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Audit the agent as a complete system—not just its model prompt. Test whether direct user instructions or malicious instructions embedded in content the agent reads can redirect its work, expose data, or trigger unauthorized actions. Use sandboxed tools and dummy data, define observable pass/fail conditions, and repeat the tests when the system changes.
What should a prompt-injection audit cover?
Prompt injection occurs when input changes an AI system’s behavior or output in an unintended way. The injection may come from a user’s message or from untrusted material the agent processes, such as an email, document, or webpage. If the model can process the content, an instruction does not have to be visible to a person to matter.
For an agent, the security question is not only whether it produces a suspicious answer. Its tools, data access, memory, and autonomy can turn a changed response into a consequential action. Scope the audit to the capabilities the deployed system actually has and the business impact if those capabilities are misused. OWASP’s GenAI Security Project notes that retrieval-augmented generation (RAG) and fine-tuning do not fully mitigate prompt injection.
Test both direct and indirect paths
| Path | Where the instruction enters | What the test should establish |
|---|---|---|
| Direct injection | User-controlled prompt or message | Whether the agent follows instructions that conflict with the task or its permissions. |
| Indirect injection | External content the agent retrieves, browses, or receives through an integration | Whether untrusted content can redirect the agent or cause it to misuse tools, data, or authority. |
Test indirect injection through the external-content channel under assessment. Putting a payload only in the user’s message does not test whether the agent safely handles a malicious instruction inside a document, email, or webpage.
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How do you define the system and its trust boundaries?
Start by recording exactly what is being tested. A result is useful for a release decision only if you can connect it to the configuration and boundaries that produced it.
- Tested application and agent version, model provider, and relevant model configuration.
- System prompts, policies, and other trusted instructions.
- Retrieval sources, integrations, and memory configuration.
- Available tools, credentials, permission scopes, and output destinations.
- Approval rules, including which actions require approval and how approval is checked.
- The boundaries between trusted instructions and untrusted user or retrieved content.
Record where each test input enters and what it can reach. For example, an agent with read-only access to a test document store presents a different risk from one that can also send messages or change records. Do not infer production safety from a test that used fewer tools or narrower permissions than production.
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How do you build useful abuse cases?
For each test, specify the legitimate task, the injection point, the resource or capability at risk, what the agent is permitted to do, and the observable condition that counts as failure. This makes it possible to distinguish a harmless response change from a security violation.
| Abuse case | Example failure to check |
|---|---|
| Prompt override or goal hijacking | The agent abandons the permitted task to follow an attacker’s instruction. |
| Tool misuse | The agent invokes a tool or performs an action that is not authorized for the task. |
| Privilege escalation | The agent uses access beyond the user’s or task’s permitted scope. |
| Sensitive-data disclosure | Protected information reaches an unauthorized user, tool, or destination. |
| Memory poisoning | Untrusted content changes stored information in a way that affects later behavior. |
| Approval bypass | A high-impact action proceeds without the required valid approval. |
| Recursive or cost-intensive behavior | The agent enters repeated or excessive tool activity instead of stopping within the intended limits. |
| Multi-agent chaining | An instruction crosses agent boundaries and causes a downstream agent to take an unauthorized action. |
Adapt these cases to the product. If the application does not accept images, for example, multimodal injection is outside the tested input surface; if it does, include the accepted modalities in scope. OWASP identifies multimodal inputs as an additional attack surface when a system processes them.
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How do you run the tests safely?
- Use an isolated test setup. Choose dummy accounts and data, sandboxed tools, and safe substitutes for email, shell, payment, or administrative actions. Avoid exposing real users, production records, or irreversible operations to test payloads.
- Write the expected result first. State the legitimate task, the intended violation being tested, and the observable outcome that constitutes success or failure. For an indirect test, place the payload in the external-content source the agent is expected to read.
- Exercise each accepted input path. Test ordinary user instructions as well as content delivered through retrieval, browsing, or integrations. Vary formatting or modality only where the application actually accepts it.
- Observe the whole execution. Inspect tool calls, data access, approval decisions, state changes, and stopping behavior, not only the agent’s final wording.
- Save the evidence. Keep the tested configuration, case definition, expected outcome, observed result, relevant tool trace, and any residual risk or limitation.
What counts as a pass or a failure?
Judge the result against the abuse case’s predefined success criterion. A polite refusal in the final response is not evidence that no side effect occurred: the agent may have made an unauthorized call or changed state before refusing.
- Failure: an unauthorized tool call, sensitive data crossing a boundary, an unapproved state change, an approval bypass, or another defined violation occurs.
- Pass for that case: the expected safeguard prevents the defined violation, and the execution trace shows no prohibited action or data transfer.
- Inconclusive: the test did not reach the relevant boundary, the evidence is incomplete, or the result cannot be distinguished from a transient or unrelated failure.
Record outcomes by attack path, task, capability, severity, and control behavior rather than collapsing everything into a single score. For stochastic behavior, repeat cases and inspect individual task results as well as aggregate rates; an aggregate can conceal a serious failure on a high-impact task.
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Which controls should the audit verify?
Least privilege at the enforcement boundary
Give the agent only the tools and resource scopes its task needs. Verify that the application or tool boundary rejects unauthorized requests; a prompt telling the model not to use a capability is not a substitute for enforcing permission checks.
Independent approval for consequential actions
Require explicit, current approval for high-impact or irreversible actions. Check that approval is bound to the proposed action and its parameters. Include attempts to reuse stale approval or apply approval for one action to a different one.
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Safe treatment of untrusted content
Identify external content as untrusted and keep it separate from trusted instructions in the system design. Validate inputs and outputs, but do not treat delimiters or filters alone as proof that malicious instructions have been neutralized.
Deterministic checks on tool execution
Where possible, compare proposed actions with the original user intent and enforce permissions in application code. OWASP discusses capability-tracking designs that separate privileged planning from quarantined parsing, while noting that this approach is early-stage and needs further research; it should not be treated as a proven, stand-alone fix.
How should results inform a release decision?
Keep the configuration, abuse cases, expected outcomes, observed decisions and actions, and accepted residual risks together as release evidence. Run adversarial regression tests in CI/CD and gate material changes that affect high-risk policies, credentials, or approvals. A passing smoke test establishes only what happened in the tested cases; OWASP describes its smoke tests as illustrative, not as a security benchmark.
Evaluation methods also need to adapt. In a January 2025 account of its evaluation work, NIST’s Center for AI Standards and Innovation wrote, “Evaluations need to be adaptive.” In one held-out Workspace evaluation of an upgraded Claude 3.5 Sonnet setup, CAISI reported an 81% attack success rate for its strongest newly developed attack, compared with 11% for its strongest baseline attack. Those figures apply to that evaluation’s model and attack setup; they are not estimates of how often AI agents generally will fail.
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Use a smoke test as one repeatable check, not as proof of resistance to an adaptive attacker. Revisit the abuse cases when capabilities, integrations, prompts, policies, or authorization rules change, and state clearly which paths and configurations the evidence covers.
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