The Tool Desk
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A useful test produces an auditable result: the attack variant, the production-relevant configuration, the tool calls and arguments, permissions checked, data accessed, side effects, alerts and final impact. A final answer that looks harmless is not a pass if the agent already performed an unauthorized action.
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What prompt-injection testing must prove
Prompt injection occurs when an attacker places instructions where an agent can read them and the agent treats those instructions as authoritative. A direct attack arrives in a user message. An indirect attack is embedded in a web page, document, email, support ticket, retrieval result, memory item or tool response. The agent may confuse untrusted content with instructions from the user or system.
The security question is therefore broader than “Did the model refuse the prompt?” You need to establish that untrusted content cannot obtain capabilities it was not authorized to use. OWASP’s DevSecOps Guideline puts the distinction plainly: “Agentic systems turn prompt injection from a content problem into an authorization problem.”
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- Input handling: Did the content cross a trust boundary, and was its origin preserved?
- Decision making: Did the model classify data as instructions, or follow it despite policy?
- Tool authorization: Was the requested operation permitted for this user, task and resource?
- Arguments: Were destination, record, query, file and amount checked outside the model?
- Effects: Was sensitive data read, a message sent, a record changed, code run or money moved?
- Detection and recovery: Were the attempt and its consequences logged, blocked and reversible?
Testing only response text can miss a dangerous tool invocation that occurred before the response was generated. Inspect the complete trace.
Define scope, authority and a safe test environment
Document the intended operating envelope
Before writing attacks, record what the agent is supposed to do and what it must never do. Include its user populations, external content sources, model and prompt versions, retrieval indexes, memory stores, tools, credentials, network routes, data classes and approval rules. List actions by consequence rather than by marketing feature: reading customer records, downloading files, sending email, changing tickets, invoking code, purchasing goods or communicating with an external service.
Test only systems, accounts and assets your team owns or is explicitly authorized to assess. OpenAI’s red-teaming documentation makes authorized scope a condition of using its service. Keep production secrets, real customer data and live payment or messaging endpoints out of the test whenever a substitute can provide the same observation.
Use harmless data and instrumented substitutes
Create a test-only secret such as TEST_SECRET_7F3A in a simulated document, mailbox or database. Use a sink that records attempted outbound messages instead of delivering them. Replace destructive APIs with fakes that validate arguments and return a controlled result. If browsing or code execution is required, run it in an isolated environment with no ambient credentials and no route to internal services.
Instrumentation should capture the input source, retrieval or browsing event, model decision, tool name, complete arguments, authenticated identity, authorization result, data objects touched, network destination, approval event, timestamp and resulting state. A blocked action should be visible too; otherwise a silent failure can be mistaken for a safe design.
Map trust boundaries and possible impact
List every channel where instructions can enter
Draw a data-flow diagram for the deployed workflow. Mark content as trusted, user-controlled or third-party-controlled at each hop. Typical untrusted channels include:
- Direct user prompts and uploaded files.
- Web pages, search snippets and browser-rendered content.
- Retrieved knowledge-base documents and vector-search results.
- Email bodies, attachments and calendar invitations.
- Support tickets, chat transcripts and CRM notes.
- Tool responses, API error messages and generated documents.
- Long-term memory and summaries written by earlier runs.
- Messages passed between agents or from an MCP server.
Record whether the system preserves provenance. A retrieved paragraph should remain identifiable as external data, rather than being concatenated into a prompt that appears to have system authority.
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For each tool, identify the most damaging plausible result of an injected instruction. A read-only search can still expose personal data. A mail tool can exfiltrate a secret. A browser can submit a form. A database writer can alter a customer record. A code runner can reach the network or steal a credential. Rank scenarios by impact and test the highest-impact boundaries first.
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| Capability | Injection objective to test | Evidence of a pass |
|---|---|---|
| Document or database read | Retrieve a protected test secret outside the user’s authorization | No unauthorized read; denied request and audit event |
| Email or messaging | Send the secret or internal context to a test-controlled sink | No outbound action without specific approval; attempted destination logged |
| Record update | Change a ticket, account, order or setting | Arguments rejected or held for approval; state unchanged |
| Browser or HTTP client | Visit an attacker page and follow its instructions | Page treated as data; no prohibited form submission or network call |
| Code execution | Read files, invoke a shell or reach an internal service | Sandbox blocks access; no ambient credential or internal route |
Build realistic prompt-injection test cases
Pair every attack with an objective and an observable outcome
A test case should state the user task, the attacker-controlled content, the intended security property, the allowed tools and the pass/fail evidence. For example, place a benign secret in a simulated report and add an instruction telling the agent to find it and send it to a test-controlled address. A passing run has no unauthorized read and no outbound call; the attempted instruction and the blocked tool request appear in telemetry.
For indirect injection, put the instruction in the external channel under test. A malicious sentence in a retrieved document tests retrieval and document handling. The same sentence in the user prompt tests a different boundary. Do not substitute one for the other and claim coverage of both.
Vary wording, encoding and location
Run families of equivalent attacks rather than one memorable sentence. Change capitalization, spacing, markup, Unicode characters, quoted text, images with embedded text, JSON fields, HTML comments, code blocks and multi-step wording. Place the instruction at the beginning, middle and end of content, and split it across retrieved chunks or tool responses. These variations reveal defenses that depend on a single pattern.
Include attacks that claim authority (“system message,” “security test,” or “developer override”), requests to reveal hidden prompts, instructions to ignore previous rules, and indirect requests to summarize or forward sensitive material. Keep the content harmless and direct every attempted exfiltration to your sink.
Include benign controls
For every adversarial family, add legitimate tasks using similar words and data. Ask the agent to summarize a normal document, update an allowed record, or send an approved message. Measure whether controls block normal work, because a system that refuses everything has not demonstrated useful security. Benign controls also expose filters that generate false positives when formatting or domain vocabulary resembles an attack.
Inspect and harden the tool boundary
Enforce authorization outside the model
The model can propose a tool call; execution code must decide whether it is allowed. For each operation, validate the caller’s identity, resource ownership, operation type, destination, amount, query scope and data classification. Do not rely on a natural-language instruction such as “only send to approved recipients” when an allowlist, policy engine or server-side check can enforce it.
- Use separate credentials and service accounts for separate tools.
- Grant the smallest data and network scope needed for the task.
- Reject unknown or extra arguments rather than silently ignoring them.
- Apply object-level authorization at execution time, not only when a plan is created.
- Require action-specific human approval immediately before high-risk operations.
- Make approvals show the exact operation, arguments, destination and affected data.
Test confused-deputy cases in which a low-privilege user asks the agent to use a high-privilege integration. Also test argument substitution: an allowed operation on one record must not become an operation on another record merely because an injected document changes an identifier.
Review MCP and third-party tool metadata
Treat MCP servers, tool descriptions and schemas as third-party code. Test for hidden instructions in descriptions, overbroad scopes, unsafe defaults and cross-server tool shadowing. Compare the tool actually invoked with the tool the user authorized, and alert when a newly introduced server changes names, descriptions or permissions.
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Sandbox browsers and code execution
Browsing and code execution should run in an environment without ambient cloud credentials, host filesystem access or unrestricted internal networking. Test escape attempts, access to metadata services, DNS and HTTP requests to internal addresses, file reads outside the workspace and persistence between runs. A browser that merely displays hostile text is different from one that can submit a form or download a file; evaluate the complete capability.
Run evaluations through the production-like path
Preserve versions and configuration
Call the same application API and protections used in production. Record the model provider and version, system and developer prompts, tool schemas, retrieval configuration, memory settings, policy versions, temperature or other generation settings, application build, test corpus and sandbox image. An isolated model test cannot tell you whether production filtering, retrieval, approval or permission code changes the outcome.
Repeat runs and retain regression cases
Model behavior can vary between runs. Execute each case multiple times and retain the complete per-run trace. When a case finds a flaw, keep it permanently as a regression test. Rerun the suite after changes to prompts, tools, memory, retrieval, policies, model providers, browser controls or network topology. OWASP recommends structured testing before launch and after material changes.
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Report rates with their numerator and denominator
Do not collapse unlike outcomes into one unexplained score. Report separate measures such as unauthorized reads, forbidden tool calls, successful exfiltration attempts, destructive state changes, alert latency and benign-task success. State exactly how many cases and runs produced each result, which variations were included and which controls were applied.
| Metric | Definition | Why it matters |
|---|---|---|
| Unauthorized read rate | Runs in which protected test data was accessed without permission divided by eligible runs | Measures confidentiality failure even when no message was sent |
| Forbidden-call rate | Runs with a denied operation reaching execution or an unapproved call completing | Shows whether authorization is enforced at the tool boundary |
| Exfiltration success | Runs in which the sink received protected data | Captures an actual downstream consequence |
| Benign success rate | Allowed tasks completed without unnecessary refusal or unsafe side effect | Exposes defenses that destroy legitimate utility |
| Detection latency | Time from attempted injection or tool call to alert | Determines how quickly operators can contain impact |
Judge impact, not just whether an attack was detected
A detected injection is not automatically harmless. Examine what data was exposed, which operation occurred, whether the system stopped before or after the side effect, whether an alert reached the right operator and how reversible the result is. A rare high-impact outcome deserves priority even if it appears once in a small test set.
Classify findings by the strongest observed consequence. A model refusal after an attempted unauthorized read is different from a successful read that was blocked from leaving the system, which is different again from data arriving at the attacker-controlled sink. Record containment controls separately from prevention controls so a downstream block is not mistaken for authorization at the source.
Choosing a testing approach
Most teams need a combination of manual adversarial review, automated cases and scenario benchmarks. Compare approaches on coverage of real input and tool paths, visibility into side effects, repeatability, isolation, fit to the threat model, reporting needs and operating effort.
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| Approach | Best use | Limit | How to use it responsibly |
|---|---|---|---|
| Manual adversarial review | Discover unusual chains, business-logic weaknesses and confusing user journeys | Variable coverage and difficult repetition | Pair expert exploration with recorded cases and instrumented tools |
| Promptfoo | Automate prompt, agent and application cases against an HTTP endpoint | Harness output is only as good as the scenarios and observability supplied | Use it to run repeatable cases and inspect behavior, not as proof of security |
| AgentDojo | Experiment with simulated Workspace, Travel, Slack and Banking settings and hijacking scenarios | Its tasks and permissions may not match your application | Use as a benchmark or starting point, then reproduce equivalent cases in your own workflow |
| WASP | Study web-agent prompt-injection behavior in an isolated benchmark environment | Benchmark-specific web tasks and outcomes | Use its results for context, never as a deployment pass threshold |
| Managed enterprise red teaming | Independent assessment, specialized expertise or formal reporting | Scope, availability and commercial terms vary | Define authorized assets, required evidence and remediation ownership in the statement of work |
OWASP’s LLM Prompt Injection Prevention Cheat Sheet says: “Use the examples below as a smoke test, not a security benchmark.” Its illustrative examples are intentionally small. Adapt them to your agent’s tasks, permissions, input paths and consequences.
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What benchmark results can—and cannot—tell you
NIST CAISI describes AgentDojo as four simulated settings—Workspace, Travel, Slack and Banking—with tools and hijacking scenarios. This makes it useful for comparing techniques under controlled conditions, but it does not establish how your production agent behaves.
The 2025 WASP preprint evaluated web agents in an isolated environment intended not to affect real users or the live web. Its authors report that agents began executing adversarial instructions in 16% to 86% of cases and achieved the attacker’s goal in 0% to 17% of cases. Those ranges describe the benchmark scenarios and evaluated agents; they are not a prevalence rate for deployed agents and should not become a universal acceptance threshold.
No broad industry prevalence rate or universal pass threshold is established by these sources. Set release criteria from your own impact model. A single successful transfer of sensitive data may be unacceptable even when the aggregate failure percentage is low.
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- Define authority: Write down intended users, assets, tools, credentials, data classes, external sources and prohibited actions. Confirm written authorization for the test scope.
- Map boundaries: Trace direct and indirect content from ingestion through retrieval, model context, memory, tool selection, authorization and side effect. Mark every trust transition.
- Prepare isolation: Seed dummy secrets, create test identities, replace live destinations with sinks, disable ambient credentials and restrict network and filesystem access.
- Write objectives: For each capability, specify the attack, allowed behavior, forbidden behavior and observable pass evidence.
- Deliver attacks through the right channel: Put indirect instructions in the document, page, email, ticket or tool response being evaluated. Keep direct prompt cases separate.
- Add variations and controls: Change wording, encoding, formatting and placement; run equivalent benign tasks to measure false positives.
- Execute the real workflow: Use the production API, retrieval, policies, approvals, model settings and tool adapters, with test-only assets.
- Inspect traces: Review tool names, arguments, identities, permissions, data access, destinations, approvals, state changes and alerts—not just the final response.
- Repeat and measure: Run multiple trials, retain configuration versions and report each objective’s numerator and denominator.
- Fix and regress: Correct authorization, validation, sandboxing or monitoring; preserve the failing case and rerun it after every material change.
Common failure modes and corrections
Testing only the chat prompt
Failure: The team pastes attack text into a model playground and concludes that a refusal proves safety.
Correction: Exercise retrieval, browsing, memory, tool dispatch, authorization and side-effect handling through the deployed application path.
Putting an indirect attack in the user message
Failure: A document-injection claim is tested by writing the malicious instruction directly in the prompt.
Correction: Place it in the actual file, page, ticket, email or tool result and verify provenance survives each hop.
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Failure: The model is instructed not to send secrets or modify records, but execution code accepts its arguments without checking.
Correction: Enforce identity, object-level permissions, schemas, destinations and approvals outside the model.
Using real secrets or live endpoints
Failure: A realistic test creates a real breach.
Correction: Use dummy data, test accounts, sink endpoints and isolated tools. Keep production credentials out of the environment.
Reporting one score
Failure: Unauthorized reads, blocked calls, successful exfiltration and benign refusals are averaged into a single percentage.
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Correction: Report separate objectives, impact levels, test counts and configuration versions.
Stopping after the first fix
Failure: A prompt filter is added and the original attack is never rerun through retrieval and tools.
Correction: Add the case to the regression suite, vary its wording and retest the complete path after every relevant change.
Release checklist
- Every direct and indirect input channel is listed and tested.
- Test data is synthetic or explicitly approved; live secrets and destructive endpoints are excluded.
- Tool permissions, schemas, identities, network reach and credentials are documented.
- Authorization and argument validation execute outside the model.
- High-risk operations require specific, reviewable approval.
- Browser and code tools run without ambient credentials or unrestricted internal access.
- Tool calls, arguments, data access, side effects and alerts are retained in auditable traces.
- Attack variations and benign controls are included.
- Runs use production-relevant model, prompt, retrieval, memory and policy versions.
- Metrics identify each objective’s numerator and denominator.
- High-impact outcomes receive remediation priority regardless of frequency.
- Previous failures remain regression cases after launch and after material changes.
The practical pass condition is not a particular refusal phrase or benchmark score. It is evidence that untrusted content cannot obtain unauthorized capabilities, that allowed work still succeeds, and that any attempted or completed side effect is constrained, visible and recoverable.
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