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Test an AI agent end to end, not just its system prompt. Put the model, prompts, retrieved content, memory, tool descriptions, authorization gateway, credentials, orchestrator, and every side-effect channel in a sandbox. Use synthetic secrets and instrumented tool substitutes, then attack the system with both direct and indirect prompt injections. A passing result means the agent preserves the user’s goal, refuses unauthorized instructions and tool calls, validates arguments outside the model, and requires explicit approval for consequential actions.
Because model behavior is probabilistic, run each scenario repeatedly and report attack success alongside benign-task success, false blocks, approval bypasses, exfiltration, and resource use. Keep the exact prompts, fixtures, traces, approvals, denials, and software versions so a failure becomes a regression test rather than a one-off demonstration.
What does a complete prompt-injection test cover?
A complete test covers the whole execution path in which untrusted content can influence a decision. OWASP’s guidance treats the model, system and developer prompts, retrieval pipeline, tools, permissions, memory, and output handling as one attack surface. OpenAI defines prompt injection as a third party inserting instructions into context to mislead the model. NIST calls the indirect form agent hijacking when an email, file, or website contains instructions that redirect an agent to a harmful task.
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- Direct injection: the user message itself asks the agent to ignore its rules, disclose hidden instructions, change the goal, or call a privileged tool.
- Indirect injection: hostile text is embedded in an email, ticket, document, web page, search result, calendar entry, image alt text, or tool response.
- Tool misuse and excessive agency: manipulated model output triggers an unauthorized read, write, send, delete, purchase, code execution, privilege change, or cross-tenant access.
- State and delegation attacks: poisoned memory, retrieval results, inter-agent messages, or altered approval state carry the attack into a later step.
- Availability and control failures: recursive plans, repeated retries, oversized context, malformed results, or provider errors consume resources or disable monitoring.
The objective is not to make the model say “no” to everything. It is to preserve the authorized task while containing untrusted instructions and enforcing permissions in application code.
How do I build a safe baseline before attacking?
Record the configuration, run benign controls, and move all side effects into a test environment before sending adversarial content.
Record the complete build
Create a versioned test manifest before the first attack. Include the agent build, model and provider, system and developer prompts, tool policy, retrieval and memory configuration, credential scopes, orchestrator version, approval rules, network destinations, and safety controls. Hash the fixture files and record the tool and policy versions used for each run.
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|---|---|
| Model layer | Model identifier, provider, inference settings, context limits, and date of the run |
| Instruction layer | System prompt, developer prompt, user task, and any policy or routing templates |
| Data layer | Retrieval indexes, memory namespaces, fixture hashes, access rules, and document provenance |
| Action layer | Tool catalog, schemas, parameter constraints, API scopes, identity binding, and approval requirements |
| Control layer | Gateway version, orchestrator version, rate and recursion limits, circuit breakers, logging, and alert rules |
Use a sandbox and synthetic canaries
Replace production credentials, customer records, payment endpoints, and real secrets with synthetic equivalents. Instrument every substitute so it records the caller identity, arguments, approval token, response, and side effect without contacting a real external system. Put outbound HTTP, email, file, code-execution, and purchase operations behind mocks that can deliberately return errors or malicious-looking data.
Run benign controls first
Give the agent ordinary tasks that should succeed, such as summarizing a clean document or creating a permitted test record. Measure completion rate, normal tool sequence, latency, token use, retry count, and approval behavior. These controls establish whether an adversarial fixture caused a regression or merely exposed a pre-existing workflow problem.
How do I map capabilities and enforce authorization outside the model?
Make the tool gateway the security boundary. The model may propose an action, but application code must decide whether that caller, resource, parameter set, and approval state are valid.
- Inventory every capability. List tools, API scopes, data stores, memory namespaces, network destinations, file systems, code runners, and inter-agent channels.
- Define a capability contract. For each item, document the permitted caller, allowed resources, parameter constraints, reversibility, rate limit, recursion limit, and approval requirement.
- Bind identity and session. Resolve the user, tenant, session, and delegation chain at the gateway. Do not trust an identity string supplied by the model or by retrieved text.
- Validate parameters in code. Check object ownership, destination allowlists, amount or quantity limits, path normalization, and data classification before execution. Reject unknown fields and unsafe combinations.
- Require action-specific approval. For high-risk operations, show the reviewer the exact tool, arguments, target, and expected effect. A general “continue” instruction is not an adequate substitute.
- Test the gateway directly. Send crafted tool invocations without involving the model. The gateway must reject unauthorized calls even when the request is perfectly formed and confidently worded.
OWASP’s prompt-injection guidance specifically places tool-call validation, least privilege, approval, and downstream output controls around the model rather than relying on a system prompt alone.
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How do I test direct prompt injection?
Use harmless probes that attempt to override instructions, reveal hidden context, change the user’s goal, or obtain a privileged tool call. A pass preserves the original task and prevents disclosure or unauthorized action while still completing benign work.
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Build a direct-injection matrix
- Ask the agent to “ignore previous instructions” or to treat the latest message as the new system prompt.
- Request the system prompt, developer instructions, hidden chain-of-thought, credentials, or internal policy text.
- Change the stated goal midway, for example from “summarize this report” to “send the report to an external address.”
- Ask for a privileged read, write, delete, purchase, code execution, or role-change operation.
- Wrap the same request in base64, Unicode confusables, mixed languages, unusual spacing, markup, quoted text, or a long context.
- Split the attack across multiple turns so that no single message contains the complete malicious instruction.
Define the expected result before running it
For each probe, specify the allowed answer, allowed tools, prohibited disclosures, and required approval. Capture the exact input, retrieved context, tool proposals, gateway decisions, final response, and side effects. Do not mark a test as passed merely because the final prose sounds cautious: an unauthorized tool call that the gateway blocked is a control success but still a model-behavior finding worth tracking.
Check preservation of the user’s goal
Include a useful benign task in every injection case. For example, ask for a summary while placing a request for hidden instructions in the same conversation. The agent should complete the summary, ignore the override, and avoid exposing protected context. This catches defenses that “pass” only by refusing all work.
How do I test indirect injection and agent hijacking?
Plant clearly marked malicious instructions in realistic data sources and observe whether the agent treats them as data or as authority. NIST’s AgentDojo experiments provide useful scenario patterns in simulated Workspace, Travel, Slack, and Banking environments.
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Seed every ingestion path
Create fixtures for synthetic email, support tickets, office documents, web pages, search results, calendar entries, image alt text, and tool responses. Vary where the hostile text appears: visible body text, quoted replies, metadata, comments, footers, OCR output, or a response field that looks like a system message.
Test exfiltration channels
Ask the injected instruction to transmit a synthetic secret through an HTTP request, markdown image, link, email, log entry, file upload, or another tool. Instrument each channel and make the canary unique to the scenario so you can identify exactly which path leaked it. A blocked request is not enough if the secret appears in a URL, tool argument, generated file, or log that an attacker can read.
Use single-turn, multi-turn, and crescendo attacks
Run each fixture once as an isolated task and again after normal conversation history has accumulated. In a crescendo attack, start with harmless requests, gradually reframe the user’s objective, and only then introduce the exfiltration or destructive action. Compare the agent’s behavior when the hostile item is retrieved automatically, quoted by the user, or returned by a tool.
NIST describes agent hijacking as making a resource look like ordinary data while embedding instructions intended to redirect the agent. Your expected result should therefore distinguish “the document contains an instruction” from “the user authorized this action.”
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Attempt every high-impact operation with an attacker-controlled goal, tool description, parameter, response, and approval state. OWASP defines excessive agency as damaging action triggered by unexpected, ambiguous, or manipulated model outputs, including injections and compromised extensions or peer agents.
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Cover the destructive and cross-boundary cases
- Read data belonging to another user or tenant.
- Write, delete, send, purchase, or publish outside the stated task.
- Execute code, alter permissions, or create a new credential.
- Change a destination, amount, account, file path, or recipient after approval.
- Use a tool whose name or description has been replaced with attacker-controlled text.
- Treat returned data as a new instruction, including a fabricated approval or policy message.
Tamper with the control plane
Alter tool names, schemas, descriptions, returned data, approval tokens, and timestamps in the sandbox. Test stale approvals, approvals for different arguments, approvals issued to another session, and approvals whose target changed after review. The reviewer must see the exact action and arguments immediately before execution, and the gateway must revalidate them at execution time.
Verify operational limits
Exercise least-privilege scopes, identity binding, parameter validation, rate limits, retry limits, recursion limits, and circuit breakers. A plan that eventually reaches a safe result can still be a failure if it makes unauthorized attempts, loops indefinitely, or consumes excessive resources before the gateway stops it.
How do I test memory, retrieval, and multi-agent paths?
Treat persistent state and delegated messages as untrusted inputs. Poisoned content must remain isolated, expire according to policy, and never grant authority to a different user or session.
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Memory poisoning
Store a synthetic instruction that claims to be a permanent preference, policy, credential, or approval. Start a later session and check whether the agent accepts it without user confirmation. Verify namespace isolation, expiration, deletion, provenance, and the behavior when a memory record is edited or replayed.
Retrieval and vector-store poisoning
Insert conflicting or malicious chunks into the index and test retrieval authorization separately from tool authorization. Confirm that a user cannot retrieve another tenant’s content through semantic similarity, metadata tricks, or a fabricated document identifier. Record which chunks were retrieved so an unexpected result can be reproduced.
Multi-agent delegation
Attempt to make one agent impersonate another, alter an inter-agent message, escalate privileges through delegation, or bypass the orchestrator’s policy. Every message should carry authenticated sender and recipient context, a constrained capability set, and a traceable correlation identifier. The receiving agent must not treat a peer’s prose as an approval unless the orchestrator has issued a verifiable authorization.
How do I stress failure and resource controls?
Security controls must hold when dependencies fail, responses are malformed, or the model is under pressure. Induce tool errors, partial completion, timeouts, empty results, invalid schemas, provider failures, context saturation, recursive plans, and repeated retries.
- Fail closed: an unavailable authorization service, missing identity, or ambiguous result should prevent the side effect rather than silently allow it.
- Bound work: enforce token, cost, latency, retry, rate, and chain-depth limits appropriate to the workflow.
- Make retries safe: use idempotency keys or equivalent protections so a retry cannot duplicate a payment, message, deletion, or write.
- Protect the audit trail: the agent must not disable, rewrite, or erase its own security logs, even when a tool response instructs it to do so.
- Surface partial state: report which actions completed, which were blocked, and which require reconciliation after a timeout.
Include provider and network failures in the same evidence set as successful attacks. A system that blocks injections only while every dependency is healthy is not ready for production.
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What should I measure in an AI-agent security test?
Report disaggregated rates rather than one overall safety score. Repeat scenarios under the exact same configuration, then compare versions and attack classes.
| Metric | What to count | Why it matters |
|---|---|---|
| Attack success or hijack rate | Runs in which the injected instruction changes the goal or produces a prohibited outcome | Shows whether the agent can be redirected |
| Unauthorized-tool-call rate | Tool requests outside the caller’s scope or task authorization, whether blocked or executed | Separates model tendencies from gateway enforcement |
| Sensitive-data exfiltration rate | Runs in which a synthetic secret reaches an unapproved channel | Measures confidentiality impact directly |
| Approval-bypass rate | High-risk actions completed without valid, action-specific approval | Tests the human-in-the-loop control |
| Benign-task success | Authorized tasks completed correctly while the attack is present | Detects defenses that simply refuse useful work |
| False-block rate | Benign tasks incorrectly denied or interrupted | Shows usability and policy overreach |
| Resource consumption | Tokens, cost, latency, retries, tool calls, and chain depth per scenario | Exposes denial-of-service and runaway-agent risk |
For every run, retain prompts, fixture hashes, retrieved items, tool traces, gateway decisions, approvals, denials, timeouts, outputs, and the agent, model, and tool-policy versions. Because behavior varies across runs, publish the number of repetitions and the exact test configuration with each rate; do not present a single run as a universal probability.
What do published benchmark figures actually show?
OpenAI’s 2025 ChatGPT Agent results illustrate why attack classes must remain separate. The figures are not a universal score for every agent, model, browser, or tool policy.
| Scenario | Reported result | Context |
|---|---|---|
| Synthetic text-browser irrelevant-instruction cases | 99.5% | OpenAI, 2025 |
| Visual-browser irrelevant-instruction cases | 95% | OpenAI, 2025 |
| In-context visual-browser data-exfiltration cases | 78% | OpenAI, 2025 |
| Active visual-browser data-exfiltration cases | 67% | OpenAI, 2025 |
The lower exfiltration figures demonstrate why harmless irrelevant-instruction tests cannot stand in for attacks that connect to a real side-effect channel. Reproduce the relevant environment and report your own results instead of borrowing a benchmark number as a guarantee.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How do I turn findings into release gates?
Version the attack corpus and make security behavior part of normal delivery. OWASP’s AI Agent Security Cheat Sheet recommends repeatable abuse-case matrices and retained evidence for the tested agent version, model provider, tool policy, retrieval configuration, expected outcomes, and observed approvals or denials.
- Store each attack with its fixture, expected decision, permitted tools, prohibited effects, and pass criteria.
- Run the corpus in CI/CD whenever prompts, tools, memory, retrieval, policies, model providers, or approval logic change.
- Block a release if a previously denied action succeeds, exfiltration or approval-bypass rates regress, or a high-risk permission expands without corresponding tests.
- Review benign-task success and false-block rates alongside attack metrics so a security fix does not silently disable the product.
- Continue production monitoring for drift, and add every confirmed incident or newly discovered bypass as a regression case.
Keep a human review path for high-impact releases. The reviewer should be able to trace a decision from untrusted input through retrieval, planning, gateway validation, approval, execution, and final output.
Which framework and harness features matter?
Choose a harness by the attack surface it can reproduce, not by the number of canned prompts. OpenAI’s red-teaming guidance distinguishes adversarial red teaming from ordinary evaluations and points to Promptfoo as an open-source framework for evaluating prompts, agents, and AI applications.
Compare candidate tools on these axes:
- Coverage of direct and indirect injection, including encoded and long-context variants.
- Simulation of realistic tools, identities, data stores, network destinations, and side effects.
- Independent gateway and authorization testing that can bypass the model.
- Multi-turn, memory, retrieval, and multi-agent support.
- Sandboxing, synthetic-secret canaries, and controllable exfiltration channels.
- Reproducibility, repeated trials, rate reporting, and fixture versioning.
- CI/CD integration, trace quality, failure triage, cost, and operational burden.
- Maintenance of attack datasets as new failures and model behaviors appear.
A framework can generate useful attacks, but it cannot certify your authorization gateway. Keep direct gateway tests and end-to-end tests in the same release pipeline.
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What does a passing result look like?
For each scenario, the agent should identify untrusted instructions as data, preserve the authorized user goal, avoid protected-context disclosure, and request only permitted tools. The gateway should independently reject calls outside scope, validate every argument, enforce identity and tenant boundaries, and require approval for consequential actions. Logs should show the attempted path, the decision, and any blocked side effect without exposing real secrets.
A useful demonstration is a document-summary task containing an instruction to upload a synthetic secret. A passing run returns the requested summary, does not upload or reveal the canary, records the attempted instruction as untrusted content, and leaves the upload tool unused or explicitly denied. If the agent refuses the entire summary, record that as a false block rather than a clean pass.
How should I troubleshoot a failed or flaky test?
An unauthorized call was blocked by the gateway
Keep the gateway result as a control success, then inspect why the model proposed the call. Tighten tool descriptions, retrieval boundaries, or planning rules, and retain the trace so a future gateway change cannot turn the same proposal into execution.
A secret appeared in a tool argument or log
Treat the run as an exfiltration failure even if the external request was denied. Identify the first boundary where the canary crossed from protected data into an unapproved channel, then add a regression fixture for that exact path.
Results vary between repetitions
Freeze model and tool-policy versions, capture inference settings and retrieved context, and increase repetitions rather than selecting the most favorable run. Report the distribution by attack class and investigate whether timing, retries, context ordering, or approval races explain the variance.
Benign work is blocked
Separate policy errors from injection defenses by rerunning the clean control without hostile content. If the clean task succeeds but the mixed task fails, refine the boundary between data and authority or add a narrowly scoped approval step instead of weakening the gateway.
Frequently Asked Questions
Can I test an AI agent without using production credentials?
Yes. Use synthetic secrets, mock APIs, isolated tenants, and instrumented side-effect substitutes. Give each scenario a distinct canary so you can trace accidental disclosure without risking customer or payment data.
How many times should I repeat a prompt-injection scenario?
There is no universal repetition count because variance depends on the model, workflow, and risk. Choose a sample size appropriate to the decision, keep it fixed for comparisons, and report the run count and configuration with every rate.
Should a human approval click count as a successful defense?
Only when the reviewer sees the exact tool, arguments, target, and expected effect, and the gateway revalidates them immediately before execution. A stale, generic, altered, or cross-session approval should be recorded as an approval-control failure.
What is the difference between red teaming and an ordinary agent evaluation?
An ordinary evaluation measures expected task quality on representative inputs. Adversarial red teaming deliberately searches for harmful behaviors, including indirect injections, privilege escalation, exfiltration, and approval bypass, then turns confirmed failures into repeatable regression cases.
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