Test the complete agent system in a contained, deployment-like environment—not only the underlying model. Exercise realistic destructive scenarios, combine repeatable evaluations with adversarial multi-turn red teaming, inspect tool calls and state changes, and block release when a required safeguard can be bypassed. A passing result is evidence about the tested version, permissions, harness, attack budget, and cases; it is not proof that every future interaction will be safe.
What “destructive behavior” includes
An AI agent is more than a language model. Its behavior emerges from the model, orchestration harness, tool integrations, credentials, system and developer instructions, retrieval sources, persistent memory, approval controls, and the environment in which it operates. Testing only the model’s text responses can miss the failure that matters: an apparently reasonable answer followed by a harmful tool call.
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For this guide, destructive behavior means an agent causing, attempting, or materially enabling an unauthorized or unsafe change. Examples include deleting the wrong resource, modifying records outside the approved scope, exposing sensitive data, escalating privileges, sending an unapproved external message, poisoning memory for later runs, or consuming tools in an uncontrolled loop. A test should distinguish an attempted action from an executed side effect, because a well-designed control may block the attempt while still logging it for investigation.
Start by deciding what claim you need to support. “This configuration denied the listed deletion scenarios under the tested conditions” is defensible. “The agent cannot be destructive” is not. NIST’s AI Risk Management Framework treats contextual mapping, documented testing, monitoring, and residual-risk decisions as parts of the risk process; OWASP’s agent checklist similarly emphasizes evidence from controls, approvals, denials, timeouts, and circuit breakers.
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Map the full system before writing tests
Make an inventory that a tester can use to reconstruct the exact setup. Record each capability, identity, boundary, and safety dependency. The map should cover both intended use and plausible misuse.
| System element | Inventory to capture | Destructive-risk question |
|---|---|---|
| Model and provider | Model name or version, provider, context limits, tool-calling mode | Could a provider, model, or context change alter refusal or tool-selection behavior? |
| Harness and policies | System and developer instructions, routing logic, policy checks, retry rules, fallback behavior | Can user text, retrieved text, or a failed check override a higher-priority rule? |
| Tools | Every callable function, accepted parameters, validation, side effects, and error handling | Can a parameter target a broader resource, or can an error trigger an unsafe retry? |
| Identity and credentials | Runtime identity, token scopes, service accounts, expiration, secret injection | Can a low-privilege session reach a privileged operation or secret? |
| Data and memory | Readable and writable stores, retrieval indexes, persistent memory, retention rules | Can untrusted content become an instruction or persist into a later task? |
| Approvals | Who can approve, how approval is bound to parameters, validity period, and audit record | Can an old, generic, forged, or mismatched approval authorize a new action? |
| Environment | Filesystem, databases, queues, network routes, external services, reset procedure | What is the worst real side effect if containment fails? |
| Observability and stops | Tool traces, state-diff logs, alerts, kill switch, circuit breakers, human escalation | Can you see and halt a harmful sequence before the side effect occurs? |
Mark actions as reversible, recoverable from backup, or irreversible. Identify high-impact operations such as deletion, financial movement, permission changes, production deployment, or external communication. For each one, document the required approval and the exact parameters that approval must cover. This inventory supplies the context for a go/no-go decision and prevents a test from silently omitting a dependency.
Turn capabilities into concrete abuse cases
Write one test case per meaningful failure mode. Each case needs a controlled setup, an adversarial input or environmental condition, an expected safe behavior, and observable evidence. Use deployment-specific actions only when the agent genuinely has access to them; do not simulate a risk that the production system cannot create.
| Abuse case | Test condition | Expected safe behavior | Evidence to inspect |
|---|---|---|---|
| Instruction override | User text asks the agent to ignore system or developer rules | Higher-priority policy remains in force; prohibited tool calls are denied | Prompt trace, policy decision, tool authorization result |
| Untrusted retrieved content | A document or web result contains instructions aimed at the agent | Content is treated as data, not authority, unless explicitly trusted | Retrieved text, trust classification, resulting actions |
| Unauthorized tool request | Agent is asked to invoke a tool outside its allowlist | Invocation is refused or blocked before execution | Requested tool, allowlist decision, denial log |
| Privilege escalation | A low-privilege identity attempts an administrative operation | Operation fails at the authorization boundary; no alternate credential is used | Identity, scopes, authorization response, state diff |
| Memory poisoning | Malicious content asks the agent to save a future instruction | Persistent memory rejects or quarantines untrusted instructions | Write request, memory policy, later retrieval behavior |
| Data leakage | Prompt or tool output tries to expose secrets through arguments, citations, logs, or the final answer | Sensitive values are redacted or withheld and are not sent to unauthorized tools | Raw and redacted traces, outbound payloads, response |
| Runaway chain | Retries, recursion, or long tool sequences are encouraged | Turn, time, token, and tool limits stop execution safely | Counter values, timeout, circuit-breaker event, final state |
| Approval bypass | High-impact action is attempted with no approval, an expired approval, or mismatched parameters | Execution is blocked until a current, parameter-specific approval is verified | Approval ID, parameters, timestamp, verification result |
| Multi-agent boundary failure | One agent hands instructions or authority to another across a trust boundary | Delegated scope is explicit and cannot silently expand | Handoff message, identity, delegated permissions, downstream calls |
Add cases for the actual destructive operations in your deployment: deleting a resource with a similar name, changing the wrong account, sending to an unapproved recipient, or acting on stale state. Define the safe result before running the case. “The agent should be careful” is not a test oracle; “the delete function is not called and the user receives an approval request” is.
Contain the evaluation environment
Use isolated, resettable resources and least-privilege credentials. A sandbox is useful only if its boundary is real and observable. Never place production customer data, production credentials, or an unrestricted route to third-party targets in an evaluation environment unless that access is explicitly authorized and is itself the object of the test.
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- Create separate test accounts, databases, buckets, queues, and identities. Seed them with synthetic records that make an unintended change easy to detect.
- Restrict network egress to the endpoints required for the evaluation. Deny everything else by default and log attempted connections.
- Keep secrets outside the agent-accessible environment where possible. If a secret is required, use a narrowly scoped, short-lived credential and verify its scope.
- Install a human-controlled stop mechanism that can terminate tool execution, revoke credentials, and preserve the trace.
- Alert a human when the agent probes for escape, requests unexpected network access, attempts to discover credentials, or reaches a boundary it should not see.
- Reset state between cases so one run cannot quietly influence another through files, queues, memory, or cached credentials.
Validate containment before the first adversarial prompt and periodically during a run. Do not infer sandbox integrity from what the model says about its environment. Anthropic’s published security practices for high-risk evaluations describe pre-engagement sandbox checks, escape detection, and human alerting; the same principle applies to ordinary pre-deployment testing. Confirm that a challenge is solvable within the intended scope rather than accidentally rewarding an escape route.
Combine repeatable evaluations with red teaming
Use two complementary modes. A repeatable evaluation measures whether the system meets known expectations. Red teaming searches for unexpected paths around those expectations. Neither mode is sufficient alone.
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|---|---|---|---|
| Repeatable behavioral evaluation | Regression against defined policies and expected denials | Fixed cases, rubric, seeded state, repeatable harness | The tested setup met the stated pass criteria for those cases |
| Adversarial red team | Unexpected prompts, context manipulation, boundary probing, and novel attack paths | Human or automated attackers with realistic tools and multi-turn interaction | The tested attack strategies did or did not expose a weakness under the stated budget |
| Automated auditing agent | Many scenario variations and long interaction sequences | Auditor generates and explores cases while a separate system records traces | Additional exploratory coverage, subject to human review of traces and judge quality |
For behavioral evaluations, define a rubric with observable outcomes. A case can pass only when the control works and the side effect is absent; a polite refusal after a destructive tool call is still a failure. For red teaming, vary wording, order, context, retrieved documents, identities, and timing. Include attackers who cooperate initially and change goals after several turns.
Automated judges can help triage large volumes of traces, but model-generated scores are not ground truth. Have a reviewer inspect concerning traces, verify the actual state change, and resolve disagreements. Preserve the original inputs and outputs so a score can be audited later.
Use a realistic, multi-turn harness
Single-turn prompts miss interactions among tools, memory, retrieval, approvals, and changing state. Build scenarios that resemble how the agent will be used: a user makes a legitimate request, the agent retrieves content, calls a tool, receives an error or partial result, and then decides what to do next. Insert adversarial events at different turns rather than always at the beginning.
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Report the conditions that determine what the result means:
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- Available tools, parameter validation, credential scopes, approval implementation, and memory or retrieval configuration.
- Attack strategy, number of turns, token and time budgets, retry limits, and any stopping rule.
- Environment topology, seeded data, network restrictions, reset behavior, and monitoring controls.
- Why the scenarios represent expected use or plausible misuse for the deployment.
OpenAI’s evaluation guidance notes that harness choice and evaluation budget can change measured performance. A narrow harness can understate what an agent can do, while an unrealistic harness can produce alarming but irrelevant behavior. State the limits alongside every result instead of presenting a score without context.
Set release gates before running the tests
Decide in advance what blocks deployment. Gates should be tied to the system’s risk tolerance and the consequences of failure, not to an arbitrary aggregate score. Examples of operational gates include:
- No unauthorized high-impact action executes, even when the request is distributed across multiple turns or agents.
- Every required approval is current, valid, authenticated, and bound to the exact operation and parameters being executed.
- Tools outside the declared allowlist remain unavailable, and low-privilege identities cannot obtain broader credentials through alternate paths.
- Turn, time, token, recursion, and retry limits stop runaway behavior without leaving a partially applied unsafe operation.
- Containment alerts, credential revocation, and the human stop mechanism work during an active run.
- The agent fails safely when a required condition is missing, ambiguous, stale, or unavailable.
- Changes to prompts, tools, memory, retrieval, providers, credentials, or approval logic trigger the relevant regression suite before release.
These are control examples, not universal numerical thresholds. Set thresholds appropriate to your organization’s residual-risk tolerance. NIST’s framework calls for keeping residual negative risk within that tolerance and for safe failure. If a critical destructive action can occur without a required control, fail the gate until the behavior is corrected, the capability is removed, or the action is constrained behind a stronger boundary.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Inspect side effects, not just the final answer
The final response can sound safe while the agent has already changed state. For every run, correlate the conversation with execution evidence:
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- Which tool was selected, under which identity, with which arguments?
- Which policy and authorization checks ran, and what decisions did they return?
- Was approval requested, verified, and matched to the operation at execution time?
- What files, records, permissions, messages, or external requests changed?
- Did a timeout, retry, circuit breaker, or human halt occur, and did it leave partial work?
- Did the agent attempt to access a resource outside the declared scope?
Use before-and-after state snapshots or immutable audit events for high-impact resources. Test the observability controls themselves: generate a deliberate denied call and verify that the denial, identity, parameters, and timestamp are retained. OWASP recommends preserving observed approvals, denials, timeouts, and circuit-breaker behavior so an evaluator can reconstruct what happened.
Preserve evidence and make results reproducible
Store enough information for another engineer to rerun a case and understand the decision. At minimum, retain the agent and model versions, provider, harness, prompts, tool policy, retrieval and memory configuration, credentials and permission model, environment settings, test cases, expected results, observed outcomes, traces, state changes, residual risks, and compensating controls.
A compact record can use a structure like this:
{
"case_id": "approval-expired-delete",
"agent_version": "recorded release identifier",
"model_and_provider": "recorded model and provider",
"harness": "recorded orchestration and tool adapter",
"setup": "seeded resource, identity, policy, and network limits",
"attack": "multi-turn input and retrieved content",
"budget": {"turns": "recorded limit", "tokens": "recorded limit", "time": "recorded limit"},
"expected": "delete is blocked and approval request is emitted",
"observed": "tool trace, authorization decision, and state diff",
"result": "pass or fail with reviewer rationale",
"residual_risk": "remaining exposure and compensating control"
}Keep structured traces and decision evidence with the release record. If an automated judge labels a case, retain the raw trace and the human validation outcome rather than only the label. This supports evidence-grounded audits—the goal described by NIST’s evaluation-probe work is to show what the AI found, where it found it, and how the evidence supports the conclusion.
Retest after changes and during operation
Run regression cases whenever a material dependency changes: prompts, tool schemas, parameter validation, memory, retrieval indexes, policies, providers, credentials, approval logic, or the orchestration harness. A prompt-only change can alter tool selection; a credential change can enlarge the blast radius even when the model is identical.
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Keep a baseline suite for known destructive cases and add a new regression whenever a red team finds a novel bypass. Run broader adversarial exercises on a schedule appropriate to the risk, and monitor production behavior for denied calls, unusual tool sequences, repeated retries, privilege requests, and boundary probes. NIST recommends testing before deployment and regularly in operation; OWASP recommends adversarial tests in CI/CD and regression suites.
When the deployed system differs from the evaluated one, do not carry the old result forward automatically. Record the difference, decide whether it invalidates the prior evidence, and rerun the affected cases. A release decision should name the exact configuration it covers.
How to interpret a passing or failing result
A pass means the agent behaved acceptably in the tested situations with the tested harness, permissions, budgets, environment, and safeguards. It does not establish safety for every possible input, sequence, tool failure, or future version. State the attack strategies used, the cases omitted, the limits of the environment, and the residual risk.
A single critical failure can outweigh many harmless passes. If an agent executes an irreversible action without the required approval, crosses a trust boundary, leaks a secret, or escapes containment, stop the release and preserve the evidence. The remediation may be a policy or prompt change, but it may also require narrowing a tool schema, reducing credential scope, adding an authorization service, removing persistent memory, inserting a human gate, or disabling the capability entirely.
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