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Configure the model as a governed, least-privilege test subject—not as an unrestricted administrator. Written authorization and rules of engagement come first; then use a staging target, synthetic or minimized data, isolated tools, separate credentials, explicit egress controls, human approval for destructive actions, strict run limits, and complete evidence capture.
Start with authorization and a precise test boundary
An AI penetration test is authorized only when the system owner has approved the activity in writing. Authorization should identify the legal entity, accountable owner, testing team, and exact assets that may be touched. Treat the model, prompts, memory, retrieval systems, tools, credentials, orchestration code, hosting layer, logs, and downstream services as one connected attack surface.
NIST describes the AI Risk Management Framework (AI RMF) as voluntary guidance for incorporating trustworthiness considerations into the design, development, use, and evaluation of AI systems. Its four functions are Govern, Map, Measure, and Manage. Use that structure to organize the engagement, but do not represent the framework as a mandatory certification or checklist.
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Write the rules of engagement before creating credentials
- In-scope assets: list model identifiers or endpoints, applications, prompt templates, conversation memory, retrieval indexes, vector databases, plug-ins, MCP servers, APIs, orchestration services, logging systems, and downstream applications.
- Permitted techniques: state whether direct and indirect prompt injection, poisoned retrieval documents, tool-abuse attempts, cross-tenant tests, rate tests, data-exfiltration simulations, and code-execution probes are allowed.
- Test window: specify start and end times, time zone, maintenance overlaps, and blackout periods.
- Network boundaries: record approved source and destination IP ranges, domains, ports, and egress routes. Identify services that must never receive test traffic.
- Data rules: define whether production data is prohibited, what synthetic or minimized data may be used, retention duration, encryption requirements, and who may view attack corpora and results.
- Contacts: name the system owner, technical operator, security lead, incident commander, and an emergency contact who can revoke access.
- Stop conditions: document triggers such as unexpected production access, exposure of real personal or secret data, uncontrolled outbound traffic, privilege escalation, runaway resource consumption, or a service-impacting error.
- Approval authority: identify who can authorize a destructive, irreversible, financial, messaging, deletion, or privilege-changing action. The model must not be able to grant itself that authority.
Keep the signed authorization with the configuration and test results. If the model, tools, target environment, or test techniques change materially, obtain an updated approval rather than relying on an old scope statement.
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Inventory the complete AI attack surface
Build an asset and data-flow inventory before writing probes. For each component, record its owner, environment, identity, reachable resources, input and output formats, and logging path.
Model and instruction layers
- Record the model name, provider or hosting runtime, exact version or revision, context-window settings, system prompt, developer prompt, safety policies, and conversation-memory behavior.
- Capture routing logic if different requests use different models, tools, or policies.
- Document whether user input, retrieved documents, tool output, or prior conversation content can influence instructions.
Retrieval and knowledge sources
- List document stores, vector databases, embedding models, index-build jobs, metadata filters, tenant identifiers, and authorization checks.
- Identify who can add, modify, delete, or re-index documents. Include ingestion from uploads, web pages, tickets, email, and third-party feeds.
- Trace the path from a retrieved chunk to the model prompt and then to any output or tool call. This reveals where a poisoned document or missing tenant filter could cross a security boundary.
Tools, connectors, and downstream systems
Inventory every function the model can call, including shell or code execution, browsers, HTTP clients, databases, cloud APIs, ticketing, email, messaging, file storage, and MCP servers. For each tool, document allowed methods, resource identifiers, authentication mechanism, input validation, output handling, and whether the action is reversible. Include the orchestration code and hosting layer; a safe prompt cannot compensate for an over-privileged connector.
Use a non-production target
Point the model and every connector at a staging environment designed for testing. A separate endpoint, account set, database, queue, object store, and monitoring workspace prevents a successful probe from reaching live business systems.
Data and identity separation
- Use synthetic records or the smallest minimized dataset that can answer the test question. Do not copy production secrets, tokens, personal records, or customer conversations into the test environment.
- Create separate identities and credentials for the model, the red-team harness, human reviewers, and each tool. Give the test identity a distinctive name so audit logs are unambiguous.
- Set short credential lifetimes where possible, rotate them after the engagement, and revoke all access when testing ends.
Network isolation
- Route test traffic through an explicitly allow-listed network segment or proxy.
- Block cloud metadata endpoints, internal administrative interfaces, secret stores, production databases, and unneeded east-west paths.
- Apply egress filtering to domains, ports, protocols, and destination addresses. Log denied and permitted connections.
- Separate the browser and code-execution sandbox from internal services. A browsing tool should not be able to pivot into the orchestration host.
Validate isolation with an ordinary, non-destructive connectivity check before sending adversarial prompts. The expected result is that permitted staging services respond and protected destinations do not.
Apply least privilege to every model action
Make read-only access the default. Scope each function to the resources required for the stated test, preferably through the test user’s identity rather than a shared administrator account. The model should receive only the tool descriptions and parameters it needs; hiding a dangerous function in documentation is not an access control.
Separate read, write, and irreversible capabilities
- Read operations: limit queries to seeded test records, test tenants, and approved fields. Mask secrets and unnecessary personal data in responses.
- Reversible writes: direct updates to disposable staging objects and require schema and destination validation.
- Irreversible or high-impact actions: place deletion, privilege changes, money movement, external messaging, account changes, production deployment, and bulk modification behind a human approval gate.
Use deny-by-default policies for methods and resource identifiers. Validate arguments before execution, enforce server-side authorization after the model call, and return only the minimum output needed for the next step. A prompt that says “never delete” is not an adequate substitute for a policy that makes deletion impossible without approval.
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Design approval gates that a person can understand
When a proposed action crosses a boundary, pause the run and show the reviewer the tool name, exact arguments, target resource, expected effect, data involved, and rollback plan. Do not ask for a vague “continue?” confirmation. Record the reviewer identity, decision, timestamp, and any changed arguments. A rejection must terminate or safely redirect the action rather than causing the model to retry through another tool.
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Run code execution, file handling, and browsing in isolated sandboxes with restricted CPU, memory, filesystem, process, and network access. Mount only disposable working directories. Block access to host sockets, credential files, environment variables containing secrets, cloud metadata, and internal DNS unless a specific test requires them and the rules of engagement allow it.
Pin the model revision, system and developer prompts, tool schemas, policy files, retrieval corpus, embedding model, evaluator version, harness code, and dependency versions. Save a configuration hash with each run. Without pinned inputs, a later result may differ because the model, retrieval index, tool description, or evaluator changed rather than because a fix worked.
Set operational limits before the first adversarial run
Define maximum turns, token budgets, recursion depth, concurrent jobs, request rate, tool-call count, wall-clock duration, and spend. Set limits per run and per test campaign so a loop cannot consume the entire project budget or overload a staging service.
- Alert on repeated tool failures, repeated authorization denials, unexpectedly long chains, unusual token growth, or destinations outside the allow-list.
- Stop the run automatically when a stop condition is met, an egress rule is violated, or a resource threshold is exceeded.
- Keep a human-controlled kill switch independent of the model and its orchestration process.
- Test the stop path itself with a harmless trigger before running high-risk cases. Verify that queued jobs, child processes, credentials, and outbound connections are actually terminated.
Make observability complete and tamper-resistant
Capture enough context to reproduce a finding without guessing. At minimum, retain:
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- Every user input, retrieved document or chunk, tool description, tool argument, tool result, approval decision, model output, and evaluator judgment.
- Authentication principal, effective permissions, target resource, timestamps, request identifiers, and network events.
- Sandbox events such as process creation, filesystem access, denied requests, outbound connections, and resource-limit triggers.
Protect attack prompts, retrieved content, logs, and reports under classification rules at least as strict as those for the environment being tested. Restrict who can export them, encrypt them in transit and at rest, and define retention and deletion dates. Logging an entire secret to prove that the model disclosed it can create a second incident; redact or tokenize sensitive values while preserving enough evidence to establish what happened.
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Build the test corpus around real AI failure modes
Use OWASP’s LLM risk categories as the minimum corpus, then add cases that match the system’s actual tools and data flows. Combine automated probes with human review; a fixed suite will miss failures that depend on context, sequencing, or business rules.
Prompt injection
Test direct attacks in user messages and indirect attacks embedded in retrieved documents, web pages, tickets, images, or tool output. Check whether untrusted content can override system instructions, alter the task, reveal hidden context, or cause an unauthorized function call. Agentic systems make this an authorization problem as well as a content problem, because a successful injection can operate through the agent’s permissions.
Sensitive-information disclosure
Probe for system-prompt leakage, credentials in tool output, cross-user conversation memory, hidden retrieval documents, training or evaluation artifacts, and data returned through error messages. Include attempts to obtain information by asking for summaries, transformations, encodings, translations, or indirect confirmations rather than only direct “print the secret” requests.
Improper output handling
Send model output into every real sink used by the application: browser markup, shell commands, SQL, templates, file paths, Markdown renderers, and API parameters. Verify context-appropriate encoding, parameterization, allow-lists, and validation at the sink. Treat model output as untrusted data even when the model is instructed to produce a safe format.
Excessive agency
Attempt multi-step actions that combine individually harmless tools into a harmful result: finding a target, collecting data, changing an account, and sending a message. Check whether the agent can select a broader resource than the user authorized, bypass an approval step, or continue after a denial.
Retrieval, vector, and embedding weaknesses
- Use poisoned documents and conflicting instructions to test ingestion and retrieval trust.
- Attempt cross-tenant retrieval, metadata-filter bypass, nearest-neighbor leakage, and unauthorized document deletion or re-indexing.
- Check whether embedding or index updates are authenticated, reviewed, logged, and reversible.
Unbounded consumption
Test long prompts, recursive tool chains, repeated retries, large retrieval results, expensive model routes, and concurrent requests. Confirm that rate limits, budgets, timeouts, queue limits, and circuit breakers stop both accidental loops and deliberate exhaustion.
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Channel-specific exfiltration
Where the system exposes these channels, test links, images, HTTP requests, email, messages, files, and logs as possible exfiltration paths. A response that contains no secret in the chat transcript may still leak it through a URL, image request, outbound API call, attachment, notification, or verbose log entry.
Cover all four red-team perspectives
Organize the campaign into four complementary areas identified in OWASP’s red-team structure:
- Model evaluation: measure refusal behavior, instruction hierarchy, data disclosure, robustness to adversarial phrasing, and consistency across repeated runs.
- Implementation testing: inspect prompt construction, retrieval authorization, output encoding, tool schemas, approval logic, memory isolation, and error handling.
- Infrastructure assessment: examine identity, network segmentation, sandbox escape resistance, secrets management, logging, dependency exposure, and deployment configuration.
- Runtime behavior analysis: observe live tool selection, sequencing, retries, policy decisions, rate controls, egress, and human-approval interactions under realistic workloads.
Use automated harnesses for repeatable input generation and regression checks, but require a human to review ambiguous disclosures, business-logic impact, and chains of individually low-risk actions.
Run the engagement in controlled stages
- Baseline the target. Confirm the model revision, configuration hash, seeded data, reachable endpoints, expected logs, and normal resource use.
- Exercise low-risk probes. Begin with read-only prompt-injection, disclosure, retrieval-isolation, and output-handling cases. Confirm that telemetry and stop controls work before escalating.
- Test tool boundaries. Try unauthorized methods, resources, tenants, destinations, and argument types. Verify that server-side policy rejects them even when the model requests them confidently.
- Test chained behavior. Combine allowed functions to look for excessive agency, approval bypass, indirect injection, and cross-channel exfiltration.
- Test resource controls. Run bounded consumption cases within the approved window and verify rate, token, concurrency, timeout, and spend controls.
- Escalate only with approval. If a destructive or irreversible case is in scope, obtain the named human approval for that specific action and capture the decision before execution.
- Terminate and clean up. Revoke test credentials, remove seeded attack artifacts, stop jobs and sandboxes, close temporary network rules, and verify that no scheduled task or queued message remains.
- Retest fixes. Re-run the exact reproduction with the same pinned versions and corpus, then add a regression case so the defect remains covered.
Set deployment gates before execution. The gate should state which findings block release, which require documented risk acceptance, who can accept residual risk, and what evidence is required to close a finding. Do not invent a universal pass rate: thresholds depend on the system’s data sensitivity, tool authority, exposure, and business impact.
Record findings so another team can reproduce them
Each finding should stand alone. Retain:
- Exact input sequence, including the first prompt, injected content, retrieved context, and any intermediate tool results.
- Model, tool, evaluator, prompt, policy, corpus, and configuration versions.
- Effective identity and permissions at each step, target asset, network path, and approval state.
- Observed output or action, including whether data was displayed, stored, transmitted, changed, or deleted.
- Reproduction steps, expected safe behavior, actual behavior, and evidence artifacts such as sanitized logs, screenshots, traces, or network records.
- Severity rationale that explains confidentiality, integrity, availability, safety, tenant, financial, or regulatory impact.
- Remediation owner, due date, compensating controls, retest result, and residual-risk decision.
Map technical issues to OWASP LLM risks and, where useful, MITRE ATLAS techniques. Use OWASP AISVS as a verification catalog when you need testable control language. OWASP describes AISVS 1.0 as vendor-neutral, with 191 requirements across 12 chapters and three appendices, organized into verification levels 1, 2, and 3.
Use the frameworks for different decisions
| Framework or catalog | Best use in an AI penetration test | Published detail |
|---|---|---|
| NIST AI RMF 1.0 | Governance, risk framing, measurement, and residual-risk decisions across the AI lifecycle | Released January 26, 2023; voluntary; functions are Govern, Map, Measure, and Manage |
| NIST Generative AI Profile | Additional risk considerations for generative-AI systems | NIST-AI-600-1, released July 26, 2024 |
| OWASP LLM risk categories | Minimum adversarial corpus and security-language alignment | Covers prompt injection, disclosure, improper output handling, excessive agency, prompt leakage, vector and embedding weaknesses, and unbounded consumption |
| OWASP AISVS 1.0 | Testable verification requirements for AI and LLM applications | 191 requirements across 12 chapters and three appendices; verification levels 1, 2, and 3; OWASP Foundation, 2026 |
| MITRE ATLAS | Adversary-technique mapping for threat-informed reporting | Use where its techniques clarify attack paths or defensive coverage |
Compare two proposed configurations before choosing one
If two teams propose different AI pentest setups, score both against the same questions rather than comparing model names alone:
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- Does each have a signed scope with precise assets, techniques, windows, contacts, and stop conditions?
- Are tools and credentials limited to the test identity, staging resources, and required methods?
- Are code execution, browsing, internal routes, metadata endpoints, and secret stores isolated and explicitly controlled?
- Does coverage include model behavior, implementation, infrastructure, runtime chains, retrieval authorization, and cross-tenant isolation?
- Can an independent reviewer reconstruct every prompt, retrieved item, tool argument, approval, output, and network event?
- Are destructive actions gated by a human with a documented rollback and an auditable decision?
- Are model, tool, evaluator, corpus, and policy versions pinned so regression results are comparable?
- Are findings mapped to a recognized risk or verification catalog and assigned an owner and retest plan?
A configuration that scores well on model probing but permits broad credentials, uncontrolled egress, or incomplete logs is not a safe penetration-test configuration. Authorization precision, containment, and evidence quality are security controls, not administrative extras.
Troubleshoot common failures
The model reaches a protected service
Stop the run, revoke the affected credential, preserve network and tool logs, and confirm whether any request succeeded. Inspect routing, proxy allow-lists, DNS resolution, cloud metadata blocking, and inherited environment credentials. Recreate the staging identity with narrower permissions before resuming.
The model keeps retrying after a denial
Check whether the orchestrator treats a denied tool call as a normal recoverable error. Add a hard policy that terminates or pauses the run after defined denial patterns, and ensure alternate tools cannot reach the same resource. Record each retry because repeated bypass attempts may reveal excessive agency.
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Compare the captured trace with the required evidence list. Missing retrieved context, tool arguments, effective identity, or network events prevents reliable severity assessment and retesting. Add correlation IDs across the model gateway, orchestrator, tools, sandbox, and proxy, then repeat the case.
Results change between runs
Check for unpinned model revisions, sampling settings, prompts, retrieval indexes, tool schemas, evaluator versions, dependency updates, and changing test data. Freeze those inputs, save a configuration hash, and label any unavoidable nondeterminism in the report.
A test leaks real sensitive data
Trigger the incident procedure in the rules of engagement, stop further requests, preserve sanitized evidence, and notify the named owner. Remove the data from temporary stores and logs according to the approved handling plan, rotate exposed credentials, and do not continue until the scope and containment decision are documented.
Quick Recap
Minimum go/no-go checklist
- Written authorization and rules of engagement are signed and current.
- Scope, techniques, test window, network ranges, data rules, contacts, stop conditions, and destructive-action approver are recorded.
- The target is staging, uses synthetic or minimized data, and has separate identities and credentials.
- Tools are deny-by-default, read-only where possible, and server-side authorization is enforced.
- Code execution and browsing are sandboxed; internal routes, metadata, and secret stores are blocked.
- Turns, tokens, recursion, concurrency, rate, tool calls, duration, and spend have limits with alerts and a kill switch.
- Prompts, retrieval, tool calls, approvals, outputs, evaluator decisions, and network events are captured and protected.
- The corpus covers OWASP LLM risks plus retrieval authorization, tenant isolation, poisoned documents, malicious tool output, and applicable exfiltration channels.
- Findings have reproducible evidence, severity rationale, an owner, remediation, and a retest result.
- Deployment or continued testing is gated by documented thresholds and an explicit residual-risk decision.
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