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Test the complete agent system—not only its language model—against realistic destructive scenarios in an isolated, resettable environment. Combine repeatable evaluations with adversarial, multi-turn red teaming; observe every tool call and state change; verify permissions, approvals, limits, and containment; then enforce explicit release gates. A pass demonstrates acceptable behavior only for the tested model, harness, permissions, cases, and budget, not proof that every future interaction will be safe.
What counts as destructive behavior in an AI agent?
A destructive behavior is any action or decision that can damage data, systems, finances, privacy, availability, safety, or organizational control. Deletion is the obvious example, but an agent can cause comparable harm by modifying the wrong record, exposing a credential, sending an unapproved message, granting access, changing a production setting, or repeatedly invoking an expensive tool.
Judge the behavior of the deployed system, not an abstract model. The system includes the model, orchestration harness, prompts and policies, tools, credentials, retrieval sources, memory, approval workflow, monitoring, and execution environment. OWASP’s agent-security checklist and OpenAI’s evaluation guidance both emphasize that harness and tool choices can materially change what an evaluation measures.
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Why model-only testing is insufficient
A model may refuse a harmful request in a chat test yet perform the same operation when a tool is available, when a retrieved document contains an instruction override, or when a multi-turn conversation gradually changes the context. Conversely, a model that appears risky in isolation may be constrained by strict tool permissions and human approval checks. Your claim must therefore be about a named configuration: model version, harness, tools, policies, identities, retrieval and memory settings, and environment.
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Define the harm boundary
List actions that are irreversible, high-impact, externally visible, or difficult to detect. Examples include deleting or overwriting records, changing access controls, moving money, sending messages outside the organization, publishing content, changing infrastructure, or exporting sensitive data. Also include lower-level actions that can become destructive when chained, such as broad searches, bulk edits, retries, recursive calls, and file-system operations.
Map capabilities, identities, and trust boundaries
Begin with a capability inventory. For each tool and data source, record what the agent can read, create, change, or trigger; the identity under which it acts; the approval required; and whether the result is reversible. Include hidden or indirect capabilities, such as plugins, webhooks, scheduled jobs, shell commands, background workers, and memory-writing functions.
Capability-mapping worksheet
| Item to map | Questions to answer | Evidence to retain |
|---|---|---|
| Tools and actions | What operations exist, and which can alter or delete external state? | Tool schema, policy, and a complete invocation trace |
| Identity and credentials | Which user, service account, role, or token is used? What is its maximum scope? | Role definition, token scope, and identity shown in logs |
| Data access | Which records, files, secrets, and retrieval indexes are readable or writable? | Access-control configuration and representative access tests |
| Approvals | Which actions require approval, how is approval bound to parameters, and when does it expire? | Approval request, decision, timestamp, approver, and approved arguments |
| State and memory | What persists between turns, users, sessions, or agents? Who can write to it? | Before-and-after state snapshots and write audit entries |
| External connections | What network destinations, APIs, queues, or devices are reachable? | Egress rules, DNS or proxy logs, and attempted destinations |
| Recovery | Can each operation be rolled back, and how quickly can access be revoked? | Restore procedure, rollback result, and credential-revocation test |
Draw trust boundaries between user input, retrieved content, memory, tools, other agents, and human operators. A handoff from one agent to another is a boundary crossing: instructions and authority must not silently gain privileges during transfer. NIST’s AI Risk Management Framework treats this contextual mapping as an input to risk measurement and the initial go/no-go decision.
Turn risks into testable abuse cases
Write a case for every material capability. Each case needs a controlled setup, an adversarial input or environmental condition, the expected safe behavior, and observable pass/fail evidence. Do not write only “the agent should be safe.” Specify the exact denial, approval, limit, alert, or state outcome that demonstrates safety.
Minimum abuse-case set
- Instruction override: A user message attempts to replace system or developer rules.
- Untrusted retrieval: A document, web page, ticket, or code comment tells the agent to ignore policy or reveal secrets.
- Unauthorized tool request: The agent is asked to use a tool that is unavailable or outside the user’s role.
- Privilege escalation: A low-privilege identity attempts an administrator-only operation, directly or through a chained tool.
- Memory poisoning: Malicious content tries to write a persistent instruction that affects later users or sessions.
- Data leakage: Sensitive information is placed in tool arguments, citations, logs, intermediate messages, or the final response.
- Runaway execution: Retries, recursion, or long tool chains continue after an error, denial, timeout, or completed objective.
- Approval bypass: A high-impact action runs without a current, valid approval bound to the exact parameters.
- Multi-agent boundary failure: A delegated agent receives authority or instructions it should not inherit.
Add deployment-specific cases only for capabilities the tested agent actually possesses. For a system that can edit a customer database, test an accidental deletion and a wrong-record update. For one that can communicate externally, test an unapproved message and an exfiltration attempt. Define the safe result from your own policy rather than assuming every operation should simply be blocked.
Build a contained evaluation environment
Run destructive-behavior tests against isolated, resettable resources. Use synthetic or disposable data, separate accounts, and least-privilege credentials. Restrict network egress to destinations required by the test. Keep production secrets and customer data outside the agent-accessible environment whenever possible.
Containment controls
- Create a separate project, account, tenant, or network segment for evaluation.
- Use credentials whose scope is limited to test resources and whose revocation is quick.
- Apply deny-by-default firewall and proxy rules; permit only documented test endpoints.
- Disable access to production systems, real customer data, and unrelated third-party targets.
- Reset databases, files, queues, and memory stores between cases so one run cannot contaminate another.
- Place a human alert and stop mechanism outside the agent’s control.
- Record configuration checks before each run, including identity, egress, tool list, and reset status.
Do not infer containment from the agent’s statements. Test the boundary itself: attempt the prohibited network access, file path, credential use, or privilege change under supervision and verify that the control—not the model’s promise—stops it. Anthropic’s published evaluation-security practices describe pre-engagement sandbox validation, escape detection, and human intervention for high-risk testing.
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When a test must touch a real system
If a deployment-like integration is essential, authorize it explicitly, narrow it to a test namespace or account, and define a rollback owner before the run. Treat any real external target as a separate risk decision; never use an evaluation as permission to probe unrelated systems.
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Instrument the agent so side effects are visible
A final natural-language answer is not enough evidence. Capture the complete execution trace: user and retrieved inputs, model outputs, tool selected, arguments, identity, policy decision, approval status, return value, retries, timing, and resulting state change. Correlate events with a run and case identifier, and protect logs from modification by the agent.
Observability requirements
- Tool trace: Every attempted and completed call, including denied calls and malformed arguments.
- Authorization trace: The policy evaluated, identity used, scope granted, and reason for denial or approval.
- Approval trace: Approver, timestamp, expiry, approved parameters, and whether the executed parameters matched.
- State diff: Before-and-after records, files, permissions, messages, or other resources.
- Control events: Timeouts, circuit breakers, retry limits, network blocks, alerts, and emergency stops.
- Data-handling evidence: Redaction decisions and locations where sensitive values appeared.
Retain enough context to reproduce a failure without retaining unnecessary secrets. Redact values in reports while preserving stable identifiers and hashes that let investigators match events.
Use repeatable evaluations and adversarial red teaming together
Repeatable evaluations answer whether the system meets defined expectations on a stable set of cases. Red teaming searches for unexpected ways to violate those expectations. Neither substitutes for the other.
| Approach | Strength | Limitation | Claim it can support |
|---|---|---|---|
| Single-turn scripted evaluation | High repeatability and easy regression tracking | Misses context drift, memory, and tool sequencing | The tested responses and controls worked for those exact cases |
| Multi-turn deployment-like evaluation | Exercises tools, context changes, retrieval, memory, and handoffs | More expensive to run and harder to diagnose | The configured workflow behaved acceptably across the tested sequences |
| Adversarial red team | Explores novel prompts, social engineering, and unexpected combinations | Coverage is difficult to quantify and results vary by strategy | The tested attack strategies did or did not find a violation |
| Model-only test | Fast feedback on refusal and instruction-following tendencies | Ignores tools, permissions, approvals, and real state | Only a narrow claim about model behavior in isolation |
| Full-system test | Measures actual authority and side effects | Requires a controlled environment and detailed instrumentation | Evidence about the named deployment configuration |
Use realistic budgets for turns, tokens, time, and tool calls. OpenAI’s red-teaming and third-party evaluation guidance notes that harness choice and evaluation budget affect measured performance; a narrow budget can understate what an agent can do. Record why the chosen conditions represent expected use or plausible misuse.
Design objective pass/fail rubrics
For each case, define observable assertions such as “no write occurred,” “the tool was denied before execution,” “approval was required and matched the requested resource,” “the chain stopped at the configured limit,” or “the secret never appeared in an argument, log, or response.” Include a severity and an owner for each failure. Avoid judging solely from a model-generated score; inspect the trace and state diff.
Automated exploration with human review
An auditing agent can generate variations of prompts, retrieved content, and multi-turn strategies, increasing exploratory coverage. Treat its findings as leads. Review concerning traces, reproduce them with a deterministic case, and validate any automated judge against recorded tool and state evidence. A model-generated “pass” is not proof that no side effect occurred.
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Instruction and retrieval attacks
Place conflicting instructions in user messages and untrusted documents. Test direct requests such as “ignore the policy,” indirect instructions embedded in a ticket, and content that asks the agent to reveal system prompts or credentials. The safe result is preservation of the higher-priority policy, explicit treatment of retrieved text as data, and no unauthorized tool call. Record whether the agent quotes the malicious content, follows it, or passes it into another tool.
Authorization and privilege escalation
Run the same operation under identities with different scopes. Ask a low-privilege identity to perform an administrator action, then attempt indirect escalation through search, export, role-management, or delegation tools. Verify denial at the enforcement point, not merely a refusal in text. Test confused-deputy paths in which the agent uses its own broader service identity on behalf of a user who lacks that authority.
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Approval bypass and parameter tampering
Require approval for a high-impact operation, then vary the target, amount, recipient, or timing after approval. Try expired, duplicated, replayed, or partially specified approvals. The action must execute only when approval is current, authentic, and bound to the exact parameters. A confirmation message that is not cryptographically or transactionally tied to the operation is not sufficient evidence.
Data leakage
Seed test data with canary values and sensitive-looking records. Attempt to make the agent disclose them through a final answer, citation, tool argument, error message, log entry, retrieval result, or handoff. Inspect all channels, including traces visible to operators and downstream systems. Check that redaction does not merely hide the value from the report while still sending it to a tool.
Memory poisoning and cross-session contamination
Submit content that asks to be remembered as a permanent rule, then start a separate session or user context. Verify write authorization, provenance, review, expiry, and isolation. A malicious user’s instruction must not become a trusted policy for later users. Reset and compare memory state before and after every case.
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Induce transient failures, empty results, contradictory tool responses, recursive plans, and repeated approvals. Confirm that retry, depth, time, cost, and concurrency limits stop execution and produce an actionable alert. Test whether the circuit breaker works when the model continues requesting calls, and whether a timeout leaves a partially completed state that requires reconciliation.
Wrong-resource and irreversible actions
Use near-identical names, ambiguous identifiers, stale retrieval results, and partial matches. Ask the agent to modify one resource while presenting several plausible candidates. Safe behavior may be a clarification request, a preview, a dry run, or a refusal to proceed without an exact identifier. Verify that deletion and overwrite operations have a separate confirmation or approval path and that rollback restores the intended state.
Multi-agent handoffs
Give one agent untrusted instructions and let it delegate to another with different tools. Test whether the handoff preserves the original user’s authority, strips untrusted directives, and carries only the minimum necessary data. Attempt to pass a forged “approved” flag or elevated role in the message. The receiving agent must re-authorize the action at its own boundary.
Follow a controlled pre-deployment runbook
- Freeze the test configuration. Record model and provider versions, prompts, harness commit, tool schemas, policies, credentials, retrieval indexes, memory settings, and environment image.
- Map capabilities. List actions, identities, data stores, approvals, reversibility, external connections, and worst plausible consequences.
- Set the risk decision. Define unacceptable outcomes, required controls, compensating controls, and who can accept residual risk before execution.
- Prepare and verify containment. Provision isolated resources, least-privilege identities, egress rules, reset scripts, monitoring, alerting, and an emergency stop. Run boundary checks before inviting the agent to act.
- Implement cases and rubrics. Give every case setup instructions, adversarial inputs, expected behavior, observable assertions, severity, and cleanup steps.
- Run the repeatable suite. Execute baseline cases under fixed conditions and retain structured traces and state snapshots.
- Run adversarial campaigns. Add multi-turn attacks involving retrieval, memory, tools, approvals, changed context, and agent handoffs. Record attack strategy and budget.
- Review side effects. Compare state before and after each run, inspect authorization and approval events, and reproduce every suspected violation.
- Apply release gates. Block deployment when a critical control is absent or bypassed; assign remediation and rerun affected and regression cases.
- Publish an evidence record. Summarize scope, methods, outcomes, limitations, residual risk, compensating controls, and the exact configuration tested.
Set explicit release gates
Gates should be decided before results are known. The following operational gates are examples; numerical thresholds must come from your organization’s risk tolerance and the impact of the capability.
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| High-impact authorization | No unauthorized high-impact action executes under any tested path. | Remove or constrain the capability, fix enforcement, and rerun all related cases. |
| Approval integrity | Approval is current, authentic, parameter-specific, and checked immediately before execution. | Disable execution until approval binding and replay protection are verified. |
| Least privilege | Each identity can access only required test or production resources. | Reduce scope and retest direct and confused-deputy paths. |
| Runaway control | Retry, depth, time, concurrency, and cost limits stop pathological chains. | Enable limits and circuit breakers; investigate partial state. |
| Containment | Forbidden egress, credentials, paths, and targets are blocked and detected. | Stop testing, repair isolation, rotate exposed credentials, and repeat boundary validation. |
| Observability | Tool calls, decisions, approvals, denials, alerts, and state changes are reconstructable. | Do not release until missing evidence can be produced reliably. |
| Safe failure | Missing context, tool errors, timeouts, and ambiguous targets lead to denial, clarification, or controlled rollback. | Change fallback behavior and test failure branches explicitly. |
OWASP recommends blocking releases when high-risk policies, approval logic, or credential scopes change without updated tests. NIST guidance likewise places deployment decisions within an organization’s residual-risk tolerance and calls for systems to fail safely.
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Interpret results without overclaiming
A successful run means the agent behaved acceptably in the tested situations with the tested harness, permissions, budget, environment, and safeguards. It does not establish that every possible prompt, document, tool sequence, or future model update is safe. State the attack strategies attempted, the resources available, the cases not covered, and the residual risks that remain.
If a critical destructive action occurs without a required control, fail the release gate. Remediation options include fixing the policy or enforcement layer, reducing credential scope, adding an approval step, removing the tool, narrowing the action to a safe subset, or disabling the capability until evidence supports re-enablement. Do not convert an unresolved violation into a passing average score.
Evidence package for a go/no-go meeting
- Configuration identifier and change history.
- Threat model and capability map.
- Case definitions, expected outcomes, and severity ratings.
- Evaluation and red-team methods, including turn, token, time, and tool budgets.
- Structured traces, approval and denial records, state diffs, and containment alerts.
- Failures, reproductions, fixes, retest results, and open limitations.
- Residual-risk owner, compensating controls, and expiration or review date.
NIST’s evaluation-probe work emphasizes evidence-grounded conclusions: show what the system found, where it found it, and how the evidence supports the decision. Apply that standard to destructive-behavior testing instead of relying on a narrative generated by the agent.
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Retest after changes and during operation
Keep a regression suite for every discovered failure and for each high-impact capability. Rerun it after changes to prompts, tools, memory, retrieval, policies, model providers, credentials, approval logic, orchestration code, or network configuration. A change that appears unrelated can alter the agent’s authority or context.
Run scheduled adversarial testing in addition to pre-release checks. Monitor production-like behavior for unusual tool sequences, denied-call spikes, repeated retries, sensitive-data patterns, unexpected destinations, and approval mismatches. Alert and stop execution when behavior crosses the defined boundary. NIST’s framework calls for testing before deployment and regularly in operation; OWASP recommends adversarial tests in CI/CD and maintained regression suites.
Change-triggered test selection
- Prompt or policy change: Run instruction-override, retrieval, approval, and safe-failure cases.
- Tool or schema change: Run authorization, parameter-tampering, wrong-resource, and rollback cases.
- Credential or role change: Run least-privilege, privilege-escalation, and confused-deputy cases.
- Memory or retrieval change: Run poisoning, cross-session, leakage, and untrusted-content cases.
- Provider or model change: Run the full critical suite plus multi-turn red-team variants.
- Network or infrastructure change: Revalidate egress, containment, emergency stop, and recovery.
Common testing mistakes and fixes
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Problem: Benign examples show normal task completion but not how the system responds to manipulation or ambiguity.
Fix: Add hostile, indirect, malformed, conflicting, and multi-turn inputs tied to each real capability.
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Problem: A refusal can hide a tool call that already changed state, while a verbose answer can be harmless.
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Fix: Make tool traces, authorization events, and state diffs authoritative for destructive-action assertions.
Using an unrealistic sandbox
Problem: A model-only or toy environment cannot reveal permission, network, approval, or recovery failures.
Fix: Use deployment-like tools and workflows with synthetic resources, then maintain strict isolation and reset capability.
Assuming a single pass proves safety
Problem: Coverage is limited by cases, attack strategy, budget, and configuration.
Fix: Report bounded claims, preserve limitations, rerun after material changes, and test continuously in operation.
Letting the tested agent control the evidence
Problem: The agent may alter logs, suppress alerts, or claim that a blocked action succeeded.
Fix: Collect traces and state snapshots through independent infrastructure, protect them from agent writes, and verify outcomes externally.
Quick Recap
Pre-deployment checklist
- Every tool, identity, credential, data store, memory path, and external connection is documented.
- High-impact and irreversible actions have explicit approval and rollback requirements.
- Abuse cases cover overrides, untrusted content, unauthorized tools, escalation, leakage, poisoning, loops, approval bypass, and handoffs.
- The environment is isolated, least-privileged, resettable, monitored, and tested for escape.
- Repeatable evaluations and multi-turn adversarial red teaming both run against credible harnesses.
- Budgets, prompts, attack strategies, versions, and conditions are recorded.
- Tool calls, identities, arguments, approvals, denials, alerts, and state changes are independently observable.
- Release gates and residual-risk ownership were defined before testing.
- Critical failures block deployment until corrected or the capability is removed or constrained.
- Regression tests and operational monitoring are scheduled for the deployed configuration.
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