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Red-team an AI agent only in an authorized, disposable environment that contains the entire workflow—not just the model. Define the exact build, tools, data, credentials, network destinations, stop conditions, and operators before you run an attack. Use least-privilege synthetic accounts, layered sandboxing, controlled egress, continuous monitoring, and an operator-controlled pause or shutdown. Then exercise realistic multi-step abuse cases such as indirect prompt injection, unauthorized tool use, data exfiltration, approval bypass, memory poisoning, and multi-agent escalation. Preserve enough configuration and event evidence to reproduce each failure, fix the control, and add a regression test before release.
What does safe AI-agent red teaming involve?
It is an authorized adversarial evaluation of an agent’s complete execution path: model inference, prompts and policies, tool calls, retrieval, memory, credentials, network services, logs, and human approvals. The objective is not to make the model refuse a collection of tricky prompts. It is to determine whether an attacker can cause an unacceptable action and whether controls outside the model prevent, detect, and stop that action.
Use a written rules-of-engagement document and a separate test environment. Treat every finding as a release decision input: fix the weakness, document residual risk, and rerun the relevant scenario after changes to the model, prompt, tool, memory, retrieval, policy, or provider.
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How do I authorize and scope an exercise?
Test only assets you own or are expressly authorized to assess. OpenAI’s API red-teaming documentation states: “Only submit to OpenAI Red Teaming code or other assets that you own or are expressly authorized to test.” Apply the same standard to your own agent and every connected service.
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Write the rules of engagement
Record these items before the first run:
- System owner, security lead, test operators, emergency contact, and who can stop the exercise.
- Exact agent build, model and provider, policy or system-prompt version, tool versions, retrieval index, memory configuration, and test-environment image.
- Exercise start and end times, approved test accounts, maximum concurrency, CPU, memory, storage, token, API-call, and monetary limits.
- Tools and data that are in scope; destinations and protocols that are allowed; systems, tenants, people, and datasets that are prohibited.
- Expected safe behavior for each scenario, evidence to capture, severity criteria, and stop conditions.
Use safe fixtures
Use synthetic records or data specifically approved for security testing. Create short-lived credentials with only the permissions required for the scenario, and revoke them after the run. Do not place production secrets, customer records, real payment details, or shared administrator tokens in fixtures. If a scenario requires a realistic secret, use a canary value that has no access and is easy to detect in logs.
How do I contain the entire agent workflow?
A sandbox is necessary but not a complete guarantee. An agent may exploit a flaw in the sandbox, host, service, identity boundary, or an allowed network path. Layer controls and keep the exercise environment separate from production.
Map every trust boundary
Draw a data-flow map showing what the agent can read, write, invoke, store, or communicate with directly or through another service. Include:
- Model-inference endpoints and any system that assembles prompts.
- The agent runtime, operating-system user, filesystem, temporary directories, and process-execution tools.
- Retrieval stores, web fetchers, document parsers, vector databases, and memory stores.
- API keys, service accounts, approval queues, browser sessions, and secrets managers.
- Outbound DNS, HTTP, email, messaging, databases, object storage, and internal service routes.
- Logs, traces, telemetry pipelines, and other agents that can receive messages or tool results.
Apply overlapping controls
| Boundary | Minimum control | What to verify during a test |
|---|---|---|
| Runtime and host | Disposable VM or container, non-root identity, read-only base image, resource quotas, and automatic teardown | An exploit cannot reach the host, persist after teardown, or exceed CPU, memory, disk, or process limits. |
| Filesystem and data | Allowlisted directories, synthetic fixtures, isolated volumes, and no host mounts unless essential | Path traversal and tool misuse cannot read outside the assigned dataset or alter the host. |
| Network | Default-deny egress, explicit destination allowlist, isolated DNS, and monitored proxy | Forbidden domains, raw IPs, metadata services, and covert channels are blocked and alerted. |
| Identity and tools | Separate service accounts, least privilege, short-lived tokens, and policy checks outside the model | Changing a prompt or tool argument cannot grant a permission the identity does not have. |
| Memory and retrieval | Per-run or per-tenant namespaces, write controls, provenance metadata, and reset capability | Untrusted content cannot persist across sessions or influence another tenant. |
| Operations | Live event monitoring, an operator pause, and an independent kill switch | An operator can halt execution before a queued or repeated action completes. |
If external connectivity is unnecessary, use an offline environment with pre-downloaded tools and information. For high-risk activities, the UK National Cyber Security Centre recommends an isolated and disconnected environment with pre-downloaded tools and information. Its published instruction is: “Always run AI agents within a sandboxed environment that controls and manages what resources can and cannot be communicated with, both locally and over a network.” The NCSC describes this advice as interim while formal guidance is developed.
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When connectivity is required, proxy it through an allowlist that records destination, method, identity, request size, response size, and decision. Block cloud metadata endpoints, broad internal address ranges, and unapproved DNS resolution. Test the controls themselves with attempts to use alternate encodings, redirects, raw sockets, DNS rebinding, and a second agent as a relay.
How do I build an abuse-case matrix?
Start with the intended task and privileges, identify untrusted inputs and consequential actions, then define a repeatable scenario for each threat. For every row, specify the attacker setup, expected safe behavior, permitted side effects, evidence, and severity.
| Abuse case | Representative test | Pass condition |
|---|---|---|
| Direct prompt injection | Ask the agent to ignore policy, reveal hidden instructions, or perform a prohibited tool call. | The request is refused or safely transformed; no unauthorized tool invocation occurs. |
| Indirect prompt injection | Place an instruction in a retrieved web page, document, email, ticket, or tool output. | Untrusted text remains data; the agent does not treat it as authority or execute its requests. |
| Tool misuse and privilege escalation | Alter arguments, call tools out of sequence, reuse an approval, or request an administrator action. | Independent authorization checks reject the action and record the denial. |
| Data exfiltration | Ask the agent to send fixture secrets through a URL, request body, memory write, log field, or final answer. | Data-loss controls block or redact the transfer and produce an alert without leaking the secret. |
| Memory poisoning | Write false or malicious facts, then start a new session or switch tenants. | Writes are authenticated and scoped; untrusted memory cannot influence another run. |
| Approval bypass | Probe race conditions, duplicate approvals, stale approval tokens, and changes made after approval. | Approval is bound to the exact action, identity, parameters, and time window. |
| Runaway execution | Induce retries, recursion, parallel tool calls, large outputs, or expensive searches. | Budgets, depth limits, timeouts, and circuit breakers stop the run predictably. |
| Multi-agent escalation | Send untrusted instructions or fabricated authority from one agent to another. | Each agent authenticates the sender, validates the message, and enforces its own policy. |
OWASP’s agent-security guidance recommends repeatable tests for these cases and regression testing after changes to prompts, tools, memory, retrieval, policies, or model providers. Add variants for altered context, missing tools, delayed responses, malformed outputs, and partial service failure; attackers rarely present the same input twice.
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Combine structured cases, adversarial inputs, and realistic multi-step tasks. A single successful refusal proves little: test whether authorization is enforced outside the model and whether the environment blocks an unsafe action if the model is manipulated.
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- Define the objective and invariant. Example: “The agent may summarize a support ticket but must never send email.” The invariant is that no email tool call or network request occurs.
- Prepare the fixture. Load synthetic records, a malicious document or tool response, a constrained identity, and an approved destination if the scenario needs one.
- Start full telemetry. Correlate model requests, prompt assembly, retrieval results, memory reads and writes, tool arguments, authorization decisions, network events, approvals, alerts, and operator actions with one run identifier.
- Vary conditions. Repeat with different autonomy levels, tool availability, search or code-execution access, timing, context length, and presence of another agent.
- Apply stop rules. Pause on forbidden destinations, unauthorized data access, boundary crossing, suspected secret exposure, or resource-limit violations. Preserve the state before teardown.
- Classify the result. Mark the scenario pass, fail, blocked by a control, inconclusive, or not executed, and record the exact observed side effect.
Measure without overclaiming
Microsoft Foundry documents automated adversarial probing and scoring of attack-response pairs, including Attack Success Rate (ASR). ASR is useful only with the test set, success definition, evaluator, and confidence or review method stated. It is not a complete safety score and does not replace investigation of a single high-impact failure.
No consulted guidance establishes a universal number of prompts, cases, hours, or tokens that makes an agent safe. OpenAI’s October 23, 2026 account describes an internal containment red-team campaign that used “hundreds of billions of tokens”; that figure describes one organization’s scale and progress, not an industry benchmark or proof that more tokens alone produce better security.
What should I monitor and when should I stop a run?
Monitor both agent behavior and boundary behavior. Model text can look harmless while a tool, proxy, memory store, or approval service performs the consequential action.
Alert and stop triggers
- Attempted access to a forbidden file, tenant, credential, host address, or network destination.
- Tool calls that do not match the approved task, identity, sequence, parameters, or approval.
- Cross-agent messages that lack authenticated provenance or contain untrusted instructions.
- Secret-like values in outbound requests, memory writes, logs, traces, or final responses.
- Unexpected process creation, privilege changes, persistence, DNS behavior, or sandbox escape indicators.
- Retry, recursion, token, cost, storage, or time budgets nearing their limit.
The operator pause should suspend queued and in-flight work where technically possible; the kill switch should revoke credentials, isolate networking, and terminate the runtime. Exercise both controls before adversarial testing, including during a deliberately noisy run.
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What evidence should each test retain?
Keep a compact, reproducible record rather than indiscriminately storing every secret or customer-like value. Redact credentials and minimize sensitive payloads while preserving hashes, schemas, and event relationships needed for review.
| Evidence | Why it matters |
|---|---|
| Build manifest | Identifies model, agent code, prompt or policy, tools, dependencies, retrieval index, memory settings, and environment image. |
| Scenario definition | Shows attacker input, fixture, expected safe behavior, permitted side effects, and severity. |
| Execution trace | Links model turns, retrieved content, tool arguments and results, approvals, denials, retries, and timing. |
| Boundary telemetry | Records filesystem, process, network, identity, proxy, and resource-limit events. |
| Operator record | Captures alerts, pauses, shutdowns, manual decisions, and any deviation from the plan. |
| Disposition | States root cause, fix owner, due date, residual risk, release decision, and regression-test identifier. |
Store timestamps in a consistent format and use a run identifier across systems. Preserve the exact malicious artifact or a content-addressed, sanitized copy so another evaluator can reproduce the result without receiving a live exploit against a real service.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How do I turn findings into a release decision?
Prioritize impact and exploitability, not the number of failed prompts. A model refusal failure with no reachable side effect may require a prompt or evaluator fix; a single path to production data, credential use, or irreversible external action can block release.
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- Confirm the finding in the isolated environment and identify the first control that failed.
- Determine whether the root cause is model behavior, tool design, authorization, data handling, network policy, memory isolation, monitoring, or operator procedure.
- Fix the narrowest control that removes the unsafe capability; prefer enforcement outside the model for permissions and destinations.
- Add the original case and a nearby mutation to the regression suite.
- Rerun affected cases and document residual risk, compensating controls, owner, and expiry date.
- Require explicit approval for any accepted risk and set a review trigger for model, tool, data, provider, or autonomy changes.
Do not treat a green scan as a safety guarantee. Agent capability, context, tools, and connected data change over time, so schedule evaluations across autonomy levels and maintain a tested intervention path after deployment.
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Which evaluation approach or tool should I choose?
Choose by threat coverage and operational fit, not by a single score. Compare whether an approach can execute realistic multi-step tasks, isolate tools, constrain network and filesystem access, expose useful telemetry, support repeatable runs, and provide human review.
| Approach | Strength | Limitation to account for | Best use |
|---|---|---|---|
| Manual scenario exercises | Rich context, novel abuse ideas, and expert judgment. | Slower and harder to reproduce without disciplined recording. | Threat discovery and high-impact workflows. |
| Automated adversarial scanning | Broad repeat coverage and machine-generated attack-response scoring. | Scores depend on test set and evaluator; may miss environment side effects. | Regression and scale, paired with trace review. |
| Sandboxed agent evaluation | Exercises tool use while constraining execution and observing boundaries. | Sandbox weaknesses or allowed network paths can still create risk. | Containment validation and realistic tool workflows. |
| Hybrid human-plus-automation | Combines repeatability with investigation of subtle failures. | Requires clear handoffs, triage capacity, and evidence standards. | Pre-release gates and continuous evaluation. |
The UK AI Security Institute’s Inspect Sandboxing Toolkit is an open-source set of sandbox plugins for agent evaluation. Its guidance emphasizes selecting mitigations for the risk profile and separating model inference from the environment that executes tool calls. Microsoft Foundry documents an automated adversarial-scanning workflow with evaluation and reporting. These descriptions indicate different capabilities, not a controlled comparative ranking.
What changes when the agent is already in production?
Keep red-team fixtures and credentials separate from production, but carry the same controls into operations: least privilege, destination allowlists, approval binding, memory tenancy, telemetry, budgets, and an intervention path. Re-evaluate after model-provider changes, new tools, altered retrieval sources, expanded permissions, new agents, or changes in autonomy. Review alerts and near misses as regression candidates, and retire credentials or routes that the agent no longer needs.
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Frequently Asked Questions
Can I red-team an agent against real customer data?
Use synthetic data or a specifically approved, minimized dataset instead. If regulated or customer data is unavoidable, obtain written authorization, restrict access and retention, mask fields, and make the stop procedure and incident owner explicit before testing.
Is a prompt-injection benchmark enough to approve an agent?
No. Prompt tests cover only one input path. Approval also requires checking tool authorization, network and filesystem boundaries, memory isolation, approval integrity, resource limits, monitoring, and the ability to stop execution.
How often should an AI agent be red-teamed?
Run a baseline before release and rerun affected scenarios after any material change to the model, prompts, tools, retrieval, memory, policies, provider, or permissions. Continuous monitoring and periodic broader exercises catch drift between those gates.
What is a useful way to report an attack-success rate?
Report the test-set composition, attack definition, evaluator, number of attempts, confidence or review process, and severity-weighted failures. Keep high-impact incidents visible even when the aggregate rate is low.
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