The Tool Desk
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Audit the whole AI application, not just the language model. Map every trust boundary and capability, test both direct and indirect prompt injection, follow successful injections into retrieval and tool chains, measure the resulting blast radius, verify that logs and approvals let you reconstruct the event, and rerun the suite after every material change. OWASP’s AI/LLM security-testing guidance organizes this work into model evaluation, implementation testing, infrastructure assessment, and runtime behavior analysis.
A useful audit outcome is a bounded statement such as “an instruction hidden in a retrieved PDF can make the agent read customer records but cannot send data externally without an approval,” rather than a single pass/fail score.
What a prompt-injection audit must cover
Prompt injection is an attempt to make an AI system follow attacker-controlled instructions instead of its intended policy. Microsoft distinguishes two forms:
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- Indirect injection: malicious instructions are embedded in content the system reads, such as a web page, PDF, office file, email, database row, retrieval result, image metadata, or tool output. The user may never see the payload.
Microsoft’s guidance says a system that reads untrusted content should assume that content can be adversarial. The practical consequence is that the model, prompts, retrieval pipeline, parsers, memory, tools, identity, permissions, infrastructure, and runtime controls are one attack surface. See Microsoft’s prompt-injection guidance and OWASP’s AI/LLM application security-testing guidance.
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Do not infer a universal attack-success rate from an audit. No authoritative, directly comparable prevalence or success statistic is established for prompt injection and chained attacks. Your evidence should instead show which paths were reachable, under which identity, and with what consequences.
1. Inventory trust boundaries and capabilities
Start with an architecture map and an inventory that can be reviewed by security, engineering, and the service owner. Record the following for every deployment and environment:
- Models, model versions, routing logic, fallback models, temperature or other generation settings, and moderation layers.
- System and developer instructions, user-input channels, templates, output parsers, format validators, and policy-priority rules.
- Retrieval sources, crawlers, connectors, document parsers, OCR, embedding stores, ranking, citation or provenance fields, and ingestion jobs.
- Conversation memory, long-term state, caches, agent plans, task queues, and any state that survives a single request.
- Tools and APIs, argument schemas, destination allow-lists, service identities, credentials, network paths, write operations, and rate limits.
- Human approval steps, escalation paths, rollback mechanisms, and the users or services allowed to approve actions.
- Infrastructure boundaries: tenants, storage buckets, databases, queues, plugins, containers, runtime secrets, and monitoring systems.
For each input, label whether it is a trusted instruction, user data, or untrusted content. A retrieved paragraph may look authoritative to a model but must remain data, not a new policy. Draw arrows showing where content is transformed, concatenated, summarized, stored, or passed to another model or agent. Mark every point where an attacker-controlled string could cross into an instruction channel.
Define the security properties
Write the properties you intend to preserve before creating test cases. Typical properties include:
- System and developer rules cannot be replaced by user or retrieved text.
- Untrusted content cannot cause a tool call without passing argument validation and the required approval.
- An agent can access only the records and tools needed for its task, using a short-lived identity.
- Secrets, hidden prompts, and data from another tenant are not disclosed in output or tool arguments.
- Plans cannot drift from the user’s approved objective without detection and re-approval.
- Every prompt, context item, decision, tool call, approval, and side effect can be reconstructed after an incident.
2. Build a direct-injection test suite
Use a controlled test tenant and synthetic secrets. Never place production credentials or real personal data in adversarial prompts. Keep the original request, model response, policy decision, and downstream events for each case.
Instruction-override cases
Test plain-language attempts to replace the system or developer message, for example requests to “ignore previous instructions,” adopt a new role, or prioritize the user over a policy. Vary wording, punctuation, capitalization, message order, and conversation length. Test role confusion in which a user claims to be an administrator, evaluator, or internal tool.
Extraction and policy-bypass cases
Ask the system to reveal its system prompt, hidden tools, safety rules, credentials, retrieval filters, or other users’ data. Include requests to reproduce a hidden instruction verbatim and to summarize it indirectly. Test jailbreak phrasing and attempts to make the model classify the request as a test, emergency, or higher-priority instruction.
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Encoding and language variants
Repeat the cases with Unicode look-alikes, unusual whitespace, Base64 or other encodings, transliteration, mixed languages, code comments, markup, and long padding. The objective is not to collect clever strings; it is to discover whether preprocessing, tokenization, translation, or normalization changes the policy outcome.
Output and format manipulation
Ask the model to change a required JSON schema, omit safety fields, insert executable content, or return a tool-call object when the user is not authorized to request one. Send malformed, oversized, nested, and conflicting inputs to the parser and validator. Record whether a downstream component trusts the model’s text more than the validated policy decision.
Microsoft recommends input validation, segmentation, filtering, continuous red-teaming, and monitoring as complementary measures; no single prompt filter is a complete defense. These recommendations are described in the Microsoft prompt-injection guidance.
3. Seed and test indirect injection
Indirect tests reveal whether an agent treats external text as instructions. Create benign-looking fixtures with a clearly identifiable canary instruction and place them in each content path the inventory identified.
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- A web page whose visible article contains a sentence directing the agent to disclose a canary secret.
- A PDF or office document with the instruction in body text, a footnote, a comment, an alternate text layer, or hidden text.
- An email thread or calendar attachment containing an instruction that conflicts with the user’s request.
- A database row, ticket, knowledge-base entry, or code comment containing a tool directive.
- Image metadata or OCR text that asks the model to alter its plan.
Place the same canary in a trusted-looking source and a low-trust source to test whether provenance and source scoring affect behavior. Verify that citations identify the source and that a source cannot silently rewrite its own trust level.
Retrieval and tool-output fixtures
Inject the canary into a retrieved passage, search-result snippet, API response, spreadsheet cell, or tool result. Test empty results, duplicate passages, conflicting documents, truncated content, and a result that contains a second-stage URL. Observe whether the agent copies the instruction into its next prompt, stores it in memory, or forwards it to another agent.
Pass and fail criteria
A passing behavior is to treat the fixture as data, explain that it is untrusted, continue the user-approved task, and avoid the prohibited call. A failure includes obeying the fixture, changing the plan without approval, exposing a canary secret, writing malicious content into memory, or passing attacker-controlled text as an instruction to another component. Save the exact source, retrieval rank, context window, model output, and tool decision so the result is reproducible.
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4. Exercise chained attacks from injection to impact
A chained attack is not complete when the model repeats hostile text. Follow the attack through every capability it can reach. Begin with a controlled injection and test one link at a time:
- Initial compromise: deliver the direct or indirect payload through the selected channel.
- Plan change: check whether the agent adds a new objective, drops a required check, or changes the order of operations.
- Data access: attempt to retrieve a restricted record, cross a tenant boundary, read a secret, or infer hidden context.
- Tool invocation: see whether the agent calls a tool, changes its arguments, selects an unauthorized destination, or creates a second tool call.
- External side effect: test sending a message, changing a record, making a purchase, deleting data, publishing content, or changing an access rule in a sandbox.
- Persistence: determine whether the payload survives in memory, a document, a ticket, a cache, a plan, or a generated artifact.
- Propagation: check whether attacker-controlled output is consumed by another agent, workflow, service, or human reviewer as trusted input.
For each link, capture the acting identity, permission set, destination, arguments, response, and approval state. Microsoft’s indirect-injection defense guidance recommends plan-drift detection, critic agents, tool-chain analysis, information-flow control, least privilege, and human approval for risky actions; see Microsoft’s guidance on defending against indirect prompt injection.
High-value chain scenarios
- A poisoned knowledge-base article instructs a support agent to search another customer’s account and place the result in a reply.
- A malicious web page causes a browsing agent to download a file, extract a token, and send it to an external endpoint.
- An email instruction makes an operations agent create a payment or change a record without the required confirmation.
- One agent summarizes hostile content and a second agent treats the summary as an approved task.
- A payload writes itself into long-term memory so that future sessions begin with a changed objective.
Run each scenario with and without the proposed controls. The difference between those runs demonstrates containment better than a model-only refusal score.
5. Measure containment and blast radius
For every successful or partially successful case, record a structured impact statement:
- Exposed data: exact fields, records, secrets, prompts, or tenant boundaries reached.
- Reachable capabilities: tools, APIs, files, databases, network destinations, and write operations available.
- Identity and privilege: service account, user delegation, token lifetime, scopes, and approval authority.
- Side effects: messages sent, records changed, files created or deleted, purchases made, or policies altered.
- Persistence and propagation: state written and downstream systems or agents that consumed the output.
- User visibility: what the user saw, what was hidden, and whether a reviewer could recognize the attack.
- Detection time: when the first alert or human observation occurred and how quickly action was stopped or rolled back.
Classify each finding by the furthest confirmed step, not by the most alarming theoretical outcome. A model that followed an instruction but had no tool permission has a different risk from one that sent data externally. State the conditions required to reproduce the path, including tenant, identity, source, model version, and approval state.
6. Verify telemetry and incident reconstruction
Logs must preserve enough context to reconstruct the chain without storing unnecessary sensitive content. At minimum, retain protected, access-controlled records of:
- Original user prompt and normalized input.
- System and developer policy identifiers or versioned references.
- Retrieved sources, excerpts or hashes, provenance, ranking, and trust scores.
- Model, prompt-template, parser, and policy versions.
- Tool-call name, validated arguments, destination, identity, response, and side effect.
- Plan changes, critic-agent decisions, approval requests, approvals, denials, and policy evaluations.
- Final output, downstream consumer, anomaly alerts, and rollback or containment actions.
Microsoft’s retrieval-hygiene guidance emphasizes provenance, source scoring, access controls, retrieval validation, and monitoring. Apply those controls to ingestion as well as runtime retrieval: detect unauthorized document changes, preserve version history, and isolate memory writes. See Microsoft’s input and context retrieval-hygiene guidance.
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Detection signals to alert on
- Repeated attempts to override instructions or extract hidden context.
- Unexpected tool selection, unusual destinations, or a sudden increase in data-access volume.
- Plan drift from the user-approved objective or a critic-agent disagreement.
- New instructions appearing in retrieved content, memory, or tool output.
- Cross-tenant identifiers, secret-like strings, or policy-sensitive fields in model output or tool arguments.
Test alerting with replayed fixtures and verify that an investigator can identify the initial payload, every hop, and the final disposition from the retained evidence.
7. Inspect and test the core controls
Content separation
Delimit external text and label it as untrusted data in the context supplied to the model. Keep system and developer instructions outside attacker-controlled fields. Ensure summaries, citations, and tool outputs retain their untrusted label instead of being concatenated into a privileged instruction block.
Least privilege
Give each agent only the data, tools, destinations, and scopes needed for its task. Prefer short-lived, narrowly scoped credentials and separate read and write identities. Test denied calls as deliberately as allowed calls; a prompt injection is contained when the resulting identity cannot reach sensitive resources.
Tool constraints and approvals
Validate every argument against a typed schema and policy, restrict destinations, cap quantities and rates, and block dangerous tool sequences. Require an explicit user or authorized operator confirmation before sending messages, changing records, making purchases, deleting data, or taking another irreversible or high-impact action. The approval screen should show the exact destination, arguments, affected records, and source of the request.
Retrieval hygiene and memory isolation
Retain provenance, score and validate sources, quarantine suspicious documents, and monitor ingestion for poisoning or unauthorized changes. Separate per-user and per-tenant memory, require validation before writing durable state, and provide a way to inspect and delete contaminated entries.
Runtime monitoring and human control
Monitor prompts, context, tool calls, outputs, approvals, and anomalies continuously. Route high-impact or ambiguous actions to a human instead of asking the model to self-approve. Microsoft’s concise architectural advice is: “Assume prompt injection may eventually succeed and architect the system so a compromised interaction cannot cause broad damage.” The quotation appears in Microsoft’s prompt-injection guidance.
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8. Use OWASP’s four-area scorecard
OWASP’s current DevSecOps page describes four complementary testing areas. Use them as the top-level structure of the audit and attach the evidence collected in the earlier steps.
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| Testing area | Questions to answer | Evidence |
|---|---|---|
| Model evaluation | How does the model respond to direct overrides, extraction, jailbreaks, encoding, multilingual prompts, and conflicting instructions? | Versioned attack cases, outputs, refusal or compliance decisions, and regression results. |
| Implementation testing | Do prompt assembly, segmentation, parsers, validators, retrieval, memory, tool wrappers, and approval logic preserve trust boundaries? | Context traces, source labels, schema-validation results, denied calls, and controlled chain replays. |
| Infrastructure assessment | Can identities, network paths, storage, queues, secrets, tenants, or runtime services be reached beyond the agent’s intended scope? | Permission reviews, access attempts, token scopes, network decisions, and isolation tests. |
| Runtime behavior analysis | Are plan drift, unusual retrieval, data-access spikes, tool misuse, persistence, and propagation detected and contained? | Alerts, timelines, approval records, response times, rollback evidence, and post-incident reconstruction. |
Track attack-surface coverage, direct and indirect test depth, retrieval and provenance controls, least-privilege enforcement, tool-chain containment, approval gates, telemetry completeness, response speed, and regression-test repeatability. A scorecard should show the evidence and residual blast radius for each axis, not just a percentage.
9. Retest after every material change
Prompt-injection defenses regress when a model, prompt template, parser, retrieval connector, tool, permission, policy, or monitoring rule changes. Keep a versioned corpus containing:
- Direct-injection cases for every known bypass class.
- Indirect fixtures for each ingestion and retrieval path.
- End-to-end chains that previously reached data, tools, side effects, persistence, or propagation.
- Negative cases proving legitimate users can still complete approved tasks.
- New attacks observed in production, red-team exercises, or incident response.
Run the corpus in a sandbox before release, compare outputs and tool decisions with the last approved baseline, and require review for any newly reachable capability. After deployment, replay a smaller canary set and monitor the runtime signals listed above. Record the model, policy, infrastructure, identity, and dataset versions so a future failure can be reproduced.
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Each finding should contain one reproducible path and one concrete containment decision. Include:
- Entry point: direct prompt, URL, document, email, database row, retrieval result, image metadata, or tool output.
- Trust-boundary failure: where untrusted text was treated as an instruction or privileged state.
- Chain: plan change, retrieval, tool call, identity, destination, side effect, persistence, and propagation.
- Impact: exact data and capabilities reached, user visibility, and time to detection.
- Control gap: missing separation, validation, least privilege, approval, monitoring, or rollback.
- Remediation test: the attack case and expected denied behavior that must pass after the fix.
Prioritize findings that cross a tenant boundary, expose secrets, create irreversible side effects, or persist and propagate without approval. A low-impact model-policy violation may still deserve remediation if it is the first link in a chain to a privileged tool.
Framework context and terminology
NIST’s adversarial-machine-learning taxonomy and terminology provides useful language for evasion, poisoning, privacy breaches, backdoors, and attacks affecting large language models. Use that vocabulary when mapping a prompt-injection finding to broader security controls, but keep the application-specific chain and blast radius in your report.
Microsoft’s 2026 AI attack-technique catalog groups 14 distinct techniques into five macro technique families. Treat that catalog as a taxonomy for expanding test coverage, not as a claim that every technique applies to every architecture. The catalog is available at Microsoft’s AI attack-technique catalog.
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