Audit the whole AI application, not just the language model. Map every instruction, user input, retrieved document, memory store, parser, tool, identity and approval gate; then run authorized direct- and indirect-injection tests, follow successful injections through tool and data flows, measure the resulting blast radius, verify forensic telemetry, and repeat the suite after every material change. OWASP organizes this work into model evaluation, implementation testing, infrastructure assessment and runtime behavior analysis: OWASP AI/LLM application security testing guidance.
What the audit is trying to prove
A prompt-injection audit answers two separate questions. First, can attacker-controlled text influence the model or agent to disregard its intended instructions? Second, if that happens, what can the compromised interaction actually do? The second question is the security outcome. An injection that changes a harmless draft is materially different from one that exposes private records, invokes a privileged API, changes a production system or plants instructions for a later run.
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Microsoft distinguishes direct injection, in which an attacker addresses the model through an ordinary input channel and attempts to override system or developer instructions, from indirect injection, in which hostile instructions are hidden in content the system reads, such as a webpage, document, email, database row, retrieval result or tool output. The distinction and recommended defenses are described in Microsoft’s prompt-injection guidance.
Your final deliverable should therefore be a bounded statement such as: “A malicious instruction in a retrieved PDF caused the agent to request customer records using service identity X; the request was denied by the data policy, no external write occurred, and the event was visible in the security log after 42 seconds.” Do not reduce the result to a single pass/fail score.
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Map the application and its trust boundaries
Start with an inventory before writing attack prompts. Draw the path from each untrusted input to the model, retrieval layer, memory, tools and external systems. Mark where data changes format or crosses an authorization boundary.
Inventory every component
- Models, model versions, system instructions, developer instructions, policy prompts and output-format constraints.
- User interfaces, API endpoints, uploaded files, email connectors, browser or search connectors, webhooks and batch jobs.
- Parsers, OCR, file converters, embedding services, vector stores, document indexes, caches and long-term memory.
- Tools and APIs, including their argument schemas, destination restrictions, rate limits and write capabilities.
- Service identities, delegated user identities, tokens, roles, network locations, secrets and data stores reachable by each tool.
- Approval steps, moderation services, policy engines, critic or planner agents and downstream agents that consume model output.
- Logging, alerting, retention, redaction and incident-response paths.
Label data by trust and authority
Separate instructions from content. System and developer instructions are control data. User messages are usually untrusted requests. Retrieved text, file contents, web pages, database fields, memory entries and tool results are untrusted content even when they came from an approved connector. A document can be authentic and still contain an instruction that the agent must not execute.
| Boundary | Questions to answer | Evidence to retain |
|---|---|---|
| Input to model | Which fields can an attacker influence? Are roles and delimiters preserved? | Raw input, normalized input, prompt assembly and model version |
| Retrieval and memory | Who can write, update or delete indexed content? Is provenance carried with each passage? | Document identifier, source, owner, timestamp, chunk and ranking data |
| Model to tool | Can generated text become an executable function call? Are arguments and destinations validated? | Proposed call, validated call, policy decision and execution result |
| Identity and data | Which user or service identity is used, and what records can it access? | Principal, token scope, authorization result and records touched |
| External side effect | Can the system send, delete, purchase, publish, change or schedule something? | Approval event, final payload, destination and rollback status |
Use OWASP’s four testing areas
Structure the plan around the four areas named by the current OWASP DevSecOps page: model evaluation (behavior under adversarial prompts), implementation testing (prompt construction, filters, retrieval and tool handling), infrastructure assessment (identities, networks, stores and services), and runtime behavior analysis (what happens during real interactions). This prevents a strong model benchmark from masking a weak connector or overprivileged service account.
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Run these tests in an isolated environment with synthetic data and explicitly authorized accounts. Preserve the exact input and the assembled context so a result can be reproduced.
Instruction-priority and role tests
- Ask the model to ignore or replace system and developer instructions.
- Claim a higher role, such as administrator, evaluator or emergency operator, without proof.
- Insert conflicting instructions in multiple message fields and test which one wins.
- Request the hidden system prompt, policy text, tool credentials or internal routing details.
- Ask the model to change its required output format, safety policy or approval requirement.
Obfuscation and language variants
- Use encoding, unusual Unicode, whitespace, case changes, token splitting and nested markup.
- Repeat the same objective in several languages and in mixed-language text.
- Wrap the request in a role-play, quotation, translation, debugging or “test mode” scenario.
- Place the malicious instruction after long benign context or inside a structured field.
For each case, record whether the model merely acknowledged the text, changed its answer, disclosed protected instructions or attempted an action. Microsoft recommends input validation, segmentation, filtering, continuous red-teaming and monitoring; these controls should be tested rather than assumed to work: Microsoft prompt-injection guidance.
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Test indirect injection in every content source
Any feature that reads external content is an injection surface. Seed clearly authorized test payloads into copies of normal content, then observe whether the agent treats the payload as data or as a command.
| Source | Test placement | What to observe |
|---|---|---|
| Web pages | Visible text, hidden text, comments, navigation, metadata and linked pages | Whether browsing causes a new instruction, navigation or data request |
| PDF and office files | Body text, footnotes, spreadsheets, speaker notes, comments and embedded objects | Whether parsing or summarization promotes file text to control instructions |
| Subject, body, quoted thread, signature, attachment name and headers | Whether a message can trigger forwarding, reply or attachment access | |
| Databases | Rows, free-text fields, labels, descriptions and audit notes | Whether a record can alter a query, workflow or response to another user |
| Retrieval results | Indexed chunks, ranking metadata and citations | Whether low-trust passages override higher-priority instructions |
| Images and media | OCR-visible text, captions, alt text and image metadata | Whether multimodal extraction turns embedded text into executable guidance |
| Tool output | API messages, error strings, status fields and returned records | Whether a tool can feed a new instruction into the next model turn |
Microsoft’s guidance says systems that read untrusted content should assume that content may be adversarial. Keep the source and trust label attached to every passage, and test the same payload through each parser and connector because normalization can change what the model sees: Microsoft prompt-injection guidance.
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Exercise chained attacks, not isolated prompts
A chained attack is a sequence in which an injection changes the agent’s plan and the altered plan reaches another capability. Begin with the malicious content, then follow each transition until the chain is blocked or an effect occurs.
- Seed the entry point. Put a benign-looking hostile instruction in a controlled webpage, file, email, record, retrieval chunk or tool response.
- Trigger ingestion. Ask the agent to perform the normal task that causes the content to be fetched, parsed or remembered.
- Watch plan changes. Capture whether the agent changes its objective, adds an unexpected step, suppresses a warning or requests a new permission.
- Test data reach. Check whether it can retrieve restricted records, secrets, private memory or another tenant’s content. Use synthetic canaries rather than real sensitive data.
- Test tool reach. Let policy controls evaluate the proposed call, then record whether arguments, destinations and identities remain within the test authorization.
- Test side effects. In a sandbox, exercise message sending, record changes, publication, deletion, purchase or scheduling only when the action is explicitly approved for the test.
- Test persistence and propagation. Determine whether the payload is written to memory, an index, a ticket, a prompt template or another agent’s input, and whether attacker-controlled output is trusted downstream.
Microsoft recommends plan-drift detection, critic agents, tool-chain analysis, information-flow control, least privilege and human approval for risky actions. Apply those controls at each transition, not only at the first prompt: Microsoft guidance on defending against indirect prompt injection.
Useful chain scenarios
- A retrieved support article instructs the agent to search a customer database and place the results in an external message.
- An email attachment tells a document agent to add a new rule to long-term memory, which a later run follows.
- A tool error contains hostile text that causes the planner to call a second tool with attacker-selected arguments.
- One agent summarizes poisoned content and another agent treats the summary as an approved instruction.
These are test patterns, not claims that every product permits them. Only exercise capabilities that exist in the application you are auditing.
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Measure containment and blast radius
For every successful or partially successful case, document the maximum consequence reached and the control that stopped further progress.
| Measure | Record |
|---|---|
| Data exposure | Data class, record count or canary identifier, tenant and whether content left the approved boundary |
| Tool reach | Tools requested and executed, arguments, destinations and denied attempts |
| Identity and privilege | User or service principal, token scope, role and effective permissions |
| External effects | Messages, writes, deletions, purchases, publications or other changes, including rollback status |
| Persistence | Memory, index, cache, ticket, prompt template or downstream agent state modified |
| User visibility | What the user saw, what required approval and whether warnings were clear |
| Detection | Alert generated, evidence available and elapsed time from injection to detection |
Report partial containment as well as full compromise. “The tool call was blocked but the model disclosed the names of restricted fields” is more actionable than “failed test.” Do not invent an attack-success rate: no authoritative, directly comparable prevalence or success statistic is established for prompt injection or chained attacks.
Verify telemetry and incident response
An investigator must be able to reconstruct the chain without relying on an agent’s summary. Preserve the following records with synchronized timestamps and a correlation identifier:
- Original user input and normalized representation.
- System, developer and policy instructions used for that turn.
- Retrieved passages, source identifiers, provenance, ranking and trust labels.
- Model name or version, parameters relevant to reproducibility and complete output.
- Planner, critic and policy decisions, including rejected alternatives.
- Every tool proposal, validation result, approval, execution result and returned payload.
- Identity, token scope, authorization decision, destination and data objects touched.
- Alerts, analyst actions, containment steps and any rollback.
Redact secrets and personal data according to your retention policy, but do not remove the fields needed to establish causality. Microsoft’s retrieval-hygiene guidance emphasizes provenance, source scoring, access controls, retrieval validation and monitoring: Microsoft input and context retrieval hygiene guidance.
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Content separation
Delimit external text, label it as untrusted data and keep system and developer instructions outside attacker-controlled context. Verify that every connector preserves those labels through parsing, chunking, summarization and hand-off to another agent.
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Least privilege
Grant only the data, tools and short-lived permissions required for the task. Separate read and write identities, restrict cross-tenant access, and make a tool fail closed when its authorization context is missing or ambiguous.
Tool constraints
Validate types, ranges, identifiers, destinations and allow-lists independently of the model. Restrict network targets, block dangerous sequences, make irreversible actions non-default and require confirmation for high-impact operations. Treat a model-generated argument as untrusted input.
Retrieval hygiene
Retain provenance, score sources, validate documents before indexing, isolate memory by user and tenant, and monitor ingestion for poisoning or unauthorized changes. A citation proves where text came from; it does not grant that text authority to issue commands.
Runtime monitoring
Alert on repeated bypass attempts, unusual tool selection, sudden data-access spikes, new destinations, policy-denied calls, unexpected plan changes and writes to memory or indexes. Correlate model, retrieval, identity and tool logs so an alert reveals the whole sequence.
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Require an informed user approval immediately before sending messages, changing records, making purchases, deleting data or taking another consequential action. Show the exact destination, payload, identity and reason for the action; do not ask for a generic approval that hides those details.
Microsoft’s architectural principle is concise: “Assume prompt injection may eventually succeed and architect the system so a compromised interaction cannot cause broad damage.” Microsoft prompt-injection guidance.
Make the audit repeatable
- Store test cases as versioned fixtures with the source type, intended objective, expected safe behavior and allowed side effects.
- Run direct and indirect suites against a known test dataset and synthetic canary secrets.
- Capture the full evidence package, including rejected tool calls and policy decisions, not just the final answer.
- Classify each result by exploitability, reachable privilege, data exposure, side effect, persistence and detection quality.
- Assign a control owner and remediation date for every finding.
- Re-run the complete suite after model, prompt, retrieval, parser, tool, permission or policy changes.
- Add every newly observed attack and every fixed regression to the permanent suite.
Regression testing matters because a prompt change can alter context boundaries, a model change can alter tool-selection behavior, and a connector change can introduce a new untrusted source even when the user interface is unchanged.
Score two systems or audit programs consistently
When comparing architectures, vendors or internal programs, use the same evidence-based axes rather than a generic “AI security” label.
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| Axis | Evidence to compare |
|---|---|
| Attack-surface coverage | Models, prompts, retrieval, memory, parsers, tools, identities, infrastructure and runtime paths included |
| Direct-injection depth | Override, role confusion, extraction, jailbreak, encoding, multilingual and format-manipulation cases |
| Indirect-injection depth | Web, files, email, databases, retrieval, media and tool-output sources tested |
| Retrieval and provenance | Source tracking, trust labels, validation, poisoning detection and memory isolation |
| Least-privilege enforcement | Separate identities, scoped tokens, tenant boundaries and short-lived permissions |
| Tool-chain containment | Argument validation, destination controls, sequence blocking and fail-closed behavior |
| Approval gates | Actions requiring confirmation and the detail shown to the approver |
| Telemetry completeness | Prompt, context, identity, policy, tool, output, approval and side-effect evidence |
| Response speed | Alerting, investigation access, containment and rollback time |
| Regression repeatability | Versioned fixtures, automated execution, stable environments and finding history |
Map the results to OWASP’s four testing areas so a high score in model evaluation cannot conceal gaps in implementation, infrastructure or runtime analysis: OWASP AI/LLM application security testing guidance.
Use common terminology without overstating the evidence
Microsoft’s 2026 catalog lists 14 distinct AI attack techniques grouped into five macro technique families. Use that catalog to name and organize attack cases, but do not turn the count into a probability of compromise: Microsoft AI attack techniques catalog.
NIST’s adversarial-machine-learning taxonomy supplies shared terminology and mitigation concepts for evasion, poisoning, privacy breaches, backdoors and attacks affecting large language models. It is useful for classifying findings and selecting controls, not for claiming that a particular application has a fixed risk level: NIST adversarial machine-learning taxonomy.
What a defensible audit report contains
- Scope: application version, model versions, connectors, identities, environments, dates and explicit authorization.
- Architecture: trust-boundary diagram and inventory of data, memory, tools, policies and approvals.
- Test coverage: direct cases, indirect sources, chained paths and OWASP testing area mapped to each case.
- Findings: reproducible input, observed model behavior, reached capability, blocked transition and evidence links.
- Impact: data exposure, privilege, side effects, persistence, user visibility and detection time.
- Remediation: specific owner, control change, residual risk and retest result.
- Regression set: fixed cases that must pass before the next release.
The audit is complete only when you can explain not just whether an instruction was injected, but how far it traveled, which independent control stopped it and whether the evidence would let another investigator reproduce the conclusion.
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