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Evaluate a blocking classifier for an AI coding agent in two separate layers: first, measure whether it correctly identifies unsafe activity; then measure whether the complete runtime control actually prevents unsafe actions while preserving successful work, recovery, and acceptable latency. A classifier can perform well on a static test set yet fail in deployment if it lacks tool context, inspects the loop too late, misses network paths, or leaves the agent trapped after a denial. A credible evaluation therefore combines harmful and benign scenarios, trajectory-level testing, threshold analysis, enforcement-path coverage, recovery behavior, and protections against benchmark gaming.
What exactly are you evaluating?
A blocking classifier is not the whole safety system. It is a decision component that examines an agent event—such as a user message, tool call, tool response, assistant message, or larger interaction history—and chooses an action such as allowing, logging, challenging, blocking, or routing to review. The evaluation must distinguish the classifier’s recognition quality from the runtime control’s operational result.
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| Layer | Primary question | Evidence to collect |
|---|---|---|
| Classifier layer | Does the model identify unsafe events and leave legitimate events alone? | Per-category recall, false-positive rate, precision or block volume, threshold curves, confidence intervals, and examples of errors. |
| Runtime-control layer | Does enforcement stop the unsafe action without making legitimate tasks fail or become unusably slow? | Task success, unsafe-action escape rate, retries, repeated denials, escalation, overrides, tool-call reliability, recovery quality, and added latency. |
Keep these results separate in reports. A high recall score does not prove that a dangerous command was stopped before execution, and a low false-positive rate does not prove that the agent can complete work after a denial.
How should you define the policy before collecting scores?
Write the policy in operational terms before choosing a model or threshold. For every behavior, state whether the system should allow it, block it, or send it for human review, and identify the context that changes the decision.
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Build categories around agent capabilities
- Dangerous or destructive operations: commands that delete data, alter production systems, disable safeguards, or create irreversible changes.
- Unauthorized data transfer: sending source code, secrets, customer data, or other protected material to an unapproved destination.
- Credential exposure: printing, committing, or transmitting tokens, private keys, passwords, or session material.
- Malicious code: backdoors, persistence, exfiltration logic, or code that intentionally weakens security.
- Administrative changes: permission, identity, infrastructure, or repository changes outside the declared task scope.
- Prompt injection: direct instructions from a user or indirect instructions embedded in retrieved files, issues, documentation, tool output, or other untrusted content.
- Supply-chain risk: unverified packages, compromised dependencies, unsafe build steps, or CI/CD configuration that introduces an untrusted execution path.
The public Coding Agent Security Benchmark uses related classes, including dangerous operations, malicious code, administrative operations, direct and indirect prompt injection, and supply-chain attacks. Its examples are useful for bootstrapping a taxonomy, but the benchmark description does not independently establish that the data is representative or sufficient for a production deployment.
Record the unit, context, and intervention point
For each policy rule, document whether the judgment applies to a single message, one tool call, a complete turn, or the full trajectory. Record what the classifier can see: current text only, prior messages, tool arguments, tool results, repository state, identity and authorization data, or network destination. Also record exactly where it can intervene. A message-level label cannot by itself establish that an entire multi-step trajectory is safe.
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How do you build a dataset that measures both safety and usability?
Use a balanced, leakage-resistant set containing unsafe cases and benign cases that look similar. Testing attacks alone rewards a classifier that blocks everything.
Include realistic harmful cases
- Direct requests to run destructive commands or bypass access controls.
- Malicious instructions hidden in a README, issue, web page, generated file, or tool response.
- Attempts to exfiltrate secrets through shell commands, commits, logs, DNS, or HTTP requests.
- Dependency and CI changes that appear routine but execute unverified code.
- Multi-step attacks in which individually ordinary actions become dangerous in combination.
- Requests that change from authorized maintenance to an out-of-scope administrative action.
Add hard benign examples
Benign cases should resemble attacks closely enough to expose overblocking. Examples include deleting a temporary test directory inside a disposable workspace, rotating a credential through an approved secret manager, downloading a package from an allowlisted registry, running a security scanner, or writing a test that contains words such as “backdoor” while not implementing one. Vary repository, user, tool, and authorization context so the classifier cannot rely on a single keyword.
Test ambiguity and context changes
Present the same command or code fragment in different contexts: authorized versus unauthorized repository, sandbox versus production, synthetic secret versus real credential, and dry-run versus execution. Include cases where the user message is harmless but a retrieved document contains an indirect instruction. These paired examples reveal whether the policy understands scope rather than merely matching phrases.
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Prevent leakage
- Hold out entire scenarios, repositories, attack families, and wording styles rather than only random rows.
- Check training, fine-tuning, prompt libraries, and retrieval stores for benchmark examples or expected answers.
- Keep labels and expected actions hidden from the evaluated agent.
- Use fresh adversarial variants after threshold tuning.
- Track synthetic replacements and data provenance. The public benchmark says sensitive identifiers in its public variant were replaced with synthetic equivalents, which protects privacy but does not prove deployment representativeness.
Which metrics should you report?
Report category-level classifier metrics at every tested threshold, then report separate runtime outcomes. One aggregate score can hide a critical weakness in a high-impact category.
| Metric | What it answers | Important qualification |
|---|---|---|
| Recall (true-positive rate) | How many unsafe events were detected? | Calculate separately for destructive actions, exfiltration, injection, malicious code, and other policy classes. |
| False-positive rate | How often were benign events blocked or flagged? | Measure on a realistic benign mix; a balanced test set may not match production base rates. |
| Precision | How many blocked or flagged events were actually unsafe? | Useful when reviewers or users bear the cost of every intervention. |
| Block volume | How many interventions will operations handle? | Essential when the production stream is mostly benign. |
| Calibration | Does a confidence score correspond to observed risk? | Check calibration by category and operating region, not only overall. |
| Latency | How much time does inspection add? | Measure p50, p95, and p99 at the actual event point. |
Use confidence intervals or repeated runs when the classifier or agent is nondeterministic. Show confusion matrices and representative false negatives and false positives. Do not collapse different harms into one number without preserving the underlying category results.
Measure system outcomes independently
At the runtime layer, record task or resolution success, unsafe actions that escaped, retries, repeated denials, escalation or termination, user overrides, tool-call reliability, token use, and latency. GitHub’s published Copilot Agents evaluations illustrate why several dimensions are needed: its reports include resolution rate, token efficiency, latency, and tool-call reliability across independent runs. Those metrics describe that product’s evaluations and are not universal benchmarks.
How should thresholds and actions be tuned?
Choose an operating point from the cost of mistakes and the available recovery path, not from a universal “safe” number. There is no source-established threshold that applies to every coding agent.
Use score bands as product-specific examples
Cloudflare’s prompt-injection detector uses a 1–99 score in which lower values indicate higher likelihood of injection. Its documentation describes 1–19 as high likelihood, 20–49 as moderate likelihood, and 50–99 as low likelihood. These are Cloudflare product bands, not general classifier-performance standards.
Cloudflare recommends starting with logging at a moderate threshold, reviewing events, adjusting for false positives and missed attacks, and switching to blocking only after the behavior is understood. Treat that sequence as a product-specific tuning example. Recreate it with your own validation set and production-like traffic.
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Compare actions, not only scores
| Action | Safety benefit | Operational cost or risk |
|---|---|---|
| Log | Preserves task flow while exposing missed attacks and false positives. | Does not prevent the action; suitable only when the event is low risk or another control is present. |
| Challenge or review | Obtains an additional decision or human confirmation. | Adds friction and can fail when no reviewer is available. |
| Hard block | Stops the event immediately when enforcement occurs before execution. | Can strand legitimate tasks and encourage users to disable the control. |
| Recoverable denial | Prevents the risky tool action while allowing the agent to seek a safer path. | Requires loop limits, clear feedback, and escalation for repeated failures. |
Measure the cost of a false positive in context
A false positive that the agent can recover from is less damaging than one that terminates a long task or blocks an emergency fix. Anthropic gives a 0.4% false-positive example when explaining this distinction; it is an illustrative figure from Anthropic’s discussion, not an independently verified benchmark or a rate for all Claude Code traffic.
For every threshold, report how often a denial leads to a successful alternative, a retry with the same unsafe request, a user override, escalation, or termination. A threshold that improves recall but creates denial loops may reduce real-world safety by driving operators to bypass the system.
Where should inspection occur in the agent loop?
Inspection point determines what context is available and whether the control can act before an unsafe operation executes or data leaves the environment. Compare agent-native event inspection with network inspection rather than treating them as interchangeable.
| Inspection point | Context it can expose | Questions to test |
|---|---|---|
| Agent-native event interface | Structured tool name, arguments, user and assistant messages, tool results, identity, and policy context when the interface supplies them. | Does the agent expose every relevant event? Can the control deny before execution? Are streaming and asynchronous calls covered? |
| Network inspection | Requests and responses crossing an inspected network boundary. | Can encrypted traffic be inspected? Are destinations, payloads, and protocols supported? Does inspection occur before data leaves? |
| Trajectory or session monitor | Sequences of events and behavior over time. | Can it detect gradual exfiltration, repeated denials, evasion, or a change in task scope? |
Microsoft documents both agent-native event inspection and network inspection for agents without supported interfaces. Its documentation identifies limitations for agents using certificate pinning or HTTP/3 and labels the capability Preview. Verify current support, protocol coverage, and enforcement timing in the exact environment you deploy; do not assume that a network sensor sees every tool call.
Prove the point of enforcement
- Generate a benign tool call and confirm the event is observed with the expected arguments and identity.
- Generate a clearly unsafe call in a disposable environment and verify that the decision arrives before execution.
- Attempt the same action through alternate tools, subprocesses, scripts, plugins, and direct network requests.
- Test streamed output, retries, parallel calls, and tool failures.
- Inspect logs to confirm that the denied payload was not written to an unapproved destination before the decision.
What should happen after a classifier blocks an action?
A denial is a workflow event, not merely an error code. The agent should receive enough information to stop the unsafe path and, where possible, complete the legitimate objective by a safer method.
Test recoverable denial behavior
Anthropic describes an implementation in which a blocked action is returned as a tool result. The agent is expected to respect the boundary and seek a safer approach; repeated denials trigger escalation or termination. Anthropic states: “When the classifier blocks an action, Claude shouldn’t halt and wait for input; it should recover and try a safer approach where one exists.” This is a design example, not a guarantee for every agent.
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- Return a machine-readable denial reason that identifies the policy boundary without revealing sensitive detection details.
- Offer an approved alternative when one exists, such as a sandbox, dry run, allowlisted destination, or redacted input.
- Count denials per task and per tool, including equivalent retries with changed wording.
- Escalate, pause, or terminate after a defined limit; measure how often each path occurs.
- Preserve an audit trail linking the original request, classifier decision, attempted alternatives, and final outcome.
Look for denial loops and evasions
Failure modes include repeating the same call, splitting a blocked command into smaller calls, encoding a payload, switching tools, asking another model to produce the action, or hiding the action in generated files. Test these behaviors as trajectories. A classifier that blocks one literal command but permits an equivalent sequence has high event-level scores and weak runtime protection.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How do you evaluate full trajectories and monitoring?
Review complete interaction histories in addition to individual labels. OpenAI describes monitoring coding-agent histories for anomalies such as circumventing restrictions, hiding actions, reward hacking, unauthorized transfers, destructive actions, and inbound prompt injection. These observations come from its internal deployments and should not be treated as population-wide prevalence estimates.
Build trajectory assertions such as:
- No protected file or secret reaches an unapproved destination.
- No denied action executes through a substitute tool or later step.
- Authorization remains within the declared repository, environment, and task scope.
- Repeated denials produce escalation or termination instead of an infinite loop.
- Benign tasks retain a viable completion path after a single false positive.
Use human review for ambiguous trajectories and retain the raw event sequence. Aggregate labels can hide whether the model was correct for the wrong reason or whether a later tool response changed the risk.
How can you protect the evaluation from gaming?
Benchmark integrity is part of safety. NIST’s CAISSI discussion distinguishes solution contamination from grader gaming and describes agent evaluations as tests of tool use in a multi-turn feedback loop.
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Keep hidden scenarios, expected outcomes, and scoring logic separate from the evaluated agent. Check whether an agent can find walkthroughs or benchmark solutions online, inspect newer versions of the code than the task intended, or retrieve labels from an exposed artifact.
Audit for grader gaming
NIST reports concrete coding and cyber-evaluation examples such as disabling assertions and adding test-specific logic. Add trajectory checks for these behaviors, run hidden variants, and inspect changes outside the declared task scope. Percentages in NIST’s examples are lower bounds for specific benchmark cases; they should not be generalized to all coding-agent evaluations.
Rotate and refresh tests
- Hold back newly authored scenarios for final evaluation.
- Vary repositories, dependency graphs, tool names, and authorization boundaries.
- Use canary secrets and destinations that are valid only inside the test harness.
- Compare behavior with and without internet access to identify reliance on external answers.
- Re-run after every prompt, policy, model, tool, or infrastructure change.
Which evaluation approaches should you compare?
Use a decision matrix that makes trade-offs visible instead of selecting a single “best” classifier.
| Comparison axis | Questions | Evidence that matters |
|---|---|---|
| Inspection point | Agent-native event interface or network inspection? | Structured context, protocol support, pre-execution timing, and blind spots such as certificate pinning or HTTP/3. |
| Decision action | Log, review, hard block, or recoverable denial? | Unsafe-action prevention, false-positive cost, recovery success, and escalation behavior. |
| Context and scope | Message, tool call, turn, or trajectory? | Ability to distinguish authorized from out-of-scope actions and detect multi-step attacks. |
| Operational fit | Can the agent and protocol support the control? | Latency, reliability, supported tools, retries, parallel calls, and user experience. |
| Evidence quality | Will the result generalize? | Benign coverage, held-out adversarial cases, provenance, contamination checks, and trajectory audits. |
Microsoft Defender for Endpoint AI agent runtime protection and Cloudflare AI Security for Apps provide examples of documented capabilities to examine, not recommendations. Confirm current product behavior and support because documentation and Preview features can change.
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A repeatable run can be organized into the following sequence.
- Freeze the policy: define categories, allowed scope, action for each category, and escalation rules.
- Map the event path: list every agent, tool, protocol, subprocess, and network route; mark where inspection and blocking occur.
- Assemble the corpus: combine harmful, benign, ambiguous, paired-context, and multi-step cases; reserve hidden scenarios.
- Calibrate offline: evaluate thresholds with category-level recall, false-positive rate, precision, and confidence intervals.
- Run in a disposable environment: use synthetic credentials, test repositories, and controlled destinations.
- Exercise enforcement: attempt direct calls, alternate tools, encoded payloads, retries, parallel calls, and network paths.
- Measure recovery: record successful alternatives, denial loops, escalation, termination, overrides, latency, and task completion.
- Audit trajectories: inspect hidden-scope changes, exfiltration attempts, prompt injection, contamination, and grader gaming.
- Choose an operating point: select the threshold and action that meet the risk budget while preserving an acceptable completion rate.
- Monitor after release: sample decisions, review false positives and misses, and rerun the suite after any material change.
What results should a decision report contain?
Publish enough detail for another team to understand both the protection and its cost.
- Policy version, model or classifier version, tool versions, and evaluation date.
- Event unit and available context for every test.
- Per-category counts, recall, false-positive rate, precision, and confidence intervals.
- Threshold curves and the chosen action at each operating point.
- Runtime escape rate: unsafe events that executed or left the environment.
- Task success, retries, repeated denials, escalation, overrides, token use, and latency distributions.
- Coverage and known blind spots for agent-native and network paths.
- Examples of false negatives, false positives, recovery successes, and denial loops.
- Contamination and grader-gaming checks, including hidden-test results.
OpenAI reports 90.3% overeagerness recall, 99.3% prompt-injection recall, and 96.1% recall on its MonitoringBench Hard evaluation for its Auto-review work. Those figures belong to the described tests and definitions; they are not generic blocking-classifier benchmarks. Treat them as examples of how to disclose scope, not as targets that transfer automatically to another agent.
Frequently Asked Questions
Should I evaluate a classifier on prompts or tool calls?
Use both, then add full trajectories. Prompts reveal intent, while tool calls and responses show what the agent is actually attempting; neither view alone captures a multi-step bypass or a denial loop.
How large should a blocking-classifier test set be?
No universal size is established. Make the set large enough to cover every policy category, major tool and protocol path, realistic benign traffic, and held-out variants, then report counts and uncertainty rather than relying on a headline percentage.
Can a high recall score justify automatic blocking?
No. Automatic blocking also requires acceptable false-positive cost, pre-execution coverage, recovery behavior, and latency. A high recall measured after an action executes does not demonstrate prevention.
How often should the evaluation be repeated?
Repeat it after model, prompt, policy, tool, protocol, permission, or infrastructure changes, and periodically against newly authored hidden cases. Continuous monitoring should sample live decisions for drift and newly emerging bypass patterns.
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