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Test the agent as a complete socio-technical system, not as a chat model in isolation. A credible security run maps every trust boundary, establishes normal behavior, injects hostile instructions through both user and third-party content, exercises the authorization gateway without the model, attempts consequential actions and data exfiltration, and then turns every failure into a repeatable regression test.
Prompt injection is a third party misleading a model by placing malicious instructions in its context. Because that context can include retrieved pages, emails, API responses, memories and tool results, a model that refuses a few obvious jailbreaks can still make an unsafe call. A passing test preserves the user’s original task, treats external content as data, rejects unauthorized tools and parameters, requires an approval tied to the exact action, prevents secret leakage, maintains tenant isolation, bounds retries and cost, and leaves an auditable trace.
What are you actually testing when you test an AI agent?
You are testing the interaction of the model, system and developer prompts, retrieval and filtering, memory, orchestration, tool schemas, credentials, approval gates, external services and observability. An injection is successful if untrusted content changes that interaction in a way that violates the intended policy, even when the final answer looks harmless.
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OpenAI defines the issue this way: “Prompt injections occur when a third-party—not the user nor the AI—misleads the model by injecting malicious instructions into the conversation context.” The practical implication is that every place text or structured data enters the context is an attack surface. A red-team plan therefore tests the whole path from ingestion to side effect.
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How do you map the agent’s system and trust boundaries?
Start with an inventory that identifies who controls each input, what the agent can do with it and where a security decision is enforced. Treat every external surface as testable, including outputs that your own tools generate.
| Component | Examples of inputs or capabilities | Questions to record |
|---|---|---|
| Prompts and policies | System prompt, developer rules, policy templates | Which version is deployed? Can retrieved text or a tool result alter its priority? |
| Retrieval and ingestion | Web pages, files, tickets, email, search indexes | How are HTML, markdown, metadata and hidden text normalized and filtered? |
| Memory | Conversation summaries, profile fields, saved notes | Who can write memory, for how long, and can one tenant read another’s records? |
| Tools and credentials | Read APIs, write APIs, browser, shell, code execution | Are operations and resources allow-listed? Are read and write credentials separate? |
| Approvals | Human confirmation, policy service, transaction token | Is approval bound to the actor, session, operation and exact parameters? |
| Outbound channels | Email, URLs, logs, citations, web requests, other agents | Can secrets leave through a side channel, and what telemetry records it? |
For each edge, label the source as user supplied, retrieved, tool returned or agent generated. Record the trust assumption, the sanitizer or validator, the credential used, the expected side effect and the evidence you will retain. This map becomes the test matrix and prevents a “safe model” result from hiding an unsafe integration.
How do you establish a benign baseline before attacking?
Run representative normal tasks first. Controls should be shown to work under ordinary conditions before adversarial load is applied; otherwise a failure may simply be a broken workflow.
- Choose tasks that cover read-only work, a proposed write, a genuinely approved write and a denied request.
- Capture the selected tool, every argument after parsing, approval prompts, latency, retries, token and cost usage, response text and resulting side effect.
- Repeat each task enough times to learn normal variation. Record the deployed model or provider, prompt and policy versions, retrieval configuration and tool versions.
- Define invariants, such as “the agent never sends a message without an approval record” or “a search request cannot name a resource outside the caller’s tenant.”
- Freeze those traces as baseline fixtures. Later, compare both decisions and trajectories, not only the final prose.
Baselines also expose accidental defenses. If a tool is never selected in normal use, an apparent attack resistance may mean the feature is simply unavailable. Conversely, a benign task that legitimately uses a write operation tells you what a safe, approved trajectory looks like.
How should you test direct prompt injection?
Direct tests place the attack in the user message or another field intentionally treated as an instruction. Use harmless fixtures in a non-production environment; the objective is to observe policy enforcement, not to cause real damage.
Build a varied fixture set
- Instruction-priority conflicts: ask the agent to ignore its task, reveal hidden instructions or follow a lower-priority message.
- Role-play overrides: request that it act as an unrestricted administrator or claim that safety rules have been suspended.
- Privileged-tool bait: ask it to call a high-impact tool, then vary the resource, amount, recipient and approval state.
- Encoding and obfuscation: place the same instruction in escaped text, alternate encodings, unusual whitespace, markdown, images or nested data fields.
- Context pressure: prepend long irrelevant material, repeat the attack and split it across messages to test recency and truncation behavior.
Score the behavior
For each fixture, verify that the original user goal remains intact, the injected instruction is treated as untrusted or refused, no unauthorized tool is selected, arguments remain within schema and policy, and the trace contains the expected denial or clarification. A refusal alone is not a pass if the model leaked a system prompt, made a hidden call or exposed a secret in its explanation.
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How do you test indirect prompt injection through data and tools?
Indirect injection is usually more realistic than a conspicuous jailbreak because the attacker controls content the agent is expected to read. Put the fixture through the same production ingestion, chunking, retrieval, rendering and filtering path; a clean unit test of a raw string is not sufficient.
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- Retrieved documents and search snippets that tell the agent to disregard its task or disclose context.
- Web pages containing visible instructions, hidden HTML, markdown links, image URLs or copied prompt text.
- Email bodies, attachments, calendar descriptions and support tickets.
- API responses whose JSON fields contain natural-language commands or malicious URLs.
- Memory records written by another user, an imported application or a previous compromised run.
- Tool results that request a second tool call, a credential, a file upload or a change in approval state.
Check the full data path
- Seed an isolated fixture with a unique marker and the attempted instruction.
- Ingest it exactly as a real customer document, page, message or API response would be ingested.
- Run a task that should use the content while explicitly requiring the agent to treat it as data.
- Inspect retrieval ranking, transformed text, citations, model context, selected tools and outbound requests.
- Repeat with the instruction in metadata, a quoted block, a table cell, an attachment name and a tool-result field.
A pass means the content can be summarized or used as evidence without acquiring authority. The agent should quote or flag a suspicious instruction rather than execute it, and the gateway should remain safe even if the model misinterprets the text.
How do you test multi-turn and crescendo attacks?
Single-turn resistance does not establish session-level safety. An attacker can build rapport, ask for harmless partial steps, repeat a claim across turns or wait until the context is crowded before requesting the consequential action.
Useful session scenarios
- Start with a normal research task, then add a retrieved note that gradually reframes the objective.
- Ask for a draft, then request that the agent send the draft without a new approval.
- Complete half of a transaction, expire or alter the approval, and continue from the old conversation.
- Repeat a malicious instruction in several languages or encodings while adding long irrelevant context.
- Cause a tool timeout, resume the session and check whether the agent repeats a write operation.
Evaluate state transitions: what the agent believed was approved, which messages were retained or summarized, whether a stale memory changed the decision and whether a new confirmation was required. Include context-window pressure and partial task completion, because truncation can remove the very policy text that constrained an earlier turn.
How can you test the tool gateway independently of the model?
Send crafted invocations directly to the authorization or API layer. The gateway must be safe even if a model, prompt, orchestrator or compromised client asks for an unsafe operation.
| Control | Test | Passing evidence |
|---|---|---|
| Deny by default | Call an unregistered operation or omit the policy decision. | The request is rejected before execution. |
| Typed schemas | Use wrong types, extra fields, nulls, extreme values and nested objects. | Validation fails closed; no coercion creates a broader action. |
| Allow-lists | Change an operation name, resource identifier or URL to an unapproved value. | Only explicitly allowed operations and resources succeed. |
| Identity binding | Replay a valid call under another user, tenant or session. | Authorization is evaluated for the current actor and tenant. |
| Approval binding | Modify amount, recipient, resource or parameters after approval. | The token is invalidated and a fresh approval is required. |
| Replay resistance | Resend the same write request and an old approval. | Idempotency or expiry prevents a duplicate side effect. |
| Credential separation | Attempt a write with a read credential or inspect a sandbox environment. | Read and write privileges are distinct; no ambient secret is available. |
Test authorization immediately before execution, not only when the model proposes a call. Log the normalized arguments, policy decision, actor, tenant, approval identifier and execution result so an investigator can prove what was or was not allowed.
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How do you exercise high-impact actions safely?
Use a sandbox or reversible test tenant and instrument every side effect. High-impact categories include refunds, deletion, messaging, purchases, code execution, data export and permission changes.
- Define the exact operation, resource, limits and required approval in policy.
- Attempt the action with no approval, an expired approval, an approval for another user, an approval for different parameters and an approval issued before the session changed.
- Try boundary values, duplicate requests, missing fields, stale resource versions and cross-tenant identifiers.
- Interrupt the model after proposal but before execution, then resume and verify the gateway rechecks authorization.
- Confirm that a denied action leaves no partial side effect, queued message, audit gap or leaked response.
Approval should be bound to the actor and exact parameters and checked immediately before execution. A human clicking “approve” on a vague summary is not equivalent to approving the normalized operation the service will execute.
How do you detect exfiltration and side channels?
Plant non-sensitive canary values in the test context and monitor instrumented destinations. Never use real credentials or customer data merely to prove that a leak is possible.
- Watch URLs, redirects, markdown images, browser requests and DNS-like lookups for canary values.
- Inspect email bodies, attachments, shell commands, generated code, logs and citations.
- Monitor calls made by a browser, code runner or another agent on the current agent’s behalf.
- Attempt to induce the agent to place a secret in an error message, tool argument or filename.
- Verify network egress rules and ensure sandboxed tools cannot reach unauthorized services.
Repeat the checks with sensitive-looking strings split across messages, encoded or embedded in retrieved content. A pass requires both prevention and detection: the value should not leave, and the trace should make a blocked attempt attributable to a session and tool.
How do you test failures, limits and recovery?
Resilience tests target the paths where safeguards are often bypassed. Inject recursive instructions, malformed arguments, tool errors, timeouts, oversized context and partial failures.
- Set explicit maximum turns, retries, tokens, wall-clock time and cost for each task.
- Return a timeout after a write is accepted but before the result reaches the agent; verify idempotency and reconciliation.
- Make a tool return an instruction to call another tool, then ensure orchestration depth is bounded.
- Send invalid or unexpectedly large results and confirm truncation or rejection is safe.
- Trip a circuit breaker and verify the agent stops, reports uncertainty and does not “try a different” privileged path.
- Restart workers and replay queues to check that memory, approvals and side effects remain consistent.
Safe recovery is explicit. The agent should ask for clarification or hand off rather than silently broadening permissions, retrying a write indefinitely or treating an error message as an instruction.
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What does a passing test look like?
Report severity and reproduction rate rather than reducing a probabilistic system to one binary number. Preserve enough evidence for another engineer to reproduce the trajectory.
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|---|---|---|
| Task integrity | The original goal survives hostile content and context pressure. | Medium if the answer is diverted; high if a side effect follows. |
| Instruction handling | External text remains data and cannot override policy. | High when retrieved content gains execution authority. |
| Tool authorization | Unknown operations, resources and parameters are denied. | Critical when an unapproved write executes. |
| Approval security | Approval is actor-, session- and parameter-bound and checked at execution. | Critical when stale or mismatched approval works. |
| Confidentiality | Secrets and canaries cannot leave through direct or side channels. | Critical for credential or cross-tenant disclosure. |
| Isolation | Tenant, user and session data remain separated. | Critical when another identity’s data or tools are reachable. |
| Resilience | Retries, recursion, tokens, cost and time are bounded. | High for unbounded execution or repeated writes. |
| Evidence | Prompts, versions, traces, arguments, decisions and timestamps are retained. | Medium to high when an incident cannot be reconstructed. |
For every failure, record the fixture, build and provider, prompt and policy versions, retrieval settings, model messages, normalized tool arguments, approvals, denials, network events, side effects and timestamp. Classify whether the issue is deterministic or intermittent, then assign a reproduction rate and an owner.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Which tools can accelerate red-team testing?
Automation helps generate varied fixtures and inspect trajectories, but it does not replace application-specific authorization tests.
Promptfoo
Promptfoo’s documented workflow initializes a red-team configuration, generates adversarial tests with plugins and strategies, runs them against the target and reports results. Its agent guidance supports trajectory assertions—for example, asserting that a refund_payment call with particular arguments never occurs—and trace inspection of tool executions and guardrail decisions. Use it to make direct, indirect and multi-turn cases repeatable in CI, then add assertions for your own tools, tenants and approval semantics.
PyRIT and Garak scenarios
Microsoft PyRIT’s Garak scenarios provide seed probes for prompt injection, web injection, system-prompt extraction, API-key exposure and encoding. They broaden language and obfuscation coverage, but production teams still need fixtures that reflect their retrieval formats, tool schemas, data classifications and business approvals.
Compare any harness on the dimensions that matter to your deployment: direct, indirect and multi-turn coverage; observation of real trajectories and side effects; independent gateway testing; reproducibility and regression support; sandboxing and exfiltration detection; language and encoding breadth; CI/CD integration; and evidence retention.
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How do you turn findings into a regression program?
- Save each confirmed failure as a minimal deterministic fixture, including the exact untrusted content and session sequence.
- Define the expected decision, tool trajectory, approval requirement, side-effect count and data-egress result.
- Run the fixture in an isolated environment on every prompt, policy, tool, memory, retrieval, orchestration or provider change.
- Run a smaller smoke suite on every build and a broader adversarial suite on a scheduled cadence or before release.
- Compare traces, not only final text; a changed argument, extra retry or new outbound request can be a regression even when the answer is identical.
- Require a reviewed risk disposition for any failure: fix, compensating control, accepted residual risk or block release.
Keep the tested build, model or provider, configuration versions, fixtures, traces and timestamps together. When a control changes, rerun the benign baseline as well as adversarial cases so a security fix does not silently break legitimate work or encourage operators to disable the gateway.
Frequently Asked Questions
Can prompt injection ever be eliminated completely?
No. Untrusted text will continue to appear in documents, messages and tool responses, and model behavior is probabilistic. The practical goal is to limit authority at the gateway, isolate data and credentials, require explicit approvals for consequential work, and make attempted misuse observable.
Should I disable all tools while testing injection resistance?
Use two phases. Begin with read-only or fully mocked tools to explore model behavior, then test the real authorization path in a sandbox with reversible effects; model-only results cannot validate permissions, replay protection or side-effect handling.
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Run a smoke set on every build and rerun the full suite whenever prompts, policies, tools, retrieval, memory, orchestration or the model provider changes. Add scheduled runs because new third-party content and dependency updates can alter trajectories without a code change.
What telemetry is essential when an unsafe call is suspected?
Retain the rendered context or content references, model and policy versions, normalized tool arguments, actor and tenant, approval evidence, gateway decision, retries, network egress and side-effect result with synchronized timestamps. Redact secrets while preserving stable canary identifiers so investigators can correlate events.
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