Galileo. Two of the most visible LLM observability brands changed hands in a single quarter.

Neither acquisition is inherently bad for users. Both illustrate a structural risk.

Observability instrumentation is expensive to change. It touches every code path that calls a model or a tool. If your spans are emitted in a vendor-proprietary shape, switching backends means re-instrumenting the application — which in practice means you do not switch, and your negotiating position erodes accordingly.

The defence is straightforward and worth stating as a rule: instrument to the standard, treat the vendor as a detail.

Emit OpenTelemetry spans with GenAI conventions applied. Export via OTLP. Choose a backend that ingests that natively rather than one that requires a proprietary SDK. Then a backend change is a configuration change.

This argument gets stronger, not weaker, from the conventions being experimental. An unstable open standard you can version-pin is a better foundation than a stable proprietary schema you cannot leave.

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What Agent Observability Cannot Do

A control worth having is worth stating the limits of, and three of these matter for planning.

Observability is detection, not prevention. A trace records that an agent deleted the records. It does not stop the deletion. Teams that instrument thoroughly and then treat the dashboard as a safeguard have bought visibility into harm rather than protection from it. Enforcement belongs at the gateway, in permission scoping, and in approval gates.

A complete trace does not mean a correct one. The hardest agent failures produce clean telemetry. Every span succeeds, every tool call returns 200, and the aggregate outcome is wrong — a specification-gaming failure where the agent pursued its objective through a route nobody intended. Nothing in the trace is flagged because nothing failed. Recognising this requires evaluating outcomes, not inspecting spans.

Volume defeats human review. An enterprise processing millions of agent actions cannot manually inspect traces. Without automated evaluation, anomaly detection and shape-based alerting layered on top, comprehensive tracing produces an archive nobody reads. The archive is still valuable after an incident; it does very little before one.

There is also a measurement subtlety worth naming. Instrumentation changes what it measures — capturing full prompt and completion payloads on every span adds latency and cost to the request path, and aggressive instrumentation of a latency-sensitive agent can degrade the experience you were trying to protect.

The workable position is layered. Trace comprehensively for reconstruction and evidence. Evaluate a sample for quality. Alert on behavioural shape rather than error status. And keep enforcement in a separate layer that does not depend on anyone reading a dashboard in time.

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Building Agent Observability That Lasts

Six steps, ordered by dependency.

• Instrument every layer, not just the model call. Spans for LLM invocations, retrieval steps and tool calls. Attributing latency or cost to a step requires a span for that step. Auto-instrumentation packages exist for OpenAI, Anthropic, LangChain and LlamaIndex.
• Propagate correlation IDs across agent boundaries. W3C Trace Context is the mechanism. Without it, a multi-agent system produces disconnected traces and delegation chains cannot be reconstructed.
• Attribute every span to a distinct identity. Not a shared service account. This is what converts a trace into evidence.
• Define a single schema source of truth. One module holding every span name, attribute and metric, with no raw telemetry literals elsewhere in the codebase. A drift-detection test then catches divergence automatically.
• Sanitise before you store. Scrubbing belongs in the instrumentation wrapper, not the backend.
• Alert on shape, not just failure. An agent that suddenly takes twelve tool calls where it usually takes three has not errored. It has changed behavior, and behavioural drift is the signal that matters when success responses can accompany wrong actions.

A useful readiness check: pick a task your agent completed last week and reconstruct it end to end — which model, which tools in what order, how many tokens, who authorized it, what it touched. If you cannot, every control built on top of that agent is running on an assumption.

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Primary sources

• OpenTelemetry semantic conventions repository
• OpenTelemetry GenAI documentation
• W3C Trace Context
• CNCF — OpenTelemetry graduation

Convention status reflects the dedicated GenAI conventions repository as of August 2026 and is changing actively. Verify current release status before relying on attribute stability.

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Frequently Asked Questions

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A few years ago, AI governance meant an ethics committee, a set of principles, and a slide deck the board saw once.

That will not survive an examination now.

The distinction auditors draw is between intent and enforcement. A policy document states what your organisation intends to do. It says nothing about whether that happened on any particular day, to any particular decision, involving any particular person.

Practitioners have a name for the failure mode: governance theatre. Static PDF policies and annual reviews look substantial on a shelf and produce nothing when a regulator asks to see a specific decision reconstructed.

The number that frames the problem: roughly 78% of enterprises have deployed AI, while only about 25% have formal governance documentation in place.

And the asymmetry that makes it urgent — building compliance evidence after the fact is exponentially harder than generating it continuously. Logs that were never captured cannot be recreated. A human review that was never recorded did not happen, evidentially speaking, even if it did happen in reality.

Key Takeaways

• 78% of enterprises have deployed AI. Only about 25% have formal governance documentation. That gap is an open audit finding waiting to be written up.
• A policy proves intent. Auditors ask for technical proof the policy was enforced, and audit trails are the largest evidence gap in most organisations.
• If your log shows a service account rather than a named human, the record does not substantiate the assertion. Attribution failure voids the evidence entirely.
• The EU AI Act sets two different clocks: logs for at least six months, technical documentation for ten years — covering every version, not just the current one.
• ISO 42001 certification can accelerate EU AI Act readiness by an estimated 30–40%, and leaves real gaps in conformity assessment, logging retention and post-market surveillance.

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The Four Tiers of AI Compliance Evidence

Not all evidence carries equal weight. Sorting it into tiers explains why well-resourced governance programmes still fail audits.

Tier 1 — Assertions. Policies, principles, acceptable-use documents, ethics statements. These establish that a standard exists. They prove intent and nothing else. Weakest tier, and the one organisations invest in most heavily.

Tier 2 — Attestations. Someone signed something. A sign-off form, a completed checklist, a manager’s approval. Better than an assertion because it names a responsible party, but self-reported and generated by the party being audited.

Tier 3 — Artefacts. Model cards, risk assessments, technical documentation, data lineage records, test results. Substantive and specific. Their limitation is that they are point-in-time snapshots, produced deliberately, and they drift from production reality between updates.

Tier 4 — Traces. Tamper-evident logs generated automatically at the moment an event occurs. Who accessed what data, under what authorization, with what outcome, at what time. Strongest tier because it is contemporaneous and not produced for the auditor.

The pattern that sinks audits: heavy investment in tiers 1 and 3, near-total absence at tier 4. Audit trails represent the largest evidence gap for most enterprises, and tier 4 is the one that answers “show me.”

Organisations that perform well under regulatory scrutiny are not those that spent most on governance tooling. They are those that can answer “show me” with records rather than policy documents.

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What Regulators Ask For First

Five documentation categories are emerging consistently across financial services, healthcare and critical infrastructure examinations.

AI inventory. A complete, current list of every AI system in use — including systems a team stood up without telling anyone. You cannot evidence governance over a population you cannot enumerate, and shadow AI is the most common first finding.

Risk classifications. Each system mapped to applicable regulatory categories, with the reasoning recorded. Not just “we assessed this,” but what the assessment concluded and on what basis.

Policy and control documentation. Written policies paired with the technical controls that enforce them. The pairing is the point; a policy without a matching control is a tier 1 assertion.

Audit trails. Per-decision records with inputs, model version, output, and any human review. This is where most examinations break down.

Incident history. What went wrong, when it was detected, what was done, and how it resolved. An empty incident log is not a good sign — it usually indicates you are not detecting incidents rather than not having them.

The most common substantive failure is describable in one sentence: an output exists with no reasoning, no context and no trail, so the decision cannot be reconstructed.

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The AI Compliance Evidence the EU AI Act Requires

The EU AI Act is the most prescriptive regime, so it is worth using as the reference standard even outside Europe.

For high-risk systems, the operative articles are:

Article	Requirement	Evidence produced
9	Risk management system per system	Risk register, treatment plans, review records
10	Data quality and governance	Proof training and validation data was reviewed for bias and coverage gaps, with remediation documented
11 + Annex IV	Technical documentation	Structured pack covering design through post-market monitoring
12	Automatic event logging	Operational audit trail enabling traceability across the lifecycle
13	Transparency toward deployers	Information packs enabling deployer oversight
14	Human oversight	Documented oversight design, monitoring capability, override paths
15	Accuracy and robustness	Pre-market testing per harmonised standards

Two structural points matter more than the list.

These are requirements on the system and its lifecycle, not on the organisation surrounding it. An organisational management system does not discharge them.

The articles chain. Article 12 requires logging capability. Annex IV then requires your technical documentation to describe that capability — what is logged, how logs are retained, who has access. You cannot write the documentation before building the logging, which is why teams that start with documentation stall.

Note the current timing. Standalone Annex III high-risk obligations moved to 2 December 2027 under the Digital Omnibus. Article 50 transparency duties and GPAI obligations did not move.

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Retention: How Long AI Compliance Evidence Must Live

Two clocks run simultaneously, and conflating them is a common planning error.

Logs: at least six months. Article 19 sets the floor for automatically generated logs, with deployers retaining them under Article 12.

Technical documentation: ten years. Article 18 requires retention for ten years from the date the system is placed on the market, and national authorities may request access at any point in that window.

The ten-year requirement has an implication most teams miss on first reading. It applies to all versions, not just the current one. Documentation for superseded model versions must remain accessible.

For an organisation retraining quarterly, that is forty documentation versions to keep coherent over a decade — each linked to its specific training dataset, model weights and deployment configuration. This extends well beyond typical experiment-tracking retention policies.

What the ten-year pack includes: every documentation version, change logs and modification impact assessments, test results and validation reports, risk management records, post-market monitoring data, correspondence with notified bodies, and the EU declaration of conformity.

US state requirements are shorter but structurally similar. Colorado’s successor statute requires three years of records demonstrating compliance, a duty falling on both developers and deployers as set out in state AI laws and what builders and deployers each owe.

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The Identity Problem That Voids AI Compliance Evidence

This is the failure mode least discussed and most likely to invalidate an otherwise complete evidence package.

An auditor asks for log records showing that authorization was evaluated and enforced for a specific access event. You produce them. If those records show a service account identity rather than a human user identity, the evidence does not substantiate the assertion.

The log exists. It is well-formed, timestamped and tamper-evident. And it proves nothing, because it cannot answer who acted.

The required standard is that AI data access logs contain individual user attribution, per-request policy enforcement decisions, and data-asset specificity equivalent to what is required for human data access. Most AI deployments currently generate logs meeting none of these.

This is where a security problem becomes an evidentiary one. When agents share credentials, attribution collapses — and attribution is the foundation every compliance claim rests on. The mechanics of that collapse are set out in why shared credentials are the real exposure.

The practical consequence for planning: distinct agent identity is not only a security control. It is a precondition for producing admissible evidence. Teams treating it as a security backlog item are deferring a compliance dependency.

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Why ISO 42001 Is Not Enough on Its Own

ISO 42001 certification is worth having, and it is not a substitute for regulatory compliance. Both statements are true and the distinction matters commercially.

Analysis suggests certified organisations can accelerate EU AI Act readiness by roughly 30–40%. The governance infrastructure transfers, particularly for the organisational sections of Annex IV.

The gaps are specific:

Conformity assessment procedures. ISO certification is not an EU conformity assessment. Self-assessment or notified body assessment per the Act’s requirements remains separate work.

EU database registration. No ISO equivalent exists.

Logging retention specifics. ISO 42001 leaves retention to organisational policy. The Act sets floors.

Post-market surveillance. Ongoing monitoring obligations with defined content.

Annex IV format. ISO establishes documentation practices without prescribing the Act’s specific sections. Certified organisations typically cover the organisational sections and still need to produce detailed architecture, data and validation documentation.

The useful framing: ISO 42001 evidences that a management system exists. The AI Act requires evidence about specific systems and their lifecycles. One is about the organisation, the other about the artefact.

NIST AI RMF sits differently again — it earns an explicit enforcement safe harbour under Texas TRAIGA, which neither ISO nor the EU framework provides.

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The AI Compliance Evidence Deployers Owe

Most coverage addresses providers. The heavier operational burden usually falls on deployers, and it is worth separating.

A provider produces evidence once per system version. A deployer produces evidence per decision, and the volume difference is enormous.

Three obligations drive it.

Pre-use notice records. Proof that a consumer was told before an AI system influenced a decision about them. Not a copy of your notice template — evidence that this particular person received it, when, and through what channel.

Adverse-outcome explanations. Where a decision went against someone, a plain-language account of the system’s role and the principal factors it used, typically within 30 days. This is the single heaviest lift in the current US frameworks, because it requires per-decision explainability that your model vendor may not supply and your contract may not oblige them to.

Human review records. Evidence that a meaningful review path existed and, where used, that a human with authority actually revisited the outcome.

The dependency worth flagging early: you cannot produce a deployer explanation from a provider who has not given you the underlying documentation. Intended uses, known limitations, the categories of data used in training — these flow from provider to deployer, and without them the deployer’s own obligation is unsatisfiable.

That makes evidence a procurement question. If your vendor contract does not name documentation as a deliverable, you have accepted an obligation you cannot discharge. Enterprises processing millions of AI-driven actions also find that manual audit preparation simply does not scale at this volume, which is what pushes evidence generation into the pipeline rather than a quarterly exercise.

One structural point closes the loop. The Act’s articles chain from provider to deployer the same way Article 12 chains to Annex IV. Provider documentation feeds deployer notices; deployer logs feed regulator inspections. A break anywhere in that chain surfaces at the far end, usually during an examination.

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AI Compliance Evidence for Autonomous Agents

Every framework above was written for systems that make decisions about people. Agents that chain tool calls, modify records and take actions fit awkwardly.

Singapore moved first. Its Model Governance Framework for agentic AI, unveiled 22 January 2026, is the first framework specifically addressing autonomous system documentation. It requires organisations to define agent authority boundaries, autonomy classifications, and decision audit trails.

Those three requirements are a reasonable evidence template regardless of jurisdiction.

Authority boundaries. What is this agent permitted to do, expressed as scope rather than capability. Documented before deployment, not inferred afterwards.

Autonomy classification. Which actions proceed automatically, which require approval, which are prohibited. This is where human-in-the-loop checkpoints become an evidence artefact rather than only a safety control — an approval record is tier 2 evidence with a named actor and timestamp.

Decision audit trails. The full chain: which agent, spawned by which agent, on whose authority, touching which resource.

That third item is where multi-agent systems break most evidence architectures. Each delegation hop erodes attribution, and a trail that cannot name the originating human authority will not substantiate a compliance assertion.

Testing records deserve a specific note here too. Where a framework requires adversarial testing, a single passing result is weak evidence — an argument developed in why one passing red team test proves nothing. Record attempt counts and confidence bounds, not verdicts.

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Generating AI Compliance Evidence Continuously

Six practices, ordered by dependency rather than difficulty.

Instrument before you document. Article 12 logging feeds Annex IV Section 3. Build the capability, then describe it. Teams that start with the document write fiction.

Attribute every action to a human or a distinctly identified agent. Service account logs do not substantiate assertions. This is the highest-leverage single fix.

Make storage tamper-evident. Standard database logs can be altered by a compromised admin account or a determined insider. Tamper-evident architecture is increasingly named in the technical annexes of governance frameworks.

Version documentation against system versions. Link each documentation version to the specific training dataset, model weights and deployment configuration it describes. Retention covers all versions.

Maintain a traceability matrix. Map each regulatory requirement to the specific document, section or record that addresses it. This is what makes an audit tractable rather than an archaeology project.

Capture human review as structured data. Who reviewed, when, what they saw, what they decided. Reviews recorded only in email threads or meeting notes are difficult to produce and easy to dispute.

The test to apply to your own programme: pick a decision your AI system made last quarter and try to reconstruct it. What data, which model version, what output, who reviewed it. If that takes more than an afternoon, you do not have an evidence layer — you have a policy binder.

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Primary sources

• EU AI Act, Regulation (EU) 2024/1689
• ISO/IEC 42001:2023
• NIST AI Risk Management Framework
• European Commission — AI Act implementation

Retention periods and article references reflect the AI Act as amended by the Digital Omnibus. This article is general information, not legal advice.

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Frequently Asked Questions

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A general-purpose AI model under the EU AI Act is a model capable of performing a wide range of distinct tasks. The obligations attach to the provider of the model, not to whoever eventually uses it.

Article 53 sets the baseline for every GPAI provider placing a model on the EU market:

• Technical documentation under Annex XI
• Information packs for downstream providers under Annex XII
• A copyright policy addressing the text and data mining opt-out
• A publicly available, sufficiently detailed summary of training data content

That last item is more demanding than it sounds. The Commission has published a template, and the summary must be updated at least every six months when a model is further trained on additional data.

Open-weight models get partial relief under Article 53(2). Technical documentation and downstream information requirements fall away for models with publicly available weights, architecture and usage terms. Copyright compliance and the training data summary still apply — and the exemption disappears entirely if the model crosses the systemic risk threshold.

These obligations have applied since 2 August 2025. They are not new, and nothing in the 2026 amendments touched them.

Key Takeaways

• “EU delays AI Act” headlines are producing a dangerous misreading. Only the high-risk tier moved. EU AI Act GPAI obligations never shifted, and Commission enforcement powers switched on 2 August 2026.
• The Digital Omnibus on AI — Regulation (EU) 2026/1744 — entered into force on 27 July 2026, deferring standalone high-risk obligations to 2 December 2027 and leaving Articles 51 to 55 untouched.
• The two regimes attach at different layers. The EU regulates the model by training compute. US states regulate the use by decision context. A company can be fully compliant in one and entirely out of scope in the other.
• The EU has one regulator with a €15 million or 3% of global turnover penalty. The US has fifty attorneys general, no federal statute, and an active preemption fight.
• The convergence points are real. Training-data disclosure and frontier safety frameworks appear in both regimes, so one artefact can satisfy two obligations.

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The EU AI Act GPAI Systemic Risk Threshold

Article 51(2) creates a second tier with a bright-line trigger: cumulative training compute exceeding 10^25 floating-point operations.

Three details about that threshold matter operationally.

It measures the training run only, not inference. This is a one-time characteristic of how the model was built.

The presumption is rebuttable. A provider above the threshold can argue its model does not present systemic risk, and the Commission can designate a model below the threshold as systemic-risk based on equivalent impact or capabilities.

Notification is fast. A provider whose model meets or is expected to meet the threshold must inform the AI Office immediately, and within two weeks at the latest.

Crossing the threshold triggers Article 55: model evaluations including adversarial testing, systemic risk assessment and mitigation, serious incident reporting directly to the AI Office rather than national authorities, adequate cybersecurity protection, and energy consumption reporting.

The GPAI Code of Practice, coordinated by the AI Office and recognized by the Commission and AI Board as an adequate compliance route, specifies the methodology. Its adversarial testing scope covers at minimum cyber attack assistance, biological and chemical weapon development assistance, large-scale disinformation generation, and critical infrastructure vulnerability exploitation.

That testing requirement deserves scrutiny on its own terms, because a single passing evaluation demonstrates very little — an argument set out in why one passing red team test proves nothing. OpenAI, Anthropic, Google and Mistral are among the Code’s signatories.

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Which EU AI Act GPAI Deadlines Moved

This section exists to correct a widespread misreading, and it is the single most useful thing in this article.

The European Parliament approved the Digital Omnibus on AI on 16 June 2026 by 423 votes to 57 with 174 abstentions. It was published in the Official Journal on 24 July 2026 as Regulation (EU) 2026/1744 and entered into force on 27 July 2026.

Headlines read “EU delays AI Act.” Teams concluded they have until December 2027.

Here is what actually happened.

Obligation	Original date	Current date	Status
Article 5 prohibitions	2 Feb 2025	2 Feb 2025	In force
Article 4 AI literacy	2 Feb 2025	2 Feb 2025	In force
GPAI obligations (Arts 51–55)	2 Aug 2025	2 Aug 2025	Unchanged, in force
Article 50 transparency	2 Aug 2026	2 Aug 2026	Unchanged, in force
AI Office GPAI enforcement powers	2 Aug 2026	2 Aug 2026	Active
Art 50(2) marking, pre-existing models	2 Aug 2026	2 Dec 2026	Short grace period
National regulatory sandboxes	2 Aug 2026	2 Aug 2027	Deferred 12 months
High-risk Annex III standalone	2 Aug 2026	2 Dec 2027	Deferred ~16 months
High-risk Annex I embedded	2 Aug 2027	2 Aug 2028	Deferred 12 months

The delay applies only to the high-risk tier. Chatbot disclosure, deepfake labeling and machine-readable marking of AI-generated content all landed on schedule eleven days ago. So did the Commission’s enforcement powers over GPAI providers.

Penalties for GPAI and Article 50 breaches run up to €15 million or 3% of global annual turnover, whichever is higher.

If you provide a model, nothing was postponed for you.

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EU AI Act GPAI Versus the US State Patchwork

The comparison most people expect is “strict Europe versus permissive America.” That framing is wrong and it leads teams to the wrong compliance work.

Dimension	EU AI Act GPAI	US state laws
What triggers coverage	Model characteristics (training compute)	Use context (consequential decisions)
Regulated party	Model provider	Developer and deployer separately
Territorial hook	Placing a model on the EU market	Affecting that state’s residents
Tiering basis	10^25 FLOP threshold	Decision domain and sector
Enforcement body	AI Office, centralised	State attorneys general, fragmented
Maximum penalty	€15M or 3% global turnover	Varies widely by state
Current status	In force, enforcement active	Patchwork, several deferred to 2027

The US position as of August 2026: no comprehensive federal statute exists. Texas TRAIGA and several California laws are in force. Colorado’s original AI Act was repealed and reenacted as SB 26-189, effective 1 January 2027 and subject to litigation. More than 2,000 AI-related bills have been introduced across the states.

The detail of who owes what on the US side is covered in state AI laws and what builders and deployers each owe.

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Gap 1: EU AI Act GPAI Regulates Models, US Law Regulates Use

This is the structural difference everything else follows from.

The EU asks: what is this model, and how much compute trained it? Obligations attach at the point the model is placed on the market, before anyone has used it for anything.

US states ask: what decision is this system influencing, and about whom? Obligations attach at the point of use, and the same model may be regulated in one deployment and unregulated in another.

Two practical consequences fall out, and both surprise people.

A frontier model provider can be heavily regulated in the EU and largely out of scope in most US states, because it never touches a consequential decision directly. Its customers do.

A small company using an off-the-shelf model for hiring is a US deployer with real obligations and, in the EU, is not a GPAI provider at all. It may be a high-risk deployer — but that tier now sits at December 2027.

The layers are complementary rather than competing. Anyone assuming EU compliance covers US exposure has misread which layer each regime occupies.

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Gap 2: One Regulator Versus Fifty

The EU concentrates authority. The AI Office oversees GPAI providers directly, receives Article 55 incident reports, and has held enforcement powers since 2 August 2026. One body, one interpretation, one escalation path.

The US distributes it. Enforcement sits with state attorneys general applying different statutes with different definitions and different deadlines.

Three operational consequences.

Interpretation is centralized in the EU and contested in the US. The Commission publishes guidelines, templates and a Code of Practice. In the US, one state’s reading of “materially influences” may not match another’s.

Reporting channels differ. Article 55(1)(c) incidents go to the AI Office, not national authorities. There is no US equivalent — no central body receives AI incident reports.

Safe harbours are jurisdiction-specific. Adhering to the GPAI Code of Practice is a recognized compliance route in the EU. Substantially complying with the NIST AI RMF earns an enforcement safe harbour under Texas TRAIGA. Colorado’s successor statute dropped its framework-based defence entirely. Three regimes, three different answers to “does following a standard protect me?”

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Gap 3: Compute Thresholds Versus Decision Context

The EU’s 10^25 FLOP line is objective, measurable and checkable by a third party. That is its strength and its weakness.

The strength: you know exactly which side you are on. Compute is a number.

The weakness: compute is a poor proxy for harm. A model trained below the threshold, deployed into a lending decision at scale, can cause more real-world damage than a frontier model used for code completion. The Commission’s designation power exists precisely to patch this, but designation is discretionary and slow.

US decision-context tiering has the mirror-image profile. It targets harm directly — employment, lending, housing, healthcare, insurance — which is where discrimination actually happens. But the boundaries are contested. Does an AI tool that ranks candidates “materially influence” a hiring decision if a human makes the final call? Different states answer differently, and no court has settled it.

Both regimes also share a blind spot worth naming: neither is built for autonomous agents. The EU asks about model characteristics; US states ask about decisions affecting consumers. An agent that chains tool calls, modifies records and takes irreversible actions fits neither frame cleanly, and both may leave it unaddressed — the practical fallback being human approval on high-consequence actions.

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Gap 4: EU AI Act GPAI Enforcement Versus US Litigation

The final gap concerns what “the law” even means right now in each jurisdiction.

In the EU, it means a regulation in force with an active supervisory authority. The Digital Omnibus was itself adopted through the ordinary legislative process, so even the amendments are settled law rather than pending change.

In the US, it means a live contest.

Executive Order 14365, signed 11 December 2025, directed the Attorney General to establish an AI Litigation Task Force to challenge state AI laws on interstate commerce and preemption theories, and directed a Commerce review that could condition federal broadband funding on a state’s AI posture. Colorado’s law was named specifically.

Colorado’s own statute is enjoined pending litigation, and its attorney general has indicated no enforcement until rulemaking completes — rulemaking that had not formally begun as of mid-2026.

No federal preemption has been enacted. But the practical difference is stark. EU obligations are stable enough to build a two-year compliance programme around. US obligations require quarterly re-verification because the map keeps redrawing itself.

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Where EU AI Act GPAI Rules Meet US Law

The gaps are real, and so is the overlap. Three convergence points let one artefact serve two regimes.

Training data disclosure. EU Article 53(1)(d) requires a public summary of training data content. California’s AB 2013 requires public documentation of training data for generative AI. The formats differ; the underlying work is largely the same, and doing it once to the EU template will substantially cover the California requirement.

Frontier safety frameworks. EU Article 55 requires systemic risk assessment, adversarial testing and incident reporting for models above the compute threshold. California’s SB 53 requires frontier developers to publish safety frameworks and report critical incidents. Both target the same population with similar demands.

Content provenance. EU Article 50(2) requires machine-readable marking of AI-generated content. California’s AI Transparency Act, operative since 2 August 2026, requires detection tooling and latent disclosures from large providers.

The sensible sequencing: build to the stricter requirement, map it to the looser one, and maintain a single evidence base. Three years of records satisfies most US state retention rules; the EU Code of Practice’s Model Documentation Form asks for ten.

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Building One Program for Both Regimes

Six steps, ordered by dependency.

Determine your role in each regime separately. GPAI provider is an EU concept keyed to the model. Developer and deployer are US concepts keyed to use. You may hold different roles in each, and one company can be all three.

Measure your training compute. If you train models, know your cumulative FLOP figure. It determines whether Article 55 applies and triggers a two-week notification clock.

Build the training data summary first. It is required by the EU regardless of tier, required by California for generative AI, and has a published template. Highest-leverage single artefact.

Do not defer on the strength of the high-risk delay. Article 50 transparency and GPAI enforcement are live. Only Annex III moved.

Pick a governance framework and document adherence. NIST AI RMF for the US safe harbour where it exists, GPAI Code of Practice for the EU. They overlap substantially in substance.

Re-verify quarterly on the US side and annually on the EU side. The asymmetry is deliberate: EU rules are settled, US rules are not.

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Primary sources

• EU AI Act, Regulation (EU) 2024/1689
• European Commission — AI Office and GPAI
• GPAI Code of Practice
• Texas HB 149 (TRAIGA)
• NIST AI Risk Management Framework

Statutory status changes frequently and several US provisions are subject to pending litigation. This article is general information, not legal advice.

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Frequently Asked Questions

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Keep reading

Here are the latest posts from the blog.

An agent skill is a folder of instructions, scripts and resources that an AI agent discovers and loads on demand. Anthropic introduced the concept in October 2025 and published the specification as an open standard on 18 December 2025, stewarded through the Agentic AI Foundation.

Adoption was unusually fast. By mid-2026 roughly 40 products supported the format, including Claude Code, Cursor, GitHub Copilot, VS Code, Codex, Amp, Letta and OpenCode. A skill written for one agent runs unmodified in a competitor’s.

The design principle is progressive disclosure. At startup, an agent pre-loads only the name and description of every installed skill. When a task matches, it loads the full SKILL.md. Only when sub-tasks require it does the agent reach deeper resources or execute code.

That design is efficient, and it creates the security surface. Every installed skill’s description sits in the model’s context at all times, whether or not the skill is used — and descriptions are natural language the model treats as guidance.

Agent skills security therefore spans four layers at once: the prose instructions, the bundled executable code, the registry the skill came from, and the permissions the skill inherits from its host agent.

Key Takeaways

• Standards for agent skills security now exist. OWASP’s Agentic Skills Top 10 documents ten risk categories with prescribed mitigations. Registry adoption of those mitigations is the part that has not happened.
• Snyk’s audit of 3,984 skills found 36.82% contained at least one security flaw, 13.4% at least one critical issue, and 76 with active malicious payloads.
• The ClawHavoc campaign placed 1,184 malicious skills across 12 publisher accounts sharing one command-and-control address. At peak infection, five of the seven most-downloaded skills were confirmed malware.
• Publishing to an open skill registry has typically required a SKILL.md file and a GitHub account at least one week old. No signing, no review, no sandbox by default.
• Signing is necessary but not sufficient. OWASP’s own guidance is explicit that a signature proves authorship, not safety.

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Quick Navigation

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Why Agent Skills Security Failed So Quickly

Package ecosystems took a decade to build provenance controls. npm and PyPI have signing, transparency logs, lockfiles and revocation because each was added after an incident forced it.

Skill registries started from zero and scaled faster than any of them.

ClawHub, the registry serving the OpenClaw agent framework, held 2,857 skills in early February 2026 and more than 70,000 by June. OpenClaw itself went viral in late January, crossing 145,000 GitHub stars and 100,000 users within two weeks.

The publishing requirement during that period was a SKILL.md file and a GitHub account at least one week old. No code signing. No security review. No sandbox by default.

Three properties made the resulting exposure unusually severe.

Skills execute with the host agent’s full permissions. A malicious skill gains whatever the agent has — API keys, SSH credentials, wallet files, browser data, shell access.

The payload can be prose. Unlike a package, a skill can attack purely through natural-language instructions in its markdown, with no code to scan.

Portability spreads compromise. The same skill format runs across registries, so a malicious skill ports from one marketplace to another unchanged.

This is the distinction between systems that generate text and systems that act, explored in agentic AI versus generative AI — and skills are precisely where agency gets granted.

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The Incident That Defined Agent Skills Security

February 2026 compressed what usually takes years.

Koi Security identified a coordinated wave of malicious uploads on ClawHub beginning 1 February. Its audit of all 2,857 skills then on the registry found 341 malicious.

Antiy Chert’s post-incident analysis confirmed the fuller scope: 1,184 malicious skills across 12 publisher accounts, sharing a single command-and-control address, delivering Atomic Stealer against macOS wallets, SSH keys and browser credentials. The campaign is now referred to as ClawHavoc.

The detail that should worry anyone running an open registry: at peak infection, five of the seven most-downloaded skills were confirmed malware. Download count functioned as a trust signal and was pointing at the wrong things.

Related disclosures landed in the same window. Check Point Research documented remote code execution in Claude Code through poisoned repository configuration files (CVE-2025-59536 and CVE-2025-21852). Oasis Security disclosed a WebSocket hijacking issue tracked as CVE-2026-28363. Microsoft Defender issued an advisory characterizing OpenClaw as untrusted code execution with persistent credential access.

One widely reported user incident illustrates the practical stakes: an OpenClaw bot granted iMessage access sent more than 500 messages to the owner’s contacts before he regained control.

ClawHub has since implemented automated scanning and partnered with VirusTotal. The broader ecosystem largely has not.

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What the Agent Skills Security Audits Found

Two audits give the clearest quantitative picture, and they disagree in an instructive way.

Snyk’s ToxicSkills audit, February 2026, scanned 3,984 skills across ClawHub and skills.sh:

• 1,467 skills (36.82%) contained at least one security flaw
• 13.4% contained at least one critical-level issue
• 76 were confirmed malicious with active payloads
• 280+ leaked credentials

A larger subsequent analysis of 42,447 skills found 26.1% carrying at least one vulnerability.

The gap between 36.82% and 26.1% is worth noting rather than papering over. Different populations, different scanning methodologies, different definitions of “flaw.” Both figures indicate that roughly a quarter to a third of published skills have problems, and neither should be quoted as a precise measurement.

The more actionable number is the malicious count. Confirmed active payloads ran at roughly 1.9% of the Snyk sample. Most flawed skills are badly written rather than hostile — but at registry scale, 1.9% of 70,000 is a large absolute number.

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Agent Skills Security Standards Now Exist

Here is the correction to a claim still circulating widely: it is no longer true that no standards exist.

OWASP’s Agentic Skills Top 10 (AST10), authored by Ken Huang and published as an OWASP Incubator Project during 2026, is the first comprehensive security framework aimed specifically at the skill layer — the markdown file, its frontmatter, its bundled scripts, its registry, and its inherited permissions.

ID	Risk	Severity	Key mitigation
AST01	Malicious Skills	Critical	Merkle root signing, behavioural scanning
AST02	Supply Chain Compromise	Critical	Transparency logs, dependency pinning
AST03	Over-Privileged Skills	High	Least-privilege manifests, runtime enforcement
AST04	Insecure Metadata	High	Schema validation, sandboxed loading
AST05	Untrusted External Instructions	High	Source inventory, content pinning, rescanning
AST06	Weak Isolation	High	Containerisation, process isolation
AST07	Update Drift	Medium	Immutable pinning, hash verification
AST08	Poor Scanning	Medium	Multi-tool pipeline, semantic analysis
AST09	No Governance	Medium	Skill inventories, audit logging
AST10	Cross-Platform Reuse	Medium	Universal format, platform validation

Publication dates cited across sources vary between March, April and a version 1.0 milestone in August 2026, so treat the exact date with caution. The framework itself is live and citable.

The real gap is not the absence of standards. It is that registries have not implemented them. Merkle root signing, transparency logs and revocation are prescribed and largely unbuilt. That distinction matters, because “nobody knows what to do” and “the fix is known and unadopted” call for completely different responses.

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Gap 1: No Provenance at Publish Time

The first gap is the one OWASP ranks most critical.

Skill registries generally lack the provenance controls that took npm and PyPI a decade to build: no signing, no transparency log, no lockfile, no revocation.

Without provenance, you cannot answer three basic questions. Who published this? Has it changed since I reviewed it? Can it be withdrawn if the publisher is compromised?

OWASP’s prescribed fix is Merkle root signing at the registry level, treating every publication as a cryptographically verifiable event — the same approach that hardened certificate transparency for browsers.

The implementation detail in OWASP’s whitepaper deserves attention because it is easy to get wrong. A signature must bind to a resolvable, revocable publisher identity — a key ID plus a publisher identifier such as a domain or did:web, plus a published verification key — rather than a bare key. And the public key must be resolved from a trust store keyed by publisher identity, never accepted from the skill payload itself, or a self-signed attacker key verifies successfully.

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Gap 2: Permissions Checked at the Wrong Layer

The second gap explains why over-privileged skills are so common.

Permission is typically checked at the tool call, not at the intent. A skill is either allowed to read files or it is not. Nothing evaluates whether this particular read fits what the skill is supposed to do.

OWASP’s illustrations are pointed: a weather assistant that reads the entire .env file, or a skill cleared for SELECT that gets talked into DELETE.

Both actions pass the permission check. Both are wildly outside the skill’s stated purpose.

The prescribed mitigation is least-privilege manifests with runtime enforcement — declaring what a skill needs, then enforcing that declaration at execution rather than trusting it at install. Roughly 280 skills in the Snyk sample leaked credentials, and most did so through access they were nominally allowed to have.

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Gap 3: Scanners Miss Natural-Language Payloads

The third gap is the most technically interesting, and it undercuts the industry’s default response.

When registries respond to incidents, they add scanning. ClawHub did exactly this, partnering with VirusTotal. That helps against executable payloads.

It does considerably less against prose.

Adversa AI contributed an eight-scanner bypass study cited in AST08, demonstrating pattern-matcher bypass via natural-language injection. A separate proof of concept referenced under AST05 reportedly bypassed all scanners tested.

The reason is structural. A malicious skill does not need code. It can carry instructions in its markdown that steer the agent toward harmful behavior, and there is no signature to match because the payload is a sentence.

OWASP’s answer is semantic and behavioral analysis in a multi-tool pipeline rather than pattern matching alone. That is meaningfully harder to build, and it is why “we scan our registry” should prompt a follow-up question about what kind of scanning.

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Gap 4: No Revocation or Update Discipline

The fourth gap concerns what happens after installation.

Skills update. Most users never re-review an updated skill, because approval happened once at install. OWASP categorizes this as update drift, with the ClawJacked case and patch-lag exploitation as evidence.

Three controls close it, and almost nobody applies all three.

Immutable pinning. Pin to a content hash, not a version tag or a branch.

Hash verification at load. Confirm the skill you are loading is the one you reviewed.

Change alerting. Treat a modified skill description as equivalent to a dependency update requiring review, not as a silent refresh.

Absent revocation infrastructure, there is also no mechanism to withdraw a skill once a publisher is found compromised. A compromised publisher on npm can be revoked. On most skill registries, there is nothing to revoke.

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Why Signing Alone Will Not Fix Agent Skills Security

This is the nuance most commentary skips, and OWASP states it directly in its own whitepaper.

A signature proves authorship, not safety. A verified publisher can still ship malicious content. Signing composes with behavioral scanning and reputation; it does not replace them.

The ClawHavoc campaign makes the point concretely. Twelve publisher accounts operated the campaign. Under a signing regime, all twelve could have signed their skills perfectly validly. Signatures would have proved that the malware came from exactly the accounts it came from.

What signing actually buys is attribution and revocability. Once you know which publisher shipped what, you can revoke a compromised key, trace the blast radius, and stop the next upload from that identity.

That is genuinely valuable — and it is a containment control, not a prevention control. The layered position: signing for attribution, behavioral scanning for detection, least-privilege manifests for blast radius, and human approval on high-privilege installs where automation has not caught up.

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Comparing Registries on Agent Skills Security

Registries differ enormously, and the differences are not advertised prominently.

Registry	Approximate catalogue	Security posture
Anthropic official directory	Small	Manually curated, verified
Agensi	Smaller, curated	Reviewed before listing, multi-point scan
SkillHub	7,000+	Automated AI evaluation
Skills.sh	Hundreds of thousands	Builder-side auditing
ClawHub	70,000+	Automated scanning added post-incident
SkillsMP	~1.9 million	None — scraped from public GitHub

Catalogue size and security posture run in opposite directions, which is the trade-off worth understanding before choosing a source.

There is also a quality argument for curation independent of security. Analysis indicates curated skills raise agent task pass rates by around 16 percentage points on average. Curation is not only a safety tax.

For anyone mapping where skills sit in a broader threat model, the five hidden layers of the AI attack surface covers the surrounding surface.

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An Agent Skills Security Checklist

Ordered by what reduces exposure fastest.

Inventory installed skills. You cannot govern what you cannot enumerate. Include skills individual developers installed on their own machines.

Read the description, not the label. The description enters model context for every installed skill at every session start. Read the raw frontmatter.

Pin to content hashes. Not tags, not branches. Then alert on change.

Assume host-level permissions. A skill runs with whatever the agent has. If your agent holds production credentials, so does every skill installed in it.

Prefer curated sources for anything privileged. Use open registries for experimentation, curated ones for anything touching real systems.

Sandbox by default. Skills that execute code should run in a container, not on the host.

Check for the lethal trifecta. OWASP flags the dangerous combination directly: access to private data, exposure to untrusted content, and an ability to communicate externally. Break any one of the three and most exfiltration paths close.

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Primary sources

• OWASP Agentic Skills Top 10
• Agent Skills open standard
• HiddenLayer — Malicious Skills in Agentic AI
• Cloud Security Alliance — Claude Agent Skills risks for CISOs

Audit percentages vary substantially by methodology and population; ranges are shown rather than single figures. Publication dates for AST10 differ across sources and are noted inline. Corrections with a primary source are welcome.

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Frequently Asked Questions

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Vendor datasheets lead with FLOPS. For most language model serving, FLOPS is the wrong number.

Here is the physical reality of generating one token. The accelerator must read the entire weight set out of high-bandwidth memory, perform a comparatively tiny matrix-vector multiplication, and repeat for the next token.

The multiplication is trivial. The reading is not. And the reading happens again for every single token.

That pattern produces an arithmetic intensity of roughly one floating-point operation per byte moved. An H100 needs around 300 FLOP per byte before its tensor cores become the limiting factor.

The cores stall. Not because of a bug, a driver issue, or bad kernels — because of the ratio between two numbers on the datasheet.

This is the memory wall, and understanding it changes which chips look attractive, which benchmarks mean anything, and why bandwidth-per-dollar often beats FLOPS-per-dollar as a purchasing metric.

Key Takeaways

• During token generation, an H100 uses roughly 0.34% of its peak compute. The tensor cores sit idle waiting for weights to arrive from memory.
• The B200 is worse on this measure, not better — about 0.18% — because its compute grew faster than its memory bandwidth. More FLOPS widened the gap.
• Every accelerator has a balance point: peak FLOPS divided by memory bandwidth. The H100’s is 295 FLOP per byte. Auto-regressive decode delivers roughly 1.
• The H100-to-H200 comparison is the cleanest evidence available. Same compute die, bandwidth raised from 3.35 to 4.8 TB/s, and materially more tokens per second with zero added FLOPS.
• You can calculate a hard token ceiling from memory bandwidth alone, before running a benchmark. A 70B model at FP16 cannot exceed about 34 tokens per second on one H200.

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Quick Navigation

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The Roofline Model and Memory Bandwidth Limits

The framework comes from Williams, Waterman and Patterson in 2009, and it remains the right lens.

Every chip has two ceilings: peak compute, measured in FLOPS, and peak memory bandwidth, measured in bytes per second. Every workload has an arithmetic intensity — operations performed per byte moved.

Divide peak FLOPS by bandwidth and you get the balance point: the arithmetic intensity at which a workload transitions from memory-bound to compute-bound.

Below the balance point, you are memory-bound. Adding compute does nothing. Above it, you are compute-bound, and bandwidth is not your constraint.

The whole argument of this article reduces to one comparison: decode sits at roughly 1 FLOP per byte, and every current accelerator’s balance point sits in the hundreds.

There is no configuration in which that gap closes by tuning. It closes only by changing the workload’s arithmetic intensity or the chip’s bandwidth.

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Prefill and Decode Have Different Memory Bandwidth Needs

The single most common analytical error is treating inference as one workload. It is two, with opposite characteristics.

Prefill processes the input prompt. All tokens are available simultaneously, so the operation is a large matrix-matrix multiplication with substantial weight reuse. Arithmetic intensity is high. Prefill is compute-bound.

Decode generates output one token at a time. Each step depends on the previous one, so there is no parallelism to exploit across tokens. Arithmetic intensity collapses. Decode is memory-bound.

Research characterizing Llama-70B inference in FP16 shows prefill intensity rising with batch size and input length, then declining beyond roughly 10,000 tokens as memory-bound attention operations start to dominate. Decode intensity is far lower throughout and falls further as the KV cache grows.

The practical consequence is that fleet-sizing built on peak TFLOPS is systematically wrong. Your prompt processing may well be compute-bound. Your token generation — the part users wait for — almost never is.

This distinction sits alongside the training-versus-inference split covered in inference chips versus training chips.

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Memory Bandwidth Balance Points Across Current Chips

Here is the arithmetic, computed from published specifications. Balance point is peak dense FP16 FLOPS divided by memory bandwidth.

Accelerator	Peak FP16	Bandwidth	Capacity	Balance point	Decode uses
H100 SXM	989 TFLOPS	3.35 TB/s	80 GB	295 FLOP/byte	0.34%
H200	989 TFLOPS	4.8 TB/s	141 GB	206 FLOP/byte	0.49%
MI300X	1,300 TFLOPS	5.3 TB/s	192 GB	245 FLOP/byte	0.41%
MI355X	2,300 TFLOPS	8.0 TB/s	288 GB	288 FLOP/byte	0.35%
B200	4,500 TFLOPS	8.0 TB/s	180 GB	563 FLOP/byte	0.18%

The final column is the fraction of peak compute a decode workload can actually use. Read it twice.

The B200 is the worst chip on this list by that measure. It has 4.5× the FP16 compute of an H100 and 2.4× the bandwidth. Compute grew faster than bandwidth, so the balance point rose from 295 to 563, and the share of silicon a memory-bound workload can exercise fell.

This is not an argument against buying B200s. In absolute terms a B200 generates far more tokens per second than an H100, because absolute bandwidth is what matters for throughput. It is an argument against reading the FLOPS number as a proxy for inference performance. Those two things diverged.

One specification note: B200 memory is quoted as both 180 GB and 192 GB across sources. The SXM module ships 180 GB enabled. Both figures circulate; the 8 TB/s bandwidth is consistent.

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The H200 Natural Experiment

Theory is arguable. This comparison is not.

The H200 uses the same compute die as the H100. Identical FLOPS. What changed was memory: 80 GB of HBM3 at 3.35 TB/s became 141 GB of HBM3e at 4.8 TB/s.

If FLOPS determined inference performance, the two would perform identically. They do not. MLPerf results using Llama 2 70B showed the H200 exceeding 31,000 tokens per second, roughly 45% faster than the H100.

A 43% bandwidth increase produced roughly a 45% throughput increase, with zero additional compute.

That is close to linear scaling with bandwidth, and it is the clearest available demonstration that memory bandwidth — not compute — governs decode throughput.

One caveat worth carrying into procurement: a 43% throughput gain only lowers your cost per token if the hourly price premium is below 43%. Bandwidth improvements are real, and they are still something you pay for.

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Calculating Your Token Ceiling From Memory Bandwidth

You can compute an upper bound before running anything.

Tokens per second ≤ memory bandwidth ÷ bytes of weights read per token

At batch size 1, the weights read per token equal the model size in memory. Here is a 70B model across precisions and chips.

Chip	FP16 (140 GB)	FP8 (70 GB)	FP4 (35 GB)
H100	— (does not fit)	48 tok/s	96 tok/s
H200	34 tok/s	69 tok/s	137 tok/s
MI300X	38 tok/s	76 tok/s	151 tok/s
B200	57 tok/s	114 tok/s	229 tok/s
MI355X	57 tok/s	114 tok/s	229 tok/s

These are ceilings, not forecasts. Real throughput lands below them because of kernel launch overhead, imperfect memory access patterns, and attention operations on top of weight streaming.

But the ceiling is genuinely hard. No amount of optimization produces more tokens per second than bandwidth divided by bytes moved.

Two things fall out immediately. Quantisation roughly doubles the ceiling per halving of precision, because it halves the bytes moved. And batching raises aggregate throughput without raising per-request speed, because the same weights serve multiple requests per pass — which is why throughput and latency behave so differently under load, and why cost per token depends so heavily on utilisation.

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Why the KV Cache Makes Memory Bandwidth Worse

Weight streaming is the headline problem. The KV cache is the one that degrades over a conversation.

Each generated token must retrieve key and value vectors for every preceding token. That traffic grows linearly with sequence length, and the access pattern is irregular in address space.

Irregularity matters more than volume here. HBM delivers rated bandwidth on sequential, row-buffer-friendly access. Scattered reads produce poor row-buffer locality, and effective bandwidth falls well below the rated peak.

So two things happen as context grows. Total bytes moved per token increase, and the efficiency with which they move decreases.

This is why long-context serving degrades faster than a linear model predicts, and why published peak bandwidth is an optimistic upper bound rather than a working number.

FP8 KV cache quantisation is the cheapest available intervention — it halves KV traffic against FP16, and it is usually simpler to deploy than changing hardware.

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Four Ways to Buy Back Memory Bandwidth

Ordered by effort, not by effect.

Quantise the weights. Moving from FP16 to FP8 halves bytes per token and roughly doubles the ceiling. FP4 halves it again where accuracy holds.

Quantise the KV cache. Separate from weight quantisation and often overlooked. Halves KV traffic.

Batch. Weights are read once and reused across every request in the batch, so arithmetic intensity rises with batch size. This moves the workload up the roofline toward the compute-bound region — the only lever that changes which ceiling binds.

Check your kernels. This is the least obvious and frequently decisive. A 2026 cross-GPU study found that quantisation only delivers bandwidth savings if the kernel actually streams compressed weights through memory. Two int4 implementations on the same hardware differed by more than 2× in step time, with the difference attributable to kernel implementation rather than bit width.

That last finding deserves emphasis. You can quantize a model, halve its nominal footprint, and see no throughput gain, because the runtime dequantises before the bytes cross the memory bus.

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Where the Memory Bandwidth Model Breaks Down

A model that only confirms itself is not worth trusting. Here is where this one fails.

Rated bandwidth is not achieved bandwidth. Every figure in the tables above is a peak specification. Real workloads see less, sometimes substantially, because of irregular access patterns and row-buffer misses. Treat computed ceilings as upper bounds that real systems approach but do not reach.

Latency and launch overhead are not modelled. A 2026 cross-GPU study found that for small models at batch size 1, kernel launch overhead — not bandwidth — dominated step time. One 7B model reached 11.78 ms per step under default attention with CUDA graphs enabled, a regime where the roofline is not the binding constraint at all. Below roughly 7B parameters at batch 1, check launch overhead before blaming memory.

Interconnect becomes the next wall. Once a model spans multiple accelerators, tensor and expert parallelism push traffic across NVLink or equivalent fabric. Research notes this as the subsequent bottleneck after memory bandwidth and capacity, and large mixture-of-experts deployments are already approaching it.

Mixture-of-experts changes the arithmetic entirely. An MoE model activates a fraction of its parameters per token, so bytes moved per token bear little relation to total parameter count. Substituting total parameters into the ceiling formula will give an answer that is wrong by an order of magnitude.

Cost inversions are real. The same study found that an H100 was roughly 1.47× faster than an L4 on one quantised workload, while costing more than ten times as much per hour. Faster and cheaper-per-token are different questions, and the memory bandwidth ceiling only answers the first.

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What HBM4 Changes for Memory Bandwidth

HBM4 enters mass production in 2026 and doubles the interface width to 2048 bits while holding transfer rates above 8.0 Gbps, reaching roughly 2 TB/s per stack.

Doubling width rather than clock speed is the important design choice — it raises throughput without a proportional power penalty.

Two things to keep in perspective.

The balance point may not improve. If next-generation compute scales faster than next-generation bandwidth, the gap widens again, exactly as it did from H100 to B200. Bandwidth doubling is only relief if compute does not more than double alongside it.

Supply is the binding constraint. HBM demand grew more than 130% year over year in 2025 and is projected above 70% in 2026. Memory availability now determines which organisations can deploy the largest models, which is a supply-chain fact rather than an engineering one.

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Reading Memory Bandwidth Claims Critically

A short checklist for vendor material.

Find the precision. A FLOPS figure without a precision is meaningless. FP4 numbers are typically 4× the FP16 figure for the same silicon.

Check dense versus sparse. Sparse figures typically double dense ones and require structured sparsity your model may not have.

Divide FLOPS by bandwidth yourself. That single division tells you more about inference behavior than any headline throughput claim.

Ask which phase was measured. Prefill-heavy benchmarks flatter compute. Decode-heavy benchmarks reveal bandwidth.

Treat vendor comparisons as workload-specific. One current marketing claim compares a liquid-cooled part at FP4 against a prior-generation part at FP8 under different batch conditions. That is not a like-for-like measurement, and the same pattern recurs across vendors.

Watch for capacity-bandwidth conflation. More memory lets you fit a larger model. It does not make token generation faster. The MI355X’s 288 GB is a capacity advantage; its 8 TB/s is the throughput number.

Terminology in this area is inconsistent across vendors, and our AI glossary defines the specific terms used here.

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Primary sources

• Williams, Waterman & Patterson, Roofline model (ACM)
• NVIDIA data centre GPU specifications
• AMD Instinct MI350 series
• TrendForce memory wall analysis

Balance points and token ceilings above are computed from published vendor specifications using the formulas shown, so readers can substitute their own figures. Vendor capacity figures occasionally conflict; discrepancies are noted inline.

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Frequently Asked Questions

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Keep reading

Here are the latest posts from the blog.

Vendor datasheets lead with FLOPS. For most language model serving, FLOPS is the wrong number.

Here is the physical reality of generating one token. The accelerator must read the entire weight set out of high-bandwidth memory, perform a comparatively tiny matrix-vector multiplication, and repeat for the next token.

The multiplication is trivial. The reading is not. And the reading happens again for every single token.

That pattern produces an arithmetic intensity of roughly one floating-point operation per byte moved. An H100 needs around 300 FLOP per byte before its tensor cores become the limiting factor.

The cores stall. Not because of a bug, a driver issue, or bad kernels — because of the ratio between two numbers on the datasheet.

This is the memory wall, and understanding it changes which chips look attractive, which benchmarks mean anything, and why bandwidth-per-dollar often beats FLOPS-per-dollar as a purchasing metric.

Key Takeaways

• During token generation, an H100 uses roughly 0.34% of its peak compute. The tensor cores sit idle waiting for weights to arrive from memory.
• The B200 is worse on this measure, not better — about 0.18% — because its compute grew faster than its memory bandwidth. More FLOPS widened the gap.
• Every accelerator has a balance point: peak FLOPS divided by memory bandwidth. The H100’s is 295 FLOP per byte. Auto-regressive decode delivers roughly 1.
• The H100-to-H200 comparison is the cleanest evidence available. Same compute die, bandwidth raised from 3.35 to 4.8 TB/s, and materially more tokens per second with zero added FLOPS.
• You can calculate a hard token ceiling from memory bandwidth alone, before running a benchmark. A 70B model at FP16 cannot exceed about 34 tokens per second on one H200.

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Quick Navigation

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The Roofline Model and Memory Bandwidth Limits

The framework comes from Williams, Waterman and Patterson in 2009, and it remains the right lens.

Every chip has two ceilings: peak compute, measured in FLOPS, and peak memory bandwidth, measured in bytes per second. Every workload has an arithmetic intensity — operations performed per byte moved.

Divide peak FLOPS by bandwidth and you get the balance point: the arithmetic intensity at which a workload transitions from memory-bound to compute-bound.

Below the balance point, you are memory-bound. Adding compute does nothing. Above it, you are compute-bound, and bandwidth is not your constraint.

The whole argument of this article reduces to one comparison: decode sits at roughly 1 FLOP per byte, and every current accelerator’s balance point sits in the hundreds.

There is no configuration in which that gap closes by tuning. It closes only by changing the workload’s arithmetic intensity or the chip’s bandwidth.

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Prefill and Decode Have Different Memory Bandwidth Needs

The single most common analytical error is treating inference as one workload. It is two, with opposite characteristics.

Prefill processes the input prompt. All tokens are available simultaneously, so the operation is a large matrix-matrix multiplication with substantial weight reuse. Arithmetic intensity is high. Prefill is compute-bound.

Decode generates output one token at a time. Each step depends on the previous one, so there is no parallelism to exploit across tokens. Arithmetic intensity collapses. Decode is memory-bound.

Research characterizing Llama-70B inference in FP16 shows prefill intensity rising with batch size and input length, then declining beyond roughly 10,000 tokens as memory-bound attention operations start to dominate. Decode intensity is far lower throughout and falls further as the KV cache grows.

The practical consequence is that fleet-sizing built on peak TFLOPS is systematically wrong. Your prompt processing may well be compute-bound. Your token generation — the part users wait for — almost never is.

This distinction sits alongside the training-versus-inference split covered in inference chips versus training chips.

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Memory Bandwidth Balance Points Across Current Chips

Here is the arithmetic, computed from published specifications. Balance point is peak dense FP16 FLOPS divided by memory bandwidth.

Accelerator	Peak FP16	Bandwidth	Capacity	Balance point	Decode uses
H100 SXM	989 TFLOPS	3.35 TB/s	80 GB	295 FLOP/byte	0.34%
H200	989 TFLOPS	4.8 TB/s	141 GB	206 FLOP/byte	0.49%
MI300X	1,300 TFLOPS	5.3 TB/s	192 GB	245 FLOP/byte	0.41%
MI355X	2,300 TFLOPS	8.0 TB/s	288 GB	288 FLOP/byte	0.35%
B200	4,500 TFLOPS	8.0 TB/s	180 GB	563 FLOP/byte	0.18%

The final column is the fraction of peak compute a decode workload can actually use. Read it twice.

The B200 is the worst chip on this list by that measure. It has 4.5× the FP16 compute of an H100 and 2.4× the bandwidth. Compute grew faster than bandwidth, so the balance point rose from 295 to 563, and the share of silicon a memory-bound workload can exercise fell.

This is not an argument against buying B200s. In absolute terms a B200 generates far more tokens per second than an H100, because absolute bandwidth is what matters for throughput. It is an argument against reading the FLOPS number as a proxy for inference performance. Those two things diverged.

One specification note: B200 memory is quoted as both 180 GB and 192 GB across sources. The SXM module ships 180 GB enabled. Both figures circulate; the 8 TB/s bandwidth is consistent.

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The H200 Natural Experiment

Theory is arguable. This comparison is not.

The H200 uses the same compute die as the H100. Identical FLOPS. What changed was memory: 80 GB of HBM3 at 3.35 TB/s became 141 GB of HBM3e at 4.8 TB/s.

If FLOPS determined inference performance, the two would perform identically. They do not. MLPerf results using Llama 2 70B showed the H200 exceeding 31,000 tokens per second, roughly 45% faster than the H100.

A 43% bandwidth increase produced roughly a 45% throughput increase, with zero additional compute.

That is close to linear scaling with bandwidth, and it is the clearest available demonstration that memory bandwidth — not compute — governs decode throughput.

One caveat worth carrying into procurement: a 43% throughput gain only lowers your cost per token if the hourly price premium is below 43%. Bandwidth improvements are real, and they are still something you pay for.

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Calculating Your Token Ceiling From Memory Bandwidth

You can compute an upper bound before running anything.

Tokens per second ≤ memory bandwidth ÷ bytes of weights read per token

At batch size 1, the weights read per token equal the model size in memory. Here is a 70B model across precisions and chips.

Chip	FP16 (140 GB)	FP8 (70 GB)	FP4 (35 GB)
H100	— (does not fit)	48 tok/s	96 tok/s
H200	34 tok/s	69 tok/s	137 tok/s
MI300X	38 tok/s	76 tok/s	151 tok/s
B200	57 tok/s	114 tok/s	229 tok/s
MI355X	57 tok/s	114 tok/s	229 tok/s

These are ceilings, not forecasts. Real throughput lands below them because of kernel launch overhead, imperfect memory access patterns, and attention operations on top of weight streaming.

But the ceiling is genuinely hard. No amount of optimization produces more tokens per second than bandwidth divided by bytes moved.

Two things fall out immediately. Quantisation roughly doubles the ceiling per halving of precision, because it halves the bytes moved. And batching raises aggregate throughput without raising per-request speed, because the same weights serve multiple requests per pass — which is why throughput and latency behave so differently under load, and why cost per token depends so heavily on utilisation.

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Why the KV Cache Makes Memory Bandwidth Worse

Weight streaming is the headline problem. The KV cache is the one that degrades over a conversation.

Each generated token must retrieve key and value vectors for every preceding token. That traffic grows linearly with sequence length, and the access pattern is irregular in address space.

Irregularity matters more than volume here. HBM delivers rated bandwidth on sequential, row-buffer-friendly access. Scattered reads produce poor row-buffer locality, and effective bandwidth falls well below the rated peak.

So two things happen as context grows. Total bytes moved per token increase, and the efficiency with which they move decreases.

This is why long-context serving degrades faster than a linear model predicts, and why published peak bandwidth is an optimistic upper bound rather than a working number.

FP8 KV cache quantisation is the cheapest available intervention — it halves KV traffic against FP16, and it is usually simpler to deploy than changing hardware.

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Four Ways to Buy Back Memory Bandwidth

Ordered by effort, not by effect.

Quantise the weights. Moving from FP16 to FP8 halves bytes per token and roughly doubles the ceiling. FP4 halves it again where accuracy holds.

Quantise the KV cache. Separate from weight quantisation and often overlooked. Halves KV traffic.

Batch. Weights are read once and reused across every request in the batch, so arithmetic intensity rises with batch size. This moves the workload up the roofline toward the compute-bound region — the only lever that changes which ceiling binds.

Check your kernels. This is the least obvious and frequently decisive. A 2026 cross-GPU study found that quantisation only delivers bandwidth savings if the kernel actually streams compressed weights through memory. Two int4 implementations on the same hardware differed by more than 2× in step time, with the difference attributable to kernel implementation rather than bit width.

That last finding deserves emphasis. You can quantize a model, halve its nominal footprint, and see no throughput gain, because the runtime dequantises before the bytes cross the memory bus.

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Where the Memory Bandwidth Model Breaks Down

A model that only confirms itself is not worth trusting. Here is where this one fails.

Rated bandwidth is not achieved bandwidth. Every figure in the tables above is a peak specification. Real workloads see less, sometimes substantially, because of irregular access patterns and row-buffer misses. Treat computed ceilings as upper bounds that real systems approach but do not reach.

Latency and launch overhead are not modelled. A 2026 cross-GPU study found that for small models at batch size 1, kernel launch overhead — not bandwidth — dominated step time. One 7B model reached 11.78 ms per step under default attention with CUDA graphs enabled, a regime where the roofline is not the binding constraint at all. Below roughly 7B parameters at batch 1, check launch overhead before blaming memory.

Interconnect becomes the next wall. Once a model spans multiple accelerators, tensor and expert parallelism push traffic across NVLink or equivalent fabric. Research notes this as the subsequent bottleneck after memory bandwidth and capacity, and large mixture-of-experts deployments are already approaching it.

Mixture-of-experts changes the arithmetic entirely. An MoE model activates a fraction of its parameters per token, so bytes moved per token bear little relation to total parameter count. Substituting total parameters into the ceiling formula will give an answer that is wrong by an order of magnitude.

Cost inversions are real. The same study found that an H100 was roughly 1.47× faster than an L4 on one quantised workload, while costing more than ten times as much per hour. Faster and cheaper-per-token are different questions, and the memory bandwidth ceiling only answers the first.

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What HBM4 Changes for Memory Bandwidth

HBM4 enters mass production in 2026 and doubles the interface width to 2048 bits while holding transfer rates above 8.0 Gbps, reaching roughly 2 TB/s per stack.

Doubling width rather than clock speed is the important design choice — it raises throughput without a proportional power penalty.

Two things to keep in perspective.

The balance point may not improve. If next-generation compute scales faster than next-generation bandwidth, the gap widens again, exactly as it did from H100 to B200. Bandwidth doubling is only relief if compute does not more than double alongside it.

Supply is the binding constraint. HBM demand grew more than 130% year over year in 2025 and is projected above 70% in 2026. Memory availability now determines which organisations can deploy the largest models, which is a supply-chain fact rather than an engineering one.

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Reading Memory Bandwidth Claims Critically

A short checklist for vendor material.

Find the precision. A FLOPS figure without a precision is meaningless. FP4 numbers are typically 4× the FP16 figure for the same silicon.

Check dense versus sparse. Sparse figures typically double dense ones and require structured sparsity your model may not have.

Divide FLOPS by bandwidth yourself. That single division tells you more about inference behavior than any headline throughput claim.

Ask which phase was measured. Prefill-heavy benchmarks flatter compute. Decode-heavy benchmarks reveal bandwidth.

Treat vendor comparisons as workload-specific. One current marketing claim compares a liquid-cooled part at FP4 against a prior-generation part at FP8 under different batch conditions. That is not a like-for-like measurement, and the same pattern recurs across vendors.

Watch for capacity-bandwidth conflation. More memory lets you fit a larger model. It does not make token generation faster. The MI355X’s 288 GB is a capacity advantage; its 8 TB/s is the throughput number.

Terminology in this area is inconsistent across vendors, and our AI glossary defines the specific terms used here.

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Primary sources

• Williams, Waterman & Patterson, Roofline model (ACM)
• NVIDIA data centre GPU specifications
• AMD Instinct MI350 series
• TrendForce memory wall analysis

Balance points and token ceilings above are computed from published vendor specifications using the formulas shown, so readers can substitute their own figures. Vendor capacity figures occasionally conflict; discrepancies are noted inline.

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Frequently Asked Questions

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Keep reading

Here are the latest posts from the blog.

This article is general information, not legal advice. Consult counsel for your specific obligations.

Almost every US state AI statute divides the world into two roles.

A developer builds, sells, licenses, or substantially modifies the system. A deployer uses it to make or materially influence a decision about a person.

The split exists because the two parties know different things. The developer knows how the system was trained, what it was designed for, and where it fails. The deployer knows who it is being used on, for what decision, and with what consequences.

Neither can discharge the other’s duties, which is why the statutes assign different obligations rather than one shared standard.

Here is the part most companies get wrong: these laws are not aimed primarily at OpenAI, Anthropic or Google. Deployer obligations attach to the company that plugged a model into a hiring funnel, a loan decision, or a claims triage queue — regardless of whether it wrote a line of the model.

If your organisation screens resumes, scores leads, prices a policy, or triages support tickets with AI, you are probably a deployer somewhere.

Key Takeaways

• Colorado’s AI Act — the law nearly every “developer versus deployer” guide describes — was repealed before it ever took effect. SB 26-189 replaced it in May 2026, effective 1 January 2027.
• Most published guidance still describes the repealed statute, including its duty of care, impact assessments, and NIST safe harbor. None of those survived.
• These laws do not primarily target frontier labs. Deployer duties fall on the ordinary company that connected a model to a hiring, lending, or claims workflow.
• California’s AI Transparency Act became operative on 2 August 2026, adding watermarking and detection duties with penalties of $5,000 per violation per day.
• Colorado’s replacement voids any contract clause shifting liability for your own violation onto another party — which makes vendor indemnities a live procurement issue.

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Quick Navigation

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The State AI Laws Reset of 2026

This is where accuracy matters most, because the reference point moved.

Colorado’s SB 24-205, signed in May 2024, was the first comprehensive US AI statute and became the model everyone cited. It imposed a duty of reasonable care on both developers and deployers, required annual impact assessments and risk management programmes, and mandated attorney-general notification of algorithmic discrimination.

It never took effect.

The timeline: enforcement was delayed from 1 February to 30 June 2026 by SB 25B-004. On 27 April 2026, a federal court enjoined enforcement in xAI v. Weiser. Then in May 2026, Governor Polis signed SB 26-189, which repealed and reenacted the entire framework as an automated decision-making technology statute effective 1 January 2027.

What did not survive the rewrite is as important as what did.

Gone: the duty of reasonable care, annual impact assessments, risk management programme mandates, and — notably — the framework-based affirmative defense that let companies rely on recognized standards.

Retained and reshaped: developer documentation duties, deployer notice duties, consumer recourse, and three-year recordkeeping.

The scope also narrowed, from “high-risk AI systems” to “covered automated decision-making technology” that materially influences consequential decisions. ADMT is a term borrowed from privacy law, and it may capture tools the old AI-system definition missed.

Any guide describing Colorado’s duty of care or impact assessments is describing a repealed law.

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Which State AI Laws Are in Force Today

As of August 2026, here is what actually binds.

Law	Status	Primary target
California SB 53 (TFAIA)	In force since 1 Jan 2026	Frontier model developers
California AB 2013	In force since 1 Jan 2026	Generative AI developers
California SB 942 / AB 853	Operative 2 Aug 2026	Large generative AI providers
Texas TRAIGA (HB 149)	In force since 1 Jan 2026	Developers and deployers
Illinois HB 3773	In force since 1 Jan 2026	Employers
NYC Local Law 144	In force since 2023	Employers using AEDTs
Colorado SB 26-189	Effective 1 Jan 2027	Developers and deployers

Three points worth noting.

Texas is currently the broadest comprehensive law in force. TRAIGA is narrower than Colorado’s original design — the high-risk impact assessment regime was cut from the final bill. It prohibits developing or deploying AI intended to manipulate, unlawfully discriminate, incite self-harm or criminal activity, produce CSAM or non-consensual intimate imagery, or conduct government social scoring. Crucially, it grants an enforcement safe harbor to organisations substantially complying with the NIST AI RMF.

California’s approach is several narrow laws rather than one broad one. SB 53 targets frontier developers with safety framework publication and incident reporting. AB 2013 requires training-data disclosure far more broadly. SB 942, operative since 2 August, requires covered providers with over one million monthly users to offer detection tools and latent disclosures.

Reach follows the consumer, not your address. These laws generally apply if your system affects that state’s residents. A company in Bengaluru with California applicants has California obligations.

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What Builders Owe Under State AI Laws

Developer duties cluster into four categories. This is the first of the two role-specific checklists.

Duty 1 — Documentation to deployers. Under Colorado’s SB 26-189, from 1 January 2027 developers must give each deployer, in a form reasonably understandable and protective of trade secrets: a statement of intended uses and known harmful or inappropriate uses; a description of the categories of data used in training, to the extent known; known limitations and risks; and instructions for appropriate use, monitoring and meaningful human review.

Duty 2 — Update notification. Developers must notify deployers of material updates, intentional modifications, and changes to intended use or risk mitigation within a reasonable time. This turns model updates into a communications obligation, not just an engineering event.

Duty 3 — Public transparency. California layers this on separately. AB 2013 requires public documentation of training data. SB 53 requires frontier developers to publish safety frameworks and report critical incidents. SB 942 requires detection tooling and content provenance.

Duty 4 — Recordkeeping. At least three years, including version identifiers, changelogs and material-update documentation.

One scoping limit worth knowing: Colorado’s developer obligations apply where the technology was marketed, configured, contracted or licensed for consequential decisions, or where the developer becomes aware of such use consistent with intended purposes. A general-purpose tool used off-label by a customer is treated differently from one sold for that purpose.

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What Deployers Owe Under State AI Laws

Deployer duties are fewer but more consumer-facing, and they carry the operational burden.

Duty 1 — Pre-use notice. Before a covered system is used in a consequential decision, the deployer must tell the consumer. Point-of-interaction notice, plain language.

Duty 2 — Post-adverse-outcome explanation within 30 days. If the system materially influenced a decision that went against someone, the deployer must provide a plain-language description of the system’s role and the principal factors it used. This is the single heaviest operational lift in the new Colorado framework, because it requires per-decision explainability your vendor may not supply.

Duty 3 — A path to meaningful human review. Not a form that disappears. An actual route to a human who can revisit the outcome — which is why human-in-the-loop design has moved from good practice to statutory requirement.

Duty 4 — Recordkeeping. Three years of usage records demonstrating compliance.

Employment deployers carry extra weight regardless of Colorado. Illinois HB 3773 amends the Human Rights Act to prohibit employer use of AI that discriminates against protected classes. NYC Local Law 144 requires bias audits for automated employment decision tools. Both are in force now.

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The Contract Layer Most Teams Miss

This provision deserves its own section because it changes procurement, not just compliance.

SB 26-189 voids any contractual clause that attempts to shift liability for a party’s own discriminatory use of ADMT onto another party. An indemnity purporting to shield you from your own violation is void as against public policy.

Two practical consequences.

Your vendor indemnity may not do what you think. If your AI procurement contract leans on the vendor absorbing discrimination liability, that allocation may be unenforceable in Colorado. Review existing agreements now rather than in December.

Your customers will ask you for documentation you do not produce. Deployers cannot meet their obligations without developer documentation covering intended uses, training-data categories and limitations. Most vendors do not currently offer this as a standard contractual commitment. Builders who prepare it early turn a compliance cost into a sales advantage.

Liability now tracks intended use. Developers answer for harms from systems used as intended; deployers answer for their own deployment decisions, including uses the developer never authorized.

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When You Are Both Builder and Deployer

Most companies of any size are both, and the roles do not net out.

If you fine-tune a foundation model and use it internally for hiring, you are a developer of the modified system and a deployer of it. You owe documentation duties to yourself in substance — meaning you must actually produce the artefacts, because your deployer-side notices depend on them.

Substantial modification is the trigger to watch. Prompt engineering probably does not make you a developer. Fine-tuning on your own data probably does. The line has not been tested, and the statutes leave room for argument.

The practical approach: assess role per system, not per company. A single organisation may be a pure deployer for its CRM’s lead scoring, a developer for its fine-tuned resume screener, and out of scope entirely for its internal code assistant. The vocabulary underlying these distinctions is set out in our AI glossary.

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Where State AI Laws Do Not Reach Agents

A gap worth naming, because it will shape the next legislative cycle.

Every statute described here regulates decisions about people. Consequential decisions in employment, housing, lending, insurance, healthcare, education, government services. That framing comes from anti-discrimination law, and it works well for a resume screener.

It fits an autonomous agent poorly.

An agent that queries a database, calls three APIs, and modifies a record is not making a consequential decision about a consumer in the statutory sense. It may still cause substantial harm. Nothing in the developer or deployer duties above addresses tool permissions, action scoping, or what happens when an agent takes an irreversible step.

Three practical consequences follow.

Your agent deployments may be out of scope and still risky. Compliance is not a proxy for safety here. The distinction between systems that generate output and systems that take actions is drawn in agentic AI versus generative AI, and current statutes are written almost entirely for the first category.

Scope can attach unexpectedly. If an agent’s output feeds a consequential decision — even indirectly, as one input among several — the deployer duties may apply to the workflow it sits inside. “Materially influences” is a broad phrase and has not been narrowed by any court.

The documentation you build now transfers. System inventories, role assessments and three-year records are the same artefacts any future agent-focused statute will demand. Building them for today’s laws is not wasted effort if the scope expands.

Texas offers a partial preview. TRAIGA’s intent-based prohibitions apply to developing or deploying AI intended to manipulate or unlawfully discriminate, without requiring a consequential-decision context. That structure reaches conduct the Colorado model does not.

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State AI Laws and the Federal Preemption Fight

There is no comprehensive federal AI statute as of August 2026, and none appears imminent.

What exists is executive action. Executive Order 14365, signed 11 December 2025, directed the Attorney General to establish an AI Litigation Task Force to challenge state AI laws on interstate commerce and preemption theories, and directed a Commerce review that could condition federal broadband funding on a state’s AI posture. It named Colorado’s AI Act specifically.

No federal preemption has been enacted. The push is live litigation and legislative pressure, not settled law.

The practical posture recommended by most counsel is straightforward: comply with what is in force, track the litigation, and do not treat the preemption push as a reason to pause. State laws remain enforceable unless and until a court or statute says otherwise.

Colorado adds a second layer of uncertainty. Enforcement is subject to the federal stay from xAI v. Weiser, and Attorney General Weiser has indicated the state will not enforce until required rule-making is complete — rule-making that had not formally begun as of mid-2026. The 1 January 2027 date is real but the enforcement posture behind it is not yet fixed.

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Building One Program for Many State AI Laws

More than 2,000 AI-related bills have been introduced across the states. Chasing each one individually does not scale.

Inventory first. Every system that processes personal data and produces an output used to make, guide or assist a decision in education, employment, housing, financial services, insurance, healthcare or government services. Include the tools a team bought on a corporate card.

Assess role per system. Developer, deployer, both, or out of scope.

Govern to a stable framework. The NIST AI RMF earns an explicit enforcement safe harbor in Texas. It does not in Colorado’s successor — that defense did not survive the rewrite — but it remains the most widely referenced baseline and satisfies overlapping duties across jurisdictions.

Build the notice machinery. Pre-use disclosure and a 30-day adverse-outcome explanation workflow. Start here if resources are tight; it is the longest lead-time item.

Renegotiate vendor terms. Require developer documentation as a contractual deliverable. Check indemnities against the anti-shifting provision.

Keep three years of records. Version identifiers, changelogs, usage logs, notice delivery evidence.

Re-check quarterly. Colorado’s law was delayed, enjoined, repealed and replaced within eighteen months. Treat any compliance map as a snapshot.

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Primary sources

• Colorado SB26-189, Colorado General Assembly
• Texas HB 149 (TRAIGA)
• California SB 53 (TFAIA)
• NIST AI Risk Management Framework

Statutory status changes frequently and several provisions here are subject to pending litigation. This article is general information, not legal advice.

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Frequently Asked Questions

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Keep reading

Here are the latest posts from the blog.

Most discussion of sandboxing treats it as a wall. Either the agent is contained or it is not.

That framing produces bad architecture, because it leads teams to pick an isolation technology, declare the problem solved, and stop.

A better framing: sandbox isolation buys you three things, none of which is certainty.

It raises cost. An escape that requires a kernel exploit is dramatically more expensive than one that requires reading an environment variable. Cost is a real defense even when it is not an absolute one.

It shrinks consequence. A compromised workload with no network route and no credentials is a compromised workload that cannot do much. This is where most of the value lives.

It creates a detectable event. A well-configured sandbox generates a signal when something attempts to cross it. A permissive one generates nothing, because nothing was refused.

Research suggests sandboxed agents experience roughly 90% fewer security incidents than agents with unrestricted host access. That is a large reduction and not an elimination, which is exactly the right way to think about the control.

Key Takeaways

• Sandbox isolation is not a binary property. It is a probabilistic delay that shrinks the consequences of a breach, and it should be designed on the assumption that it will eventually be crossed.
• An Oxford and UK AI Security Institute benchmark found frontier models reliably escape containers through common misconfigurations, while the hardest kernel-level scenarios still hold.
• The easiest escape scenarios in that benchmark are not exploits at all. They are configuration choices: privileged containers, a mounted Docker socket, a host path mount.
• Containers share the host kernel and expose roughly 350 system calls. MicroVMs give each workload its own kernel at a cost of around 125ms boot time and under 5 MiB overhead.
• Execution isolation alone is one layer of four. Egress control, credential scoping and state isolation determine what a successful escape is actually worth.

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Quick Navigation

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The Sandbox Isolation Spectrum

Isolation strength and overhead move together. Here is where the production options sit.

Approach	Boundary	Startup	Overhead	Typical use
Docker container	Namespaces + cgroups	~500ms	Tens of MB	Trusted, vetted code
gVisor	User-space kernel (Sentry)	~100ms	10–30% on I/O	Compute-heavy, limited I/O
Kata Containers	MicroVM, OCI-compatible	Moderate	Higher	Kubernetes with VM isolation
Firecracker	Dedicated guest kernel via KVM	~125–150ms	<5 MiB	Untrusted or generated code
WebAssembly	Linear memory model	Milliseconds	Minimal	Constrained, pure-compute tasks

The distinction that matters is where the boundary sits.

A container shares the host kernel. The Linux kernel exposes roughly 350 system calls, and a single exploitable bug in any of them can become an escape. Namespaces change what a process can see; they do not change which kernel handles its syscalls.

gVisor interposes a user-space kernel written in Go. Syscalls hit the Sentry process rather than the host kernel, cutting the exposed surface to a small vetted subset. Google Cloud Run reportedly uses this approach.

Firecracker boots a genuine lightweight VM on KVM, so each workload gets its own kernel behind a hardware virtualization boundary. It powers AWS Lambda and Fargate, and in June 2026 AWS launched Lambda MicroVMs explicitly for running user- or AI-generated code, with dedicated microVMs per session running up to eight hours.

When the largest serverless platform reaches for microVMs the moment the workload becomes “code generated by a model,” that is a meaningful signal about where practitioner consensus has landed.

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What the Sandbox Isolation Escape Benchmark Found

Until recently this debate ran on intuition. In March 2026, researchers from the University of Oxford and the UK AI Security Institute published SandboxEscapeBench, later selected as an ICML oral.

The design is elegant. An Inspect AI capture-the-flag evaluation runs inside a nested architecture: a vulnerable inner container sits within an outer VM that holds the flag and has no known vulnerabilities. The model gets shell access inside the container and must reach the host to win. If it escapes the inner layer, it is still contained.

The benchmark covers 18 scenarios across three categories — four orchestration, eight runtime, six kernel — rated from difficulty 1 to 5.

The headline findings are worth stating precisely.

Frontier models reliably exploit common misconfigurations and exposed control paths. These are the low-difficulty scenarios, and they are not close.

Moderate scenarios produce mixed success. Known runtime CVEs and capability abuse fall here.

The hardest scenarios still hold. Kernel-level and complex privilege-escalation paths remain out of reach for current models.

Scale, hints and token budget all measurably affected success rates — which means the results are a moving target rather than a fixed capability ceiling.

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Why Misconfiguration Breaks Sandbox Isolation First

Look at what the benchmark rates as easiest, and the practical lesson becomes clear.

The difficulty-1 scenarios are a privileged container, a mounted Docker socket, and a host path mount. None is a vulnerability. All three are configuration decisions someone made deliberately, usually for convenience during development.

This mirrors what the disclosed containment failures of mid-2026 showed across multiple frontier labs: four of five were plain configuration errors rather than novel exploits. The environments were documented as isolated and were not.

The implication reorders most security roadmaps. Teams worry about kernel CVEs and deploy microVMs to address them. Meanwhile the actual failure path is a socket mount added six months ago to make local testing easier.

Audit your configuration before upgrading your isolation technology. A hardened container with no privileged flags, no socket mounts and no host paths beats a microVM running with a mounted Docker socket. The stronger boundary is irrelevant if you have opened a door through it.

This is one layer of a wider surface mapped in our breakdown of the five hidden layers of the AI attack surface.

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Layer 1: Execution Sandbox Isolation

The first layer is the sandbox proper, and the decision rule is simpler than the vendor landscape suggests. Match the boundary to what the agent can do.

Text-only agents with no code execution, no tool access and no network can sit in a hardened container. The risk surface is genuinely small.

Agents running your own vetted code that passed CI are also reasonable in containers. You wrote it; the threat model is accident, not adversary.

Agents executing model-generated code need stronger isolation. The code was not reviewed by anyone and may do something unintended without any attacker involved. gVisor is defensible here; microVMs are safer.

Agents installing unvetted packages or running arbitrary binaries should be treated as running hostile code. Firecracker or Kata. Not a container.

Two operational notes that decide whether this is practical. Firecracker’s snapshot-restore can pause and resume a sandbox in 5–30ms, which makes multi-turn agent sessions viable without re-initializing the environment each turn. And for Kubernetes teams, kubernetes-sigs/agent-sandbox handles lifecycle management and gives a migration path as requirements harden.

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Layer 2: Egress Control Beyond Sandbox Isolation

If you fix only one thing after reading this, make it this layer.

An agent that can make outbound network requests can exfiltrate data, fetch further instructions, or reach services you never intended it to touch. Execution isolation does nothing about any of that, because the agent is using the network legitimately from inside its sandbox.

Egress control is what converts a successful escape into a contained one. The published containment failures illustrate this precisely: in several cases the model searched deliberately for an internet route and found one through infrastructure nobody had classified as an egress path.

Three rules follow.

Default deny outbound. Allowlist the specific hosts the agent needs. Claude Code, for instance, blocks curl and wget by default as part of its command blocklist.

Treat package installation as egress. PyPI, npm and package proxies are network routes. In documented incidents, package registries were the exit.

Isolate the network names-pace. MicroVMs provide this at the hypervisor level rather than relying on host firewall rules an agent might influence.

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Layer 3: Credential Scoping

The third layer determines what an escaped agent can authenticate as.

A sandbox breach that yields no usable credentials is an inconvenience. A sandbox breach that yields a long-lived token with broad scope is an incident.

The controls here are conventional and frequently skipped.

No static credentials in the sandbox environment. Environment variables are the first thing an agent with shell access reads.

Short-lived, scoped tokens issued per task, not per agent and certainly not per team.

Distinct identity per agent, so a compromise can be revoked without breaking everything sharing a credential.

This layer interacts directly with the first three. An agent with perfect execution isolation, strict egress control and an over-permissioned credential is still one prompt injection away from a bad day — the distinction between systems that generate and systems that act, covered in agentic AI versus generative AI.

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Layer 4: State and Blast Radius

The final layer asks what persists and what is reachable.

Ephemerality is a security control. A sandbox destroyed after each task cannot carry a compromise forward. Persistent sandboxes are convenient for multi-turn work and give an attacker a foothold that survives.

Multi-tenancy raises the stakes. In a shared platform, one tenant’s workload must not read another’s. Namespace isolation on a shared kernel is adequate for low-risk work and insufficient for compliance-sensitive multi-tenancy.

Separate sensitive resources from general-purpose ones. An agent that needs a scratch file system should not sit in the same trust domain as one holding production database access.

Resource limits prevent a different failure. Runaway loops, disk exhaustion and memory consumption are availability problems that cgroup limits, timeouts and watchdogs address.

The measure to track is blast radius: what could this agent reach if the sandbox failed right now? That number, not the isolation technology’s name, is your actual risk posture.

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What Sandbox Isolation Cannot Do

Three failure modes sit entirely outside the sandbox, and conflating them with isolation is how teams end up surprised.

Sandbox isolation does not stop prompt injection. Injection happens in the model’s context window, not in the execution environment. A perfectly isolated agent that has been injected will use its legitimate, authorized capabilities to do the attacker’s work. The sandbox constrains what those capabilities are; it does not restore the agent’s judgement.

It does not stop specification gaming. When a model pursues its stated objective through an unintended route, every action may be permitted by policy. Nothing gets refused, so nothing gets logged as a denial. This is why the mid-2026 lab incidents were invisible to the organisations affected — two of three had not detected the activity at all.

It does not govern authorized tool calls. An agent with legitimate database write access, operating inside a flawless microVM, can still issue a destructive query. Isolation answers “can this code reach the host,” not “should this action happen.”

The practical consequence is that sandbox isolation belongs in a stack, not at the top of it. Above it sits authorization policy deciding which actions are permitted. Below it sits egress and credential scoping limiting the value of any breach.

Teams that treat the sandbox as the whole answer tend to have strong execution boundaries and no policy layer, which produces a system that is very good at containing the failure mode least likely to occur.

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Designing Sandbox Isolation to Fail Safely

Assume the boundary is crossed. Then work backwards.

Nest your boundaries. SandboxEscapeBench’s own architecture is the pattern worth copying: a vulnerable inner environment inside an outer one with no known weaknesses. An escape from layer one lands in layer two.

Instrument the boundary. Log denied syscalls, blocked egress attempts and permission failures. These are your earliest indicators, and a permissive sandbox produces none of them because nothing gets refused.

Log the full tool call chain. After an incident you need to reconstruct what the agent did and what triggered it. Per-call logs without chain context will not do this.

Test the escape path yourself. SandboxEscapeBench is open source under UK Government BEIS. Run your configuration against the difficulty-1 scenarios at minimum — those are the ones models pass reliably.

Keep a human gate on irreversible actions. Where automated policy enforcement is immature, human-in-the-loop approval on high-privilege operations remains the practical control.

Rehearse containment. Measure how long it takes to terminate a running agent and what else breaks when you do. Teams routinely discover the answer is “we cannot” only during an incident.

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Matching Sandbox Isolation to Your Threat Model

A short decision guide, because over-engineering has costs too.

Internal tooling, trusted code, single tenant. Hardened container. Verify no privileged flags, no socket mounts, no host paths. Add egress allow-listing.

Agent generates and runs code, single tenant. gVisor or microVM, depending on I/O sensitivity. Ephemeral sandboxes. Default-deny egress. Scoped short-lived credentials.

Multi-tenant platform or user-supplied code. Firecracker or Kata microVMs. This is the 2026 baseline for shared platforms, and managed options provide sub-second provisioning if you would rather not operate the infrastructure.

Security research or deliberately adversarial evaluation. Nested isolation, air-gapped where feasible, with explicit verification that no egress path exists. The mid-2026 lab incidents all occurred in environments assumed to meet this bar and did not.

The cost of stronger isolation has fallen far enough that the old trade-off argument is weak. A boot penalty around 125ms and overhead under 5 MiB is not a meaningful tax for most agent workloads.

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Primary sources

• Marchand et al., Quantifying Frontier LLM Capabilities for Container Sandbox Escape (arXiv:2603.02277)
• SandboxEscapeBench source, UK Government BEIS
• Firecracker
• gVisor
• kubernetes-sigs/agent-sandbox

Isolation performance figures vary by workload and configuration; those quoted are representative published values. Corrections with a primary source are welcome.

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Frequently Asked Questions

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Keep reading

Here are the latest posts from the blog.

Last verified: 13 August 2026. Prices change frequently — check provider pages before budgeting.

Ask what a token costs and you will get an answer to a question you did not ask.

There are three distinct numbers, and almost every discussion slides between them without noticing.

The list price is what a provider publishes — $5 per million input tokens, $25 per million output. This is a price, not a cost.

The blended effective rate is what you actually pay once your real request shape, caching, batching and reasoning tokens are accounted for. This is the number on your invoice.

The serving cost is what it costs in GPU time to produce a token. This is what the provider pays, and what you pay if you self-host.

The gap between list price and serving cost is provider margin. The gap between list price and your blended rate is engineering. Both are large, and each responds to completely different decisions.

Key Takeaways

• “Cost per token” means three different numbers: the list price, your blended effective rate, and what it costs to physically serve a token. Confusing them is where budgets break.
• Headline input price predicts almost nothing. At a typical 1,000-in/500-out request shape, GPT-5.6 Sol’s $5 input price produces a blended rate of $13.33 per million tokens.
• Self-hosted cost figures are meaningless without a stated batch size. The same H100 at the same hourly rate ranges from $0.17 to $8.74 per million output tokens depending purely on throughput.
• utilization dominates everything. A GPU running at 10% load costs ten times as much per token as the same GPU at full load.
• GPT-4-class capability fell from roughly $20 per million tokens in late 2022 to about $0.40 in 2026 — a decline steeper than PC compute or dotcom-era bandwidth.

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Quick Navigation

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Layer One: Published Inference Cost Per Token

Current published rates, per million tokens, input/output. Frontier-tier list prices have converged tightly.

Model	Input	Output	Tier
Claude Fable 5	$10	$50	Premium reasoning
GPT-5.6 Sol	$5	$30	Frontier
Claude Opus 5	$5	$25	Frontier
Claude Sonnet 5	$2*	$10*	Mid
GPT-5.6 Terra	$2	$12	Mid
Claude Haiku 4.5	$1	$5	High-volume
GPT-5.6 Luna	$0.20	$1.20	High-volume
DeepSeek V4-Flash	$0.14	$0.28	Budget

*Sonnet 5 is promotional until 31 August 2026, moving to $3/$15 on 1 September.

That last row deserves a note in your calendar. A 50% input price increase arriving in under three weeks is exactly the kind of thing that turns a validated cost model into a surprise.

Note also the spread. Between DeepSeek V4-Flash and Claude Fable 5 there is a 71× difference on input and a 178× difference on output. That range is why “what does inference cost” has no single answer.

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Why Headline Inference Cost Per Token Misleads

Comparisons almost always quote input price. Input price is the least useful number on the sheet.

Output tokens cost more everywhere, because generating them requires a forward pass per token while input can be processed in parallel. But the multiple varies enormously by provider.

GPT-5.6 Sol charges six times its input rate for output. Claude Opus 5 charges five times. DeepSeek V4-Flash charges twice. Grok 4.3 charges twice.

So two models with identical input prices can differ by 20% or more on your actual bill, depending entirely on how much text your application generates.

Understanding why output costs more requires knowing what the hardware is doing differently in each phase — a distinction covered in our breakdown of inference chips versus training chips.

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Calculating Your Blended Inference Cost Per Token

The fix is simple arithmetic that almost nobody does.

Blended rate = (input tokens × input price + output tokens × output price) ÷ total tokens

Take a common request shape: 1,000 input tokens, 500 output tokens. Here is what that does to the rankings.

Model	List input	Blended rate
GPT-5.6 Sol	$5.00	$13.33
Claude Opus 5	$5.00	$11.67
Claude Sonnet 5 (Sept)	$3.00	$7.00
Claude Sonnet 5 (promo)	$2.00	$4.67
Claude Haiku 4.5	$1.00	$2.33
GPT-5.6 Luna	$0.20	$0.53
DeepSeek V4-Flash	$0.14	$0.19

Two observations.

GPT-5.6 Sol and Claude Opus 5 have identical list input prices and a 14% gap in blended rate, purely from the output multiple.

And every blended figure is higher than its headline — between 1.4× and 2.7× higher. If you budgeted from the input column, you underbudgeted.

Run this with your own ratio before choosing a model. A summarisation workload (heavy input, light output) and a code-generation workload (light input, heavy output) will rank providers in different orders.

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The Multipliers Hidden Inside Inference Cost Per Token

Four adjustments change the arithmetic, two upward and two downward.

Reasoning tokens (upward). Reasoning models generate intermediate tokens before their visible answer. Those are billed as output. A response that shows 200 tokens may have billed for 2,000. This is the single largest source of unexpected overspend on reasoning-tier models.

Long-context tiers (upward). Gemini 3.1 Pro doubles its rate beyond 200,000 tokens per request, moving from $2/$12 to $4/$18. Grok 4.5 and 4.3 do the same. If your RAG pipeline stuffs context aggressively, you may be paying the higher tier without realizing a tier exists.

Prompt caching (downward). Repeated prefixes — system prompts, few-shot examples, static documents — can be cached. DeepSeek charges roughly $0.0036 per million on cache hits against $0.14 standard, about a 97% reduction. For applications with a large fixed system prompt, this is usually the biggest available saving.

Batch processing (downward). Asynchronous batch endpoints typically halve rates. If your workload tolerates delayed completion, this is free money.

Cache and batch together can move a real bill by an order of magnitude, and neither changes a single line of model output.

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Layer Three: Inference Cost Per Token on Your Own GPUs

Self-hosting replaces a per-token bill with a per-hour bill. The conversion is one formula.

Cost per million tokens = (GPU hourly rate ÷ tokens per second ÷ 3,600) × 1,000,000

The hard part is not the formula. It is getting an honest throughput number.

Here is the same H100 at $2.99 per hour, at different throughputs:

Throughput	Cost per million output tokens
95 tok/s (single stream)	$8.74
380 tok/s (batch = 8)	$2.19
1,000 tok/s	$0.83
3,000 tok/s	$0.28
5,000 tok/s	$0.17

Same hardware. Same hourly rate. A 51× spread in cost per token.

This is why hourly GPU rates tell you nothing on their own. An H100 at $2.99/hour and an A100 at $1.64/hour are neither expensive nor cheap until you know what each produces. A GPU costing twice as much per hour but generating three times the tokens is 1.5× cheaper per token.

The hardware economics underneath this are covered in more depth in the AI compute stack.

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Why Published Self-Hosting Numbers Disagree

Now apply that table to the figures circulating online, because this is where most cost comparisons quietly fall apart.

A widely cited benchmark reports self-hosted Llama 4 70B on an H100 at roughly $0.18 per million output tokens, and separately reports 380 tokens per second at batch size 8.

Those two claims are not compatible. At 380 tok/s and $2.99/hour, the arithmetic gives $2.19 per million — twelve times higher. Reaching $0.18 requires sustained aggregate throughput near 5,000 tokens per second, which is achievable with continuous batching at high concurrency, but is a very different operating condition from batch=8.

Both numbers may be correct in isolation. Presented together without the batch context, they produce a cost estimate an order of magnitude off.

The practical rule: any self-hosting cost figure without a stated batch size and utilization assumption is unfalsifiable. Ask for both before you build a business case on it.

This also explains the wild range in published break-even points. Estimates cluster around 2–5 million tokens per day on reserved capacity over a twelve-month window, but that figure moves substantially with the same two variables.

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The utilization Problem Nobody Prices

There is a second variable that matters more than throughput, and it appears in almost no comparison.

You rent a GPU by the hour whether or not you use it.

Take an H100 capable of 3,000 tokens per second at full load, at $2.99 per hour:

Utilisation	Effective cost per million tokens
100%	$0.28
50%	$0.55
25%	$1.11
10%	$2.77

At 10% utilization, self-hosting costs more per token than several managed APIs, while also requiring you to run the infrastructure.

Production traffic is not flat. It has daily peaks, weekly troughs and quiet nights. Unless you are batching offline work into the gaps, average utilization on a dedicated GPU is frequently below 30%.

This is the honest answer to “should we self-host.” Not model quality, not hourly rates — can you keep the GPU busy? If your traffic is spiky and you cannot backfill, the managed API is usually cheaper despite the visible margin, because the provider is amortising idle capacity across thousands of customers and you would be absorbing it alone.

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Why Agents Break Inference Cost Per Token Models

One workload shape deserves separate treatment, because it breaks every estimate built on chat assumptions.

A chat request is one call. An agentic task is many.

An agent reasoning through a multi-step task calls the model repeatedly — once to plan, once per tool invocation, once to interpret each result, once to decide whether it is finished. Each call carries the accumulated conversation forward as input.

Two compounding effects follow, and they multiply rather than add.

Context grows with every step. Step one sends 1,000 input tokens. Step ten may send 15,000, because it carries every prior step’s output. Input token consumption grows roughly quadratically with step count, not linearly.

Reasoning tokens stack per call. If each call generates 2,000 intermediate tokens billed as output, a twelve-step task bills 24,000 output tokens for a task whose visible result is three paragraphs.

The practical consequence: a single agent run can cost more than a thousand chat completions. Teams that validated their unit economics on a chat prototype and then shipped an agent routinely see bills an order of magnitude above forecast, and the model choice was never the problem.

Three mitigations work specifically here. Cache the stable prefix — in an agent loop the system prompt and tool definitions repeat on every call, which is the ideal caching case. summarize rather than accumulate — replace full history with a compressed state object past a threshold. Route by step — planning may need a frontier model, but parsing a tool response usually does not.

The distinction between systems that generate text and systems that take actions is set out in agentic AI versus generative AI, and it is exactly the distinction that separates a predictable bill from an unpredictable one.

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How Inference Cost Per Token Collapsed

The trend line matters as much as any single figure.

GPT-4-class capability cost roughly $20 per million tokens in late 2022. Equivalent performance runs near $0.40 in 2026. Analysts have noted this decline outpaced both PC compute and dotcom-era bandwidth, running at roughly 10× annually.

Three forces drove it. Hardware improved — H100 cloud rates fell 64–75% from their peaks to settle around $2.85–$3.50 per hour. Serving software improved — continuous batching, speculative decoding and quantization extract far more throughput from the same silicon. And competition intensified, with open-weight providers pricing 50–90% below frontier APIs.

Two consequences follow.

Inference now dominates AI compute. Training a frontier model is a one-time event; serving it runs continuously for years. Inference accounts for roughly two-thirds of total AI compute in 2026, up from about one-third in 2023.

Cheaper tokens have not reduced GPU demand. This is Jevons paradox in action: falling per-token costs open new use cases faster than they reduce spend, so aggregate demand rises. Anyone forecasting lower GPU rates from falling token prices has the causality backwards.

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Cutting Your Inference Cost Per Token

Ordered by return on effort.

Route by complexity first. Most applications send every request to their best model. A classifier that routes roughly 70% of queries to a budget tier, 20% to mid, and 10% to frontier commonly cuts spend by 80% or more with little quality impact, because the hard queries still reach the strong model.

Cache aggressively. If your system prompt is stable, cache it. A 97% reduction on the cached portion is available for a configuration change.

Batch anything asynchronous. Roughly 50% off for accepting delayed completion.

Then reduce tokens. Shorter system prompts, tighter retrieval, output length limits. This is real but slower work than the three items above.

Measure per task, not per month. A monthly total tells you what you spent. Cost per completed task tells you whether the spend is productive — and it is the only metric that survives a traffic change.

Re-verify quarterly. Prices moved twice in the last six weeks alone: a July 30 cut on one tier, and a promotional rate expiring 1 September. A cost model built in February is stale by August.

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Primary sources

• Anthropic pricing
• OpenAI API pricing
• Google Gemini API pricing
• DeepSeek API pricing
• Cost-per-token arithmetic in this article is derived from the published formula and shown in full so readers can substitute their own figures.

Model pricing changes frequently and several rates above carry known expiry dates. Verify against provider pages before committing to a budget.

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Frequently Asked Questions

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Keep reading

Here are the latest posts from the blog.

The Model Context Protocol, introduced by Anthropic in November 2024, standardizes how AI models connect to external tools and data. Before it, every integration needed custom code. After it, a model could talk to a database, a file system or an API through one interface.

Adoption was fast. The public server registry grew from roughly 1,200 entries in early 2025 to more than 9,400 by mid-April 2026 — a seven-fold expansion in about fourteen months.

MCP security is the practice of governing what happens at the join between a model and the tools it can invoke. That includes who may connect, what a server may expose, what a tool may actually do when called, and whether any of it can be reconstructed afterwards.

The protocol solved a genuine integration problem. It also created a new trust boundary, and that boundary was not designed with adversaries in mind.

Key Takeaways

• MCP security fails at a specific seam: the tool description field is unsanitised text that the model reads as instruction and the user never sees.
• Roughly 40% of internet-exposed MCP servers accept requests with no credential check. Among those that do authenticate, 53% rely on static API keys alone.
• The 2026-07-28 specification revision is the largest since launch and makes MCP servers formal OAuth 2.1 resource servers. Much published guidance still describes the older, weaker model.
• Published vulnerability rates vary enormously by methodology, and one audit found a 78% false-positive rate from YARA-based MCP scanners. Treat single percentages with caution.
• Most disclosed MCP CVEs are not novel AI attacks. 43% of CVEs filed in early 2026 were shell injection.

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Quick Navigation

The Model-to-Tool Boundary Explained

To see where things break, follow what happens when an agent connects to an MCP server.

The server advertises its tools. For each one it sends a name, a description in natural language, and a parameter schema. The client loads all of that into the model’s context so the model knows what is available and when to use it.

The user sees a friendly label — something like “Send email” in a list of connected capabilities.

The model sees the full description text.

Those two views are not the same, and the gap between them is the whole problem. The description field is where the server tells the model how to behave. It is unsanitised by design, it is rarely rendered in any interface, and almost nobody reads it after the first install.

This is the distinction between systems that generate text and systems that take actions, explored further in our comparison of agentic AI and generative AI. A tool description is not documentation. It is executable influence.

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Why MCP Security Fails at the Description Field

Invariant Labs first documented tool poisoning in April 2025. The mechanism is simple enough to state in a sentence: a malicious or compromised server embeds instructions inside what looks like help text, and the agent follows them.

OWASP ranks this at position three in its MCP Top 10, a framework currently in beta under project lead Vandana Verma Sehgal.

Three properties make this class unusually difficult.

It is invisible to source-code scanning. SAST and SCA tools read code. A poisoned description lives in a metadata field the scanner has no reason to parse. A clean scan tells you nothing about it.

It persists. A document-borne injection has to be delivered again each time. A poisoned description ships with the server and fires on every invocation, in every session, for every user, until someone reads the metadata.

Approval happens once. Users approve a tool at install and never revisit it. That is what makes rug pulls work — a server can redefine a tool’s description silently after approval.

Microsoft’s guidance reframes this usefully: treat a tool description change as equivalent to a dependency update. It is a modification to a software artifact that directly changes agent behavior, and it deserves review before deployment. The recommended controls follow from that framing — signed tool manifests, automated metadata scanning for embedded instructions, and dynamic tool scoping that limits an agent to the specific tools a session needs.

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The Four Baseline MCP Security Failures

Four classes appear across essentially every serious MCP security resource. They are the vocabulary you need before anything else makes sense.

Tool poisoning. Hidden instructions in tool descriptions or metadata. The model reads and acts on them; the user sees only the benign label.

Confused deputy. An MCP proxy holding elevated privileges performs an action for a user without verifying the user was entitled to it. The server’s permissions become the user’s permissions.

Prompt injection through tool output. A tool returns attacker-controlled content in a free-text field, and that content enters context as though it were data rather than instruction.

Token pass through. A server accepts a token issued for a different service and forwards it downstream without validating the audience. The specification explicitly forbids this as an anti-pattern, which tells you how often it happens.

Two further classes matter in multi-server deployments. Tool shadowing occurs when one server’s description manipulates how the agent uses a different server’s tools. Cross-server cascade describes what happens next: research indicates a 72.4% cascade rate once multiple connected servers are compromised.

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What the MCP Security Scan Data Shows

The empirical picture is worse than the taxonomy suggests, though the numbers need care.

Finding	Figure	Source context
Publicly exposed MCP services	12,520	Censys scan
Exposed servers with no authentication	~40%	Multiple converging scans
Authenticating servers using static API keys only	53%	Same scan population
Servers implementing OAuth 2.1	8.5%	Registry analysis
File operations vulnerable to path traversal	82%	2,614 surveyed servers
Command injection exposure	34%	Same 2,614 servers
SSRF exposure	30–36.7%	BlueRock / Equixly
Zero-days found by automated repo scan	106	VIPER-MCP, 39,884 repos

The authentication figure is the one to sit with. Roughly four in ten internet-accessible MCP servers accept requests from anyone, and of the remainder, half use a single long-lived credential with no expiry and no per-operation scope.

Meanwhile, GitGuardian found 24,008 secrets in MCP-related configuration files on public GitHub, of which 2,117 remained valid.

None of this is exotic. Path traversal, command injection and missing authentication are classic web application failures wearing new clothes. The novelty is the blast radius: these servers sit inside agent workflows with access to whatever the agent can reach, a mapping problem covered in the five hidden layers of the AI attack surface.

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Reading MCP Security Statistics Critically

Here is the part most coverage omits, and it matters if you plan to cite any of these numbers.

Reported tool-poisoning prevalence ranges from about 5.5% of 1,899 servers in one academic study, to 23% of servers showing suspicious instruction-like patterns in an Invariant Labs scan, to 66% of community servers carrying at least one critical code smell in another analysis.

Those are not the same measurement. “Contains an instruction-like pattern” and “is actively malicious” are different claims, and conflating them inflates the problem.

More pointedly: an independent audit in April 2026 found roughly a 78% false-positive rate from YARA-based MCP scanners. Any raw “X% of servers are vulnerable” figure should be read against the detection method that produced it.

Unauthenticated-server counts vary the same way — 1,862 in a July 2025 scan, 8,247 in an Invariant Labs scan in January 2026, around 40% of 12,520 in Censys data. Different populations, different dates, different definitions of exposure.

The direction is unambiguous and the magnitude is contested. Both facts belong in any honest summary.

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The MCP Security CVE Record

Between January and February 2026 alone, researchers filed more than 30 CVEs against MCP servers, clients and tooling. The breakdown is instructive: 43% were exec or shell injection, 20% were flaws in tooling infrastructure such as clients and inspectors, and 13% were authentication bypass.

Four are worth knowing by name.

CVE-2025-6514 (CVSS 9.6) affected mcp-remote, a package downloaded more than 437,000 times. Disclosed by JFrog in July 2025, it allowed remote code execution triggered by a malformed response from a compromised server — described at the time as the first real-world full RCE on a client operating system through an untrusted remote MCP server. Fixed in version 0.1.16.

CVE-2025-49596 (CVSS 9.4) hit Anthropic’s own MCP Inspector, enabling RCE via browser and DNS rebinding.

CVE-2026-30623 is the structurally interesting one. A command injection flaw in the STDIO transport interface across all four official SDKs — Python, TypeScript, Java and Rust — affecting a reported 200,000+ instances across 7,000+ public servers, with proven exploits against LiteLLM, LangChain and IBM LangFlow. At least ten CVEs trace to this single class.

Anthropic’s position on it was that this is expected behavior, with input sanitisation the developer’s responsibility. That is a defensible reading of a transport-layer specification and a genuine problem for everyone who assumed the SDK handled it.

CVE-2026-26118, an SSRF in the Azure MCP Server, exploited OAuth proxy trust. Authentication existed; the authorization boundary did not.

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Supply Chain Incidents Worth Knowing

Two incidents show the ecosystem risk rather than the protocol risk.

postmark-mcp. In September 2025, Snyk documented a malicious npm package version that silently blind-copied every processed email to an external domain. It was the first tracked malicious-MCP-server supply-chain incident, and the mechanism required no protocol flaw at all — just a package doing something extra.

IDE auto-execution. A vulnerability class concerning the conditions under which developer IDEs execute MCP servers was reported to Amazon on 20 April 2026, received an initial fix on 12 May, and was publicly disclosed under Security Bulletin 2026-047-AWS on 26 June. Combined with description poisoning, auto-execution creates a compound surface reaching well beyond a single workstation.

The pattern to take away: the protocol’s security properties and the ecosystem’s security properties are separate things, and the ecosystem is where most incidents originate.

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How the July 2026 Spec Changes MCP Security

A large amount of published MCP security guidance is now describing a specification that no longer exists. This is worth correcting carefully.

Early MCP made authorization effectively optional, which is where the widely repeated criticism comes from. That changed in stages. The June 2025 revision separated the MCP server (resource server) from the authorization server role and replaced fallback endpoints with mandatory Protected Resource Metadata under RFC 9728. The November 2025 revision required OAuth 2.1 with PKCE for remote servers.

The 2026-07-28 revision — released as a candidate on 21 May and described by maintainers as the largest since launch — went furthest. It removes sessions, drops the initialization handshake, deprecates three core features, rewrites authorization, and introduces an extensions framework.

For security specifically: MCP servers are now formally OAuth 2.1 resource servers, must implement RFC 9728 so clients can discover the correct authorization server automatically, and must use audience-bound tokens so a token issued for one server cannot be replayed against another. Clients must send resource indicators regardless of whether the authorization server supports them.

The caveat that matters: a specification requirement is not a deployment reality. Only about 8.5% of servers implement OAuth 2.1, and local STDIO servers sit outside the remote-server requirements entirely. The spec has largely caught up. The installed base has not.

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Where MCP Security Enforcement Should Live

One architectural question decides whether the checklist above is achievable or aspirational: where does enforcement happen?

Three options exist, and only one scales.

In the model. Prompt the agent to ignore suspicious tool descriptions. This fails for the same reason every instruction-based defense fails — the poisoned description is also an instruction, and the model has no reliable way to rank them.

In each server. Ask every MCP server to validate its own inputs and scope its own permissions. This is correct in principle and unachievable in practice, because you do not control most of the servers you connect to, and 43% of early-2026 CVEs were shell injection in exactly these servers.

In a gateway between them. A proxy that intercepts every tool invocation, checks the caller’s identity, evaluates the requested operation against policy, and forwards the call only if it passes.

The gateway is the only position with both the visibility and the authority to enforce anything. It sees every call regardless of which server would handle it, and it can refuse.

This is the same reference-monitor pattern security has used for decades, applied at a new boundary. A production arrangement circulating in 2026 pairs workload identity for the agent, a relationship-based authorization service holding the permission graph, and a gateway that consults that service before forwarding any tool call.

Two practical notes. A gateway does not stop tool poisoning — a poisoned description still reaches the model — but it does stop the resulting call if that call falls outside policy. And it gives you the per-request logging that OWASP flags as absent by default, which is what makes incidents reconstructable afterwards.

Adoption friction is real. Roughly 38% of organisations report that security concerns are actively blocking MCP adoption, and 50% of MCP builders name access control as their hardest problem. A gateway addresses both, at the cost of a component someone has to run.

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An MCP Security Checklist That Holds Up

Ordered by what actually reduces exposure rather than what is easiest.

Treat every MCP server as an untrusted third party. This is the Cloud Security Alliance’s framing and the correct default, including for internal servers.

Pin tool versions and signed schemas at install. Then alert on any description drift afterwards. This is the only control that addresses rug pulls, because it targets the change rather than the content.

Require authentication on everything reachable. OAuth 2.1 with PKCE, per-client consent, strict redirect-URI matching, audience-bound tokens. If you inherited static API keys, that is the first migration.

Never forward a token you did not validate. Token pass through is a named anti-pattern for good reason.

Isolate sensitive servers from general-purpose ones. A poisoned tool should not be able to reach across into a privileged server without crossing another boundary.

Show the full tool call, not a friendly summary. Users cannot approve what they cannot see. Where automated enforcement is immature, human-in-the-loop approval on the tool list is doing real work.

Log every tool call with arguments and output. OWASP includes insufficient logging in its Top 10 because most clients and servers log almost nothing by default. Without per-request logs there is no forensic trail.

Inventory shadow servers. A developer installs a community server for convenience, it works, nobody audits it, and it inherits everything the agent can reach.

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Primary sources

• Model Context Protocol specification
• OWASP MCP Top 10
• Cloud Security Alliance — MCP Security Crisis research note
• Microsoft — The state of MCP security in 2026
• CSA — MCP tool poisoning and auto-execution

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Frequently Asked Questions

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