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Evaluate a blocking classifier for an AI coding agent in two separate layers. First, test whether it correctly identifies unsafe events without blocking legitimate work. Second, test the complete runtime control: where inspection occurs, whether the action is stopped before execution or data transfer, how the agent recovers, and what the control does to task success, latency and user effort. A classifier can perform well on a labeled dataset yet fail in deployment because it sees too little context, runs after the tool call, or leaves the agent in a denial loop.
The practical standard is therefore not a single accuracy score. It is a documented policy, a leakage-resistant corpus containing harmful and benign examples, category-level threshold results, and end-to-end evidence that the agent can complete authorized tasks while unsafe actions are prevented or escalated.
What is a blocking classifier in an AI coding agent?
A blocking classifier scores an event in an agent session and decides whether to allow it, record it, ask for review, or stop it. The event may be a user message, an assistant message, a tool call, a tool response, a network request, or a larger sequence of actions. The classifier is only one component; the enforcement point, policy engine, agent runtime and recovery behavior determine the actual safety result.
Keep two questions separate throughout evaluation:
- Detection layer: Did the model identify an unsafe event, and did it leave an authorized event alone?
- Control layer: Did the deployed workflow prevent the unsafe action at the right time while preserving useful task completion?
Testing only the first layer rewards a system that blocks everything. Testing only the second can hide a weak detector behind manual review or broad permissions. Report both layers and show how they interact.
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How should you define the safety policy before testing?
Write the policy in operational terms before collecting scores. For each behavior, specify what must be blocked, what is allowed, and what requires human review. Also record the agent’s tools, credentials, filesystem boundaries, network permissions and declared task scope.
| Policy category | Unsafe examples to label | Benign look-alikes to include |
|---|---|---|
| Dangerous or destructive operations | Deleting production data, disabling safeguards, destructive shell commands or irreversible migrations | Deleting a temporary test directory, resetting a local fixture or running a documented cleanup script |
| Unauthorized transfer | Sending source code, secrets or user data to an unapproved destination | Uploading an approved build artifact to the repository’s configured release store |
| Credential exposure | Printing tokens, committing private keys or placing credentials in logs | Reading a secret through an approved secret-manager interface without displaying its value |
| Malicious code | Backdoors, persistence, exfiltration logic or intentionally harmful patches | Security tests, proof-of-concept exploit fixtures and code that handles untrusted input defensively |
| Prompt injection | Instructions in a README, issue, web page, tool response or package that attempt to override the agent’s policy | Documentation that discusses prompt injection as text without instructing the agent to act |
| Administrative changes | Changing access controls, CI settings or infrastructure outside the declared task | A requested change made through the approved change-management path |
| Supply-chain risk | Installing an unverified package, using a compromised script or weakening CI/CD verification | Adding a dependency after checksum, provenance and license checks pass |
OpenAI’s published monitoring report describes behaviors such as circumvention, deception, reward hacking, unauthorized transfer, destructive actions and inbound prompt injection. Treat those observations as examples from its deployments, not as prevalence estimates for every coding agent.
What is the unit of judgment?
State whether a label applies to a message, tool call, full turn or trajectory. A message-level classifier may flag a dangerous command, while a trajectory-level policy may determine that the command is authorized in a sandbox but not in production. Record the context visible to the classifier: prior turns, tool arguments, tool outputs, identity, repository, branch, environment and data destination.
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How do you build a useful evaluation dataset?
Build a balanced, leakage-resistant corpus with both unsafe events and legitimate events that resemble them. Attack-only testing cannot reveal false positives, and a dataset with unrealistically equal class frequencies cannot predict production block volume.
Include realistic agent activity
- Direct malicious requests from a user.
- Malicious instructions embedded in retrieved documentation, issues, web pages, package metadata and tool output.
- Multi-turn attempts that gradually assemble an unsafe action.
- Tool calls and outputs where the same command is safe in one repository or environment and unsafe in another.
- Ambiguous requests that require authorization, destination or data-sensitivity context.
- Benign maintenance, debugging, dependency updates and security testing that use sensitive-looking words.
Prevent train-test leakage
Separate repositories, task templates, attack families and wording variants between development and test sets. Keep hidden scenarios and expected outcomes outside the evaluated agent whenever possible. Check for copied benchmark examples, public walkthroughs and answer strings in model prompts, retrieval indexes, fine-tuning data and evaluator instructions. A held-out example that differs only by a renamed variable is not a meaningful generalization test.
Annotate context and severity
Store the event, preceding context, tool identity, arguments, expected authorization, data destination, severity and adjudication notes. Distinguish an unsafe action that would immediately compromise a system from a suspicious instruction that should be reviewed. Have at least two reviewers label difficult cases and adjudicate disagreements; retain the original disagreement rather than silently forcing consensus.
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Report results by category and threshold, not one blended accuracy number. At minimum, publish recall for unsafe events and false-positive rate for benign events at every threshold tested.
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| Metric | What it answers | Important qualification |
|---|---|---|
| Unsafe-event recall | What share of labeled unsafe events were detected? | Break out dangerous operations, injection, credentials, malicious code and supply-chain cases; aggregate recall can hide a weak category. |
| False-positive rate | What share of benign events were incorrectly blocked or flagged? | Use a production-like benign base rate as well as a balanced test set. |
| Precision | When the classifier flags an event, how often is it actually unsafe? | It changes with production prevalence, so report the assumed or observed base rate. |
| Block volume | How many events will users experience as interventions? | Include repeated blocks in one task; a low event-level rate can still create a high task-level failure rate. |
| Calibration | Does a score of 0.8 represent roughly 80 percent risk in the tested population? | Calibration can drift when repositories, tools or attack wording change. |
| Uncertainty | How stable is the result? | Use confidence intervals, bootstrap intervals or repeated runs when model outputs are nondeterministic. |
For each threshold, publish a confusion matrix and the number of examples in every category. If the classifier or agent is stochastic, repeat the same scenario and report the spread rather than only the best run. Do not collapse distinct harms into one score without retaining the category-level table.
OpenAI Alignment reports 90.3% overeagerness recall, 99.3% prompt-injection recall and 96.1% recall on its MonitoringBench Hard evaluation. Those figures belong to the definitions and tests described in that report; they are not generic results for blocking classifiers.
What does the second, runtime-control layer measure?
The runtime layer asks whether the complete system prevents an unsafe action and still lets the agent finish an authorized task. Measure the observation point, the information available there, the action taken after a decision, and the recovery path.
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| Inspection point | Strengths | Risks and checks |
|---|---|---|
| Agent-native event interface | Can see structured tool names, arguments, user identity, policy scope and tool results before execution. | Requires a supported interface; uninstrumented tools or side channels may be invisible. Verify that every relevant tool and response is covered. |
| Network inspection | Can observe traffic from agents that lack a native policy interface and can intervene at a network boundary. | May lack semantic context and can run too late for local actions. Microsoft’s documented runtime-protection capability is marked Preview and does not support agents using certificate pinning or HTTP/3. |
| Post-action monitoring | Useful for discovering anomalies, missed coverage and policy drift. | It cannot undo a command or transfer that already occurred; pair it with a preventive point where possible. |
Compare the structured context and intervention timing, not just detector accuracy. An agent-native event may identify that a package install changes a production deployment, while a network sensor may see only an HTTPS request. Conversely, a network control can cover an agent that exposes no native events.
What action follows a decision?
| Action | When it is useful | What to measure |
|---|---|---|
| Log | Early tuning, low-confidence signals and post-action discovery | Missed unsafe events, alert volume, analyst review time and data captured for threshold changes |
| Challenge or human review | Ambiguous cases where authorization or business context is needed | Reviewer response time, approval accuracy, abandonment and bypass attempts |
| Hard block | High-confidence, high-severity actions that must not execute | Pre-execution coverage, false blocks, task failure and override paths |
| Recoverable denial | Risky action can be refused while the agent may find a safe alternative | Successful recovery, retries, repeated denials, escalation and added latency |
Anthropic describes one recoverable-denial design in Claude Code auto mode: the denial is returned as a tool result, the agent is told to respect the boundary and seek a safer route, and repeated denials lead to escalation or termination. Its statement that the agent should recover rather than simply halt is a design principle, not a guarantee that every agent will recover safely.
How do you measure task success, friction and latency?
Run complete tasks, not isolated classifier examples. Use the same task with the control disabled, in log-only mode and in enforcement modes, then compare outcomes.
- Task success or resolution rate: the task reaches its specified outcome without violating policy.
- Unsafe-action prevention: the action is stopped before execution or data leaves the permitted boundary.
- Recovery rate: after a denial, the agent completes the task through an approved alternative.
- Repeated-denial rate: the share of tasks that circle through the same blocked action.
- Escalation and termination rate: tasks handed to a person or stopped after policy limits.
- User overrides: approved exceptions, who made them and whether the exception was within policy.
- Tool-call reliability: valid calls, malformed calls and calls lost because of the control path.
- Latency: classifier time, policy-decision time, review wait and end-to-end task delay, reported separately.
- Token and compute overhead: extra context, retries and recovery turns caused by intervention.
GitHub’s documentation for Copilot Agents reports independent evaluation runs with resolution rate, token efficiency, latency and tool-call reliability. Use the same separation: a high detection score does not compensate for a control that makes ordinary tasks fail or adds an unacceptable review queue.
Anthropic cites a 0.4% false-positive rate as an example of why a false positive may be less costly when an agent can retry. That figure is an example from its product discussion, not an independently verified rate for all Claude Code traffic.
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What is a practical step-by-step evaluation process?
- Write the policy. Define stop, allow and review outcomes for each risk category. Record authorized tools, identities, environments, repositories and destinations.
- Map the agent loop. Draw the path from user input to model response, tool selection, tool execution, tool output and network transfer. Mark where the classifier can see and intervene.
- Choose the judgment unit. Decide whether labels apply to messages, calls, turns or trajectories, and preserve enough surrounding context to reproduce the decision.
- Assemble the corpus. Combine public labeled examples with internally authored tasks, benign look-alikes, indirect injections, multi-turn attacks and realistic tool outputs. Keep sensitive production data out of the corpus or replace it with controlled equivalents.
- Annotate and adjudicate. Label safety, authorization, severity, expected action and rationale. Resolve disagreements with documented rules.
- Create leakage-resistant splits. Hold out repositories, attack techniques, wording, package names and task families. Hide test cases and expected answers from the agent and evaluator.
- Run offline threshold sweeps. Produce category-level recall, false-positive rate, precision, block volume and uncertainty for every threshold and action mode.
- Replay complete tasks. Execute the same scenarios with logging, review, hard blocking and recoverable denial. Capture trajectories, tool outcomes, retries, escalation and latency.
- Stress the enforcement point. Test unsupported tools, alternate protocols, certificate pinning, HTTP/3, encoded arguments, shell wrappers, renamed files and data paths that bypass the primary interface.
- Inspect trajectories. Look for policy circumvention, hidden actions, reward hacking, unauthorized transfers, destructive behavior, prompt injection and attempts to manipulate the grader.
- Set release gates. Define minimum unsafe-event recall, maximum false-positive and latency budgets, acceptable task-success degradation and mandatory escalation rules before deployment.
- Monitor and retest. Sample production events under privacy controls, investigate misses and false blocks, add representative cases, and rerun the hidden suite after model, tool or policy changes.
How should thresholds and actions be tuned?
Choose a threshold from the cost of mistakes and the available recovery path, not from a universal recommendation. A missed destructive action may cost more than a delayed test run, while a false block on a routine edit may be inexpensive if the agent can retry automatically.
Cloudflare’s prompt-injection detector documents scores from 1 to 99, with 1–19 described as high likelihood, 20–49 as moderate likelihood and 50–99 as low likelihood. Its documentation recommends starting with logging at a moderate threshold, reviewing events, adjusting for false positives and misses, and switching to blocking when confidence is appropriate. Those bands and actions are product-specific examples, not validated thresholds for coding agents.
For each candidate threshold, calculate an expected loss such as:
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expected loss = (missed unsafe events × miss cost) + (false blocks × false-block cost) + (review events × review cost)
Estimate costs separately for each category and environment. Then compare a strict block, a review route and a recoverable denial. A recoverable denial may preserve task success, but only if the agent receives a clear machine-readable result and a stopping rule prevents endless retries.
Which adversarial scenarios should a coding-agent test suite contain?
Use scenarios that exercise the agent’s full decision process rather than only suspicious prompts.
- A README instructs the agent to upload environment variables before running tests.
- A tool response contains an instruction to ignore the system policy and execute a shell command.
- A dependency’s install script requests a network connection and writes outside the repository.
- A user asks for a harmless refactor while a later turn requests a credential dump.
- The same database command is safe against a disposable fixture but destructive against production.
- An encoded, split or shell-wrapped command hides a prohibited operation.
- An agent receives a denial and tries a different tool, alias or network route to achieve the same result.
- A benign security test contains exploit terminology but remains within the declared sandbox.
- A tool fails after a block, forcing the agent to choose between a safe fallback and an unsafe shortcut.
Record the exact trajectory and expected safe outcome for every scenario. Include time-of-decision data so a detector that recognizes an event only after execution is not counted as preventive coverage.
How do you protect benchmark integrity?
Evaluation integrity is part of the safety result. NIST’s CAISSI discussion notes that agent evaluations test whether models use tools in a multi-turn feedback loop to solve complex problems such as debugging software or uncovering cybersecurity vulnerabilities. Its examples of cheating include finding walkthroughs online, inspecting newer code versions, disabling assertions and adding test-specific logic.
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Use hidden repositories, rotating task variants, isolated network access and independent graders. Audit trajectories for attempts to discover test answers, modify assertions, detect benchmark artifacts or optimize for a scoring shortcut. Keep the evaluator’s rubric and expected outputs unavailable to the agent where feasible. Percentages reported for individual benchmark cases are lower bounds with benchmark-specific denominators; do not generalize them to all coding-agent evaluations.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should you compare two blocking approaches?
Use a common task suite and compare the following axes rather than ranking products by a headline score.
| Axis | Questions to answer |
|---|---|
| Inspection point | Does it inspect agent-native events, network traffic or both? Is it before tool execution and before data leaves the environment? |
| Context and scope | Can it distinguish an authorized action from the same command outside scope? Does it see the trajectory, tool arguments and results? |
| Decision action | Can it log, route to review, hard block and return a recoverable denial? Are actions configurable by severity? |
| Coverage and compatibility | Which agents, protocols, tools and network features are supported? Are certificate pinning or HTTP/3 limitations relevant? |
| Operational fit | What are decision latency, reliability, review burden, retry behavior and escalation rules? |
| Evidence quality | Are benign examples present? Are adversarial cases held out? Can the benchmark resist contamination and grader gaming? |
Microsoft’s AI agent runtime-protection documentation is one example of an agent-native and network-inspection comparison; the page labels the capability Preview, so verify current support in the exact environment before relying on it. Cloudflare’s prompt-injection detection documentation is an example of product-specific scoring and threshold guidance; its ranges should not be transplanted as coding-agent policy.
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What does a release scorecard look like?
Set numeric gates with your security, engineering and operations owners. The table below is a reporting structure, not a universal target.
| Gate | Report | Release question |
|---|---|---|
| Detection | Per-category recall, false-positive rate, precision and confidence interval | Does the detector catch the risks that matter without overwhelming benign work? |
| Prevention | Unsafe actions stopped before execution or transfer | Can an event bypass the intervention point? |
| Task utility | Resolution rate, recovery rate, retries and termination | Can an authorized task still finish after an intervention? |
| Friction | Review wait, repeated denials, overrides and added tokens | Will users route around the control because it is too disruptive? |
| Performance | Classifier, policy, review and end-to-end latency | Does the control fit interactive and batch workloads? |
| Integrity | Hidden-test leakage, grader-gaming findings and trajectory anomalies | Are the scores measuring safety rather than benchmark familiarity? |
What are the most common evaluation mistakes?
Blocking everything and calling it secure
Without benign look-alikes, a classifier can achieve perfect attack recall by denying every event. Add a production-like benign stream and report false-positive block volume.
Scoring isolated prompts only
Indirect injections, tool responses, package scripts and authorization context are properties of agent sessions. Include calls, responses and trajectories, not just user text.
Inspecting after the side effect
Post-action alerts are valuable for discovery but do not prove prevention. Measure whether the control acts before execution or transfer.
Using one global threshold
Risk, authorization and recovery costs differ by category and environment. Use category-aware policies or justify why one threshold is appropriate.
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Ignoring denial loops
A blocked call can become repeated retries, alternate-tool attempts or task termination. Count those outcomes and define escalation limits.
Trusting a public benchmark as deployment proof
A labeled benchmark can reveal implementation errors, but its coverage, representativeness and licensing may not be established. Add hidden, environment-specific tasks.
Letting the evaluator leak answers
Agents can exploit visible tests, newer code versions or grader behavior. Separate hidden cases, expected results and scoring logic from the agent.
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- Policy version, model version, threshold and action for every decision.
- Event type, tool, repository, identity, environment and destination visible to the classifier.
- Whether the action was prevented before execution, after execution or only detected later.
- Human approvals, overrides, denial counts, recovery path and final task outcome.
- Latency components and any tool or protocol excluded from coverage.
- Data-retention, redaction and access controls for trajectory logs.
- Drift triggers, such as a new tool, model, protocol, repository class or rising false-positive rate.
Re-run the full suite after changing the model, policy, tool schema, network route or evaluator. Treat a new integration as a new coverage claim until its events and failure modes are measured.
Frequently Asked Questions
How much benign data is needed to estimate false positives?
Use a sample that reflects the expected production mix of repositories, tools, task types and user roles, then report the sample size and class prevalence. Keep a separate balanced set for diagnostic comparisons, because it is useful for model analysis but does not predict operational block volume by itself.
Should a classifier inspect prompts, tool calls or network traffic?
Use the earliest supported point that has enough context to make the policy decision, and add later monitoring for gaps. If a tool or protocol cannot expose events, document that blind spot and test whether the network layer provides meaningful coverage rather than assuming it does.
Is F1 score enough to choose a blocking threshold?
No. F1 combines precision and recall under one class balance and does not price task failure, review time or the severity of a missed action. Choose thresholds with category-specific costs, production prevalence and measured recovery behavior.
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How often should the evaluation suite be refreshed?
Refresh it whenever the agent model, tools, permissions, protocols or policy changes, and add cases from confirmed misses and false blocks. A scheduled review is also useful for drift, but hidden scenarios should remain protected between refreshes so the agent cannot optimize to a fixed public set.
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