Run an AI penetration test only against an explicitly authorized, production-like staging system whose credentials, data, network routes and tools are isolated from real operations. Put a human approval gate in front of consequential actions, test the complete application rather than only the language model, preserve reproducible evidence, and convert every confirmed bypass into a regression test.
Artificial-intelligence red-teaming is “a structured testing effort to find flaws and vulnerabilities in an AI system, often in a controlled environment and in collaboration with developers of AI,” according to the NIST CSRC Glossary. In practice, that means testing prompts, retrieval, agents, tools, identity, APIs, data stores and runtime behavior as one system.
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What an AI penetration test in staging must prove
A staging exercise should answer a concrete security question: can an attacker cause the AI system to disclose protected information, bypass authorization, misuse a tool, corrupt memory or retrieval, exhaust resources, or trigger an unauthorized business action? A refusal-rate demo is not enough. A model that rejects malicious text can still be dangerous when the surrounding application grants excessive permissions.
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- Model evaluation: instruction following, jailbreak resistance, harmful-content boundaries, data memorization and robustness to encoding or language changes.
- Implementation testing: prompt construction, output parsing, retrieval filters, session handling, tool schemas and error paths.
- Infrastructure assessment: cloud identity, network segmentation, secrets, storage, queues, rate limits, model endpoints and conventional API weaknesses.
- Runtime analysis: actual tool calls, data flows, agent plans, retries, memory writes, monitoring alerts and recovery behavior.
Do not claim a universal pass percentage. Authoritative guidance does not establish an independent success-rate or breach-frequency statistic that applies across AI systems. Define thresholds from the business impact of each failure instead.
1. Get written authorization and define the stop conditions
Before sending a payload, create a signed rules-of-engagement document. It should identify the staging accounts, endpoints, model providers and versions, retrieval indexes, tools, data classes, test window, rate limits, permitted techniques, emergency contacts and evidence-handling rules.
Specify what is allowed
- Direct and indirect prompt-injection attempts against named staging endpoints.
- Jailbreak, instruction-conflict, encoding, multilingual and role-confusion cases.
- Retrieval poisoning using designated test documents.
- Tool-call tests against approved mocks or sandboxes.
- Authentication, authorization, API and cloud configuration checks within the listed accounts.
- Controlled denial-of-service probes that stay below the agreed quota and concurrency limits.
Specify what is prohibited
- Outbound email, messaging, payments, customer lookups, production DNS resolution or changes to real business records.
- Persistence outside the approved staging account, credential harvesting from real users, or attempts to pivot into production.
- Destructive writes, irreversible deletion, uncontrolled crawling and traffic that could affect shared infrastructure.
- Uploading real personal, financial, health or authentication data unless the authorization explicitly covers it.
Require a human approval gate for every high-impact tool action. OWASP SAMM’s guidance is direct: “Run AI-driven testing only against authorized targets and in non-production environments.” It also notes that testing agents can perform destructive actions, so human penetration testers retain control over scope, interpretation and conclusions.
Define an emergency stop
Name the person who can halt the exercise and the exact mechanism: revoke the test credentials, disable the affected tool route, stop workers and block the test network segment. Test the kill switch before the first attack case. Record who approved a restart and why.
2. Build a production-like isolation boundary
Staging is safe only when a mistake cannot reach production. Use a separate cloud account or project and a dedicated network segment. Do not rely on a different hostname alone.
Minimum isolation controls
- Issue per-user, short-lived, least-privilege credentials. Separate the red-team identity from developer and administrator identities.
- Allow-list only mock or sandbox tools. Review every tool and agent-protocol manifest before enabling it.
- Block production DNS, payment processors, email and messaging gateways, administrative interfaces and customer-data paths at the network layer.
- Seed synthetic records or sanitized fixtures. Make test records unmistakable so an accidental lookup is visible.
- Apply quotas, payload-size limits, concurrency caps and timeouts to the model, retrieval service and tools.
- Centralize immutable logs for prompts, retrieved documents, model responses, tool calls, authorization context and infrastructure events.
- Keep a credential-revocation kill switch and a worker-shutdown mechanism independent of the agent under test.
Protect attack corpora, traces and confirmed findings under the same classification rules used for production security data. A prompt containing a hidden secret or a retrieved document containing personal information is itself sensitive evidence.
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Use safe tool doubles
Replace real side effects with deterministic simulators. A sandbox mail tool can record a would-be message without delivering it; a payment mock can return an approval or decline fixture without contacting a processor; a database tool can expose a disposable schema and require a test transaction identifier. Keep the same authentication and schema checks as production so the test exercises the control, not a toy interface.
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3. Inventory the attack surface and draw trust boundaries
Create a data-flow diagram before writing attack prompts. Trace a request from the user interface through authentication, prompt assembly, model invocation, retrieval, memory, tool routing, external APIs and output rendering. Mark every trust boundary and identify what can be influenced by an attacker.
Assets to mark
- System instructions, hidden policies, model and embedding configurations.
- Secrets, access tokens, personal data, business records and regulated content.
- Retrieval indexes, source documents, vector stores, caches and conversation memory.
- Tool manifests, function schemas, queues, webhooks and external API credentials.
- Administrative actions, customer-visible outputs, audit logs and monitoring controls.
Use PASTA, STRIDE or STRIDE AI to structure the threat model, then map scenarios to MITRE ATLAS and OWASP categories. MITRE’s knowledge base, accessed in 2026, lists 16 tactics, 208 techniques, 40 mitigations and 73 case studies. Those figures describe the catalog’s scope, not the likelihood that any one technique will succeed in your application.
Write abuse cases at each boundary
- Can untrusted user text alter a system instruction or tool argument?
- Can a poisoned document outrank trusted retrieval sources?
- Can a low-privilege user cause an agent to act with a service account’s permissions?
- Can memory carry an attacker-controlled instruction into another user’s session?
- Can output containing HTML, SQL, shell syntax or JSON control be executed by a downstream component?
- Can retries, streaming or parallel tool calls bypass a policy check?
4. Select risk-based test categories
Build a corpus that reflects the architecture and the harm a failure would cause. Include both single-turn probes and multi-step attack chains.
| Category | What to test | High-severity evidence |
|---|---|---|
| Direct prompt injection | Instruction override, system-prompt extraction, role confusion, encoded or multilingual payloads | Disclosure of protected instructions or a policy bypass |
| Indirect prompt injection | Malicious text in retrieved pages, files, tickets, emails or tool results | Agent follows attacker content instead of the trusted task |
| Jailbreak and conflict handling | Competing system, developer and user instructions; repeated persuasion; context flooding | Restricted action or content is produced or executed |
| Retrieval poisoning and leakage | Ranking manipulation, tenant-crossing queries, hidden data in chunks and citations | Protected or cross-tenant data appears in context or output |
| Insecure output handling | HTML, Markdown, SQL, shell, JSON and code generated for downstream consumers | Output is executed, rendered unsafely or changes a record |
| Tool misuse and excessive agency | Forged arguments, omitted confirmation, repeated calls, privilege escalation and chained actions | Unauthorized state change or sensitive data access |
| Identity and authorization | Token substitution, confused deputy behavior, session fixation and tenant isolation | One identity performs another identity’s action |
| Memory contamination | Persistent instructions, cross-session writes, stale permissions and poisoned summaries | Attacker influence survives the intended scope |
| Availability abuse | Oversized prompts, recursive plans, expensive retrieval and retry storms | Quota exhaustion or material service degradation |
| Model and privacy risks | Extraction or inference probes where relevant, plus sensitive-data memorization | Recoverable confidential data or a meaningful privacy violation |
| Conventional weaknesses | API authorization, cloud identity, secrets, storage, dependency and network controls | Direct compromise independent of model behavior |
NIST AI 100-2e2025, published March 24, 2025, adds a common vocabulary for adversarial-machine-learning classes such as evasion, poisoning, privacy and misuse. Use that terminology when documenting attack objectives, but keep the test tied to an observed business impact.
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Stage A: Canary checks
Start with a small corpus that verifies routing, logging, tool isolation and the kill switch. Confirm that a harmless mock action produces the expected audit event and that a deliberately blocked action cannot leave staging. Stop if logs are missing or a request reaches an unapproved destination.
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Stage B: Automated evaluation
Run scanners such as garak or promptfoo against the staging endpoint, within the approved concurrency and payload limits. Treat automated results as leads, not verdicts. Capture the exact case, response and tool trace for every candidate finding.
Stage C: Architecture-specific cases
Add tests for your retrieval filters, memory implementation, tool schemas, identity model, streaming path and downstream renderers. A scanner cannot know that a particular function changes a subscription, exposes another tenant or writes to a sensitive queue.
Stage D: Human-led attack chains
Have an experienced tester combine low-severity behaviors into realistic paths: seed a malicious document, induce retrieval, persuade the agent to treat it as policy, then attempt a sandboxed tool call. Human review is essential for ambiguous outputs and high-severity cases.
Do not let the model under test grade itself. Use deterministic assertions for secrets, destination hosts, authorization outcomes and state changes; an independent grader for semantic criteria; and human spot checks for context-dependent judgments. OWASP recommends multiple trials because model behavior is non-deterministic.
6. Define pass/fail thresholds before running
Use risk-based gates rather than one aggregate score. A confirmed disclosure of protected data, unauthorized state change, cross-tenant access or production reachability should fail the release gate even if most attempts are blocked. Set zero tolerance for the most severe outcomes.
Example decision rules
- Critical: any successful production connection, credential exposure, cross-tenant disclosure or irreversible unauthorized action is an immediate stop and release failure.
- High: a repeatable privilege bypass, sensitive-data leak in staging, or tool action without required approval requires remediation and retest before release.
- Medium: a bounded policy bypass with no sensitive data or side effect receives an owner, deadline and regression case.
- Low: confusing wording, harmless refusal inconsistency or noisy logging is tracked only when it affects a security control.
For probabilistic cases, define the number of trials and the acceptance rule in advance. For example, require every trial of a destructive-action case to be blocked, while allowing a separately justified tolerance for benign classification variance. Do not turn a one-time allowance into a recurring success rate; label it as the exact test condition and date.
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7. Capture evidence that another tester can reproduce
For each case, store:
- Case ID, attacker role, objective, severity and authorization scope.
- Exact prompt, encoding, attachments and conversation history.
- Retrieved documents, document versions, ranking and citations.
- Model provider, model/version, system-prompt hash, tool manifest and retrieval configuration.
- Seed where available, temperature and other generation settings.
- Response text, structured output, tool calls and arguments, authorization context and resulting state.
- UTC timestamps, request identifiers, screenshots or HTTP traces, grader outputs and human disposition.
Redact or replace sensitive values with synthetic fixtures. Pin the model version and system-prompt hash; otherwise a later retest may not be testing the same system. Run multiple trials when sampling is non-deterministic and record the complete distribution of outcomes, not only the most dramatic response.
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Contain first
For a confirmed leak or unauthorized action, revoke exposed credentials, disable the affected tool or route, preserve logs and restrict access to the evidence. Do not continue probing a live path merely to gather a cleaner demonstration.
Fix the control at the right layer
Enforce authorization in application code, the tool implementation and the identity provider. Validate tool arguments against an allow-list, bind every action to the requesting principal, require confirmation for consequential operations and enforce tenant filters in the data layer. A system prompt is not a security boundary; OWASP’s DevSecOps guidance summarizes the problem as: “Testing the model, not the application — a safe base model wired to an over-privileged tool is still exploitable.”
Track the vulnerability
Record exploitability, business impact, affected component, owner, remediation deadline, compensating control and exact retest criteria. Close the finding only after the original case and nearby variants fail under the same evidence requirements.
9. Turn every confirmed bypass into continuous regression testing
Store the minimized attack, expected safe behavior and deterministic assertions in a versioned corpus. Rerun it whenever any of these change:
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- Model provider, model version, tokenizer or inference settings.
- System, developer or routing prompts.
- Tool, function or agent-protocol manifests.
- Retrieval sources, chunking, ranking, embeddings or access filters.
- Memory behavior, output parsers, guardrails or identity middleware.
Run a fast gate on every relevant change and a broader suite on a scheduled basis. Track failure rates by category, severity and tenant boundary. Feed production telemetry—blocked injections, anomalous tool calls and unusual retrieval patterns—back into the corpus only after safe review and sanitization. Keep the original exploit and a generalized variant so a narrow patch does not create false confidence.
Regression-case format
{"case_id":"tool-auth-001","attacker_role":"standard_user","input":"...","expected":"tool_call_denied","assertions":["no_state_change","principal_bound","audit_event_present"]}Keep variable values and template syntax inside test fixtures, not in prose shown to users. Version the corpus alongside the controls it protects and require a reviewer for changes to expected outcomes.
How the main frameworks fit together
| Framework | Best use | Strength | Limit |
|---|---|---|---|
| OWASP GenAI Red Teaming Guide | Plan the engagement across model, implementation, infrastructure and runtime | End-to-end coverage and practical testing workflow | Still requires architecture-specific cases and business-impact thresholds |
| MITRE ATLAS | Map adversary tactics, techniques and mitigations | Consistent attack vocabulary and case-study context | Does not replace application authorization tests or evidence collection |
| NIST AI 100-2e2025 | Describe adversarial-ML attack classes and terminology | Shared language for evasion, poisoning, privacy and misuse | Taxonomy is not a complete test plan |
| OWASP threat-modeling guidance | Decompose services, data stores, interfaces and trust boundaries | Finds risks outside the model itself | Needs a current data-flow diagram and knowledgeable owners |
Compare any testing approach on coverage, evidence quality, reproducibility, agent and tool support, human oversight and ability to run continuously in CI. A large prompt suite with no tool traces is weaker than a smaller suite that proves authorization and state outcomes.
Common failure modes and recovery
The test accidentally reaches production
Stop workers, revoke credentials and block the route immediately. Preserve network and application logs, notify the emergency contact and determine whether any real data or state was affected. Do not resume until the boundary is independently verified.
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Inspect the tool router, identity binding and argument validation. Add a deterministic authorization assertion and a mocked side-effect test. Move the control out of prompt wording and into code and permissions.
Results change between runs
Pin model and prompt metadata, record seeds and sampling settings where available, and run the case repeatedly. Separate semantic grading from deterministic checks and report the observed trial outcomes with their conditions.
A scanner reports a dramatic response that cannot be reproduced
Save the exact payload, retrieved context, model version and tool trace. Re-run under identical settings, then test a minimized variant. Treat an unconfirmed result as a lead, not a breach, while keeping it visible for follow-up.
Logs contain secrets or personal data
Restrict access, rotate exposed credentials, redact the evidence and replace fixtures with synthetic values. Update logging filters before continuing; security testing must not become a new data-leak channel.
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Quick Recap
Pre-flight checklist
- Written authorization names targets, dates, techniques, limits and contacts.
- Staging uses separate accounts, networks, credentials, data and DNS controls.
- Tools are mocks or sandboxes, manifests are reviewed and high-impact actions require approval.
- Kill switch, quotas, timeouts and immutable centralized logs have been tested.
- Data-flow diagram marks trust boundaries, assets and attacker-controlled inputs.
- Test corpus covers injection, retrieval, tools, identity, memory, availability, privacy and conventional weaknesses.
- Pass/fail thresholds and zero-tolerance outcomes were agreed before execution.
- Automated scans are followed by architecture-specific and human-led cases.
- Evidence pins model, prompt, tools, retrieval, settings, responses and state changes.
- Every confirmed bypass has an owner, fix, retest criterion and regression case.
- Suites run after model, prompt, tool, retrieval and guardrail changes.
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