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AI red teaming and penetration testing overlap, but the labels do not define the work. A penetration test usually examines a specified application or system. An AI red-team assessment deliberately probes an AI model or AI-enabled system for security weaknesses, harmful or misleading outputs, unexpected behavior and misuse risks, often under conditions outside normal operation. In practice, an AI red-team engagement can look much like a penetration test, so the reliable distinction is the documented target, scope, assumptions and test methods—not the name on the engagement.
A complete assessment may need both conventional application-security testing and tests of model behavior. Testing only the web application around a model cannot establish how the model behaves in deployment; testing only the model cannot establish whether the integrated product protects data, identities, tools and infrastructure.
What an AI red-team test is
NIST’s current glossary defines AI red teaming as “a structured testing effort, often adopting adversarial methods, to find flaws and vulnerabilities in an AI system, including unforeseen or undesirable system behaviors or potential risks associated with the misuse of the system.” That definition has two important implications.
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- The evaluator looks beyond ordinary security defects to behavior that is harmful, misleading, unreliable or vulnerable to deliberate misuse.
NIST also describes AI red teaming as work that can occur in a controlled environment and in collaboration with the system’s developers. The team is adversarial in method, but the engagement is authorized and bounded. The purpose is to expose weaknesses while the owner can observe, reproduce and correct them.
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For a generative system, the target might be text, image, code, audio or video behavior. A useful test can ask whether a model follows its intended policy, gives materially misleading advice, reveals information it should protect, or behaves differently when a user supplies adversarial or unusual inputs. The exact questions depend on the model’s purpose and the surrounding product.
What a penetration test normally covers
A penetration test is generally commissioned against a defined application, service, network, device or other system. The rules of engagement specify what the testers may access, which techniques are permitted, when testing may occur and how disruptive actions are handled. The resulting report maps findings to the tested target and explains evidence, severity and remediation.
Traditional cybersecurity red teaming can be broader than a penetration test. NIST’s Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations (AI 100-2 E2023, published January 2024) describes conventional red teaming as potentially combining physical and cyber attack elements, attacking multiple systems and evaluating an organization’s overall security posture. A penetration test, by contrast, commonly concentrates on one defined application or system.
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Side-by-side differences
| Dimension | AI red-team assessment | Penetration test |
|---|---|---|
| Primary target | Model behavior and vulnerabilities in an AI system, including undesirable behavior and misuse risk. | A defined application, service, device, network or other system. |
| Adversarial focus | Deliberate attempts to trigger flaws, harmful or misleading responses, unsafe actions or other AI-specific failure modes, alongside ordinary security attacks. | Attempts to compromise the security properties of the scoped target, such as confidentiality, integrity, availability or access control. |
| Breadth | May cover a model alone, an integrated AI product or an AI-enabled operational process. The phrase can also be used for a broad organizational red team. | Usually bounded to the system and components named in the statement of work. |
| Operating conditions | May intentionally use conditions outside normal operation, unusual inputs, adversarial interactions or misuse scenarios, while remaining authorized and controlled. | Uses the agreed rules of engagement for the specified system, including permitted access and timing. |
| Evaluation context | Can examine technical and contextual robustness and whether behavior changes in the real deployment context. | Usually evaluates the security of the scoped system rather than the social or operational context of model outputs. |
| Evidence | Test sets, prompts or other inputs, model and system versions, metrics, tools, conditions, outputs, uncertainty and deployment relevance. | Requests, payloads, system responses, affected assets, reproduction steps, impact and remediation evidence for the tested target. |
None of these rows creates an absolute boundary. A penetration tester can test an AI application’s authentication, authorization and APIs, and an AI red team can test those same controls when they are part of the system. The engagement brief must state what was actually assessed.
Why AI changes the test target
Model behavior is part of the attack surface
Conventional software usually has relatively deterministic input-to-output behavior. AI systems can produce different outputs for similar inputs, respond to context, and fail in ways that are not visible from a conventional vulnerability scan. An evaluator therefore needs to examine the output itself and the conditions that produced it.
OWASP’s GenAI Red Teaming Guide, revision RC3c in the cited material, says generative-AI red teaming should include harmful or misleading model responses and should treat the model and its output as part of the adversary’s view. This expands testing beyond whether an endpoint can be exploited: the question may be whether an otherwise valid request causes the system to produce unsafe guidance, disclose protected content or take an action outside its intended role.
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Misuse and unexpected behavior need explicit scenarios
An AI red-team plan should state which misuse cases matter to the product. For a customer-support assistant, that could include attempts to obtain another customer’s information, induce an unauthorized account action or elicit confidently incorrect instructions. For a code assistant, it could include unsafe generated code or disclosure of confidential repository material. These are examples of test objectives; the owner must decide which are relevant and how impact will be judged.
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Modality affects the method
NIST’s generative-AI evaluation program covers text, image, code, audio and video. The modality changes both the inputs and the evidence. Text systems can be evaluated with prompt sets and response ratings; image systems may require checks for unsafe or misleading generated content; code systems require execution or static-analysis controls; audio and video systems may require transcription, content and provenance checks. A report should identify the modalities and capabilities actually tested rather than implying that one modality represents the entire product.
Model, integrated system and field context
NIST’s ARIA evaluation program distinguishes model testing, red teaming and field testing. The distinction prevents a common overclaim: a model-only exercise cannot prove how the deployed product behaves when connected to retrieval stores, plug-ins, external tools, identity systems, rate limits, human reviewers and production data.
Model testing
Model testing isolates capabilities and limitations under controlled conditions. It is useful for repeatable benchmark sets, regression checks and comparisons between model versions. Its limitation is that it may omit application code, configuration and user workflows.
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Integrated testing exercises the model through the product interfaces and connected components. It can reveal failures caused by prompt construction, retrieval results, tool permissions, session handling, content filters or error paths. The scope should name each connected component and the access level supplied to the testers.
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Field testing
Field testing examines behavior in a realistic operational context, with representative users, policies, data and workflows. It can expose contextual risks that a laboratory model test misses, but it requires stronger safeguards for privacy, availability and unintended impact. NIST’s ARIA structure treats this as a distinct level rather than assuming that a model score is evidence of field performance.
How to define an AI assessment that is useful
The label should be settled after the scope is written. Use the following sequence to make the engagement reproducible and to prevent a report from implying coverage that did not occur.
- State the objective. Decide whether the purpose is vulnerability discovery, safety evaluation, release approval, regression testing, compliance evidence or incident investigation. A single engagement can have several objectives, but each needs an observable pass or fail criterion.
- Inventory the target. Name the model or model family, version or release identifier, application interfaces, retrieval systems, tools, data stores, identity controls, monitoring and human review. Mark components that are explicitly out of scope.
- Set authorization and boundaries. Document permitted accounts, environments, time windows, rate limits, data handling, prohibited actions and escalation contacts. Testing outside those rules is not an acceptable “red-team” shortcut.
- Describe the operating context. Record intended users, supported modalities, geographic or regulatory constraints, expected workflows and the conditions that differ between a test environment and production.
- Build test sets and scenarios. Include ordinary-use cases, edge cases, misuse attempts and adversarial interactions relevant to the system. Keep the inputs, expected behavior and rating criteria versioned so another evaluator can repeat the run.
- Choose measures. NIST’s AI RMF Measure function supports quantitative, qualitative or mixed methods. Select metrics and review procedures that match the risk: for example, policy-violation rates, harmful-response ratings, unauthorized-action counts, data-exposure findings or tool-use failures. Do not report a single score as if it represented every risk.
- Run under stated conditions. Record model settings, system prompts or policies where disclosure is permitted, connected services, test dates, tools, evaluator roles and any deviations from deployment-like conditions.
- Analyze and reproduce. Preserve the exact input, output, system state and evidence needed to reproduce a finding. Note stochastic variation and uncertainty when repeated runs produce different results.
- Report limitations and remediation. Separate confirmed findings from hypotheses, identify untested components and recommend owners and corrective actions. Retest after changes and keep monitoring because model, data and configuration updates can change behavior.
What a strong report contains
| Report element | What to record | Why it matters |
|---|---|---|
| Scope and architecture | Model, application, integrations, modalities, environments and exclusions. | Readers can tell whether a finding concerns the model, the product or an external dependency. |
| Versions and dates | Model and application versions, configuration identifiers, test window and relevant data or policy versions. | AI behavior can change after a release, prompt update or data refresh. |
| Methods | Test sets, scenarios, tools, evaluator qualifications, sampling and rating rules. | Another team can review or repeat the assessment. |
| Conditions | Accounts, permissions, network location, rate limits, connected tools and deployment differences. | Impact and reproducibility depend on the conditions under which the behavior occurred. |
| Findings | Inputs, outputs, affected assets, likelihood, impact, uncertainty and reproduction steps. | Owners need actionable evidence rather than a label such as “unsafe model.” |
| Coverage limits | Components, users, modalities, misuse cases and environments not tested. | Prevents a model-only or laboratory result from being presented as a product-wide guarantee. |
NIST’s AI RMF guidance recommends documenting test sets, metrics, tools, methods and conditions similar to deployment, and it calls for regular testing and monitoring. That makes the report useful after the initial engagement instead of treating red teaming as a one-time certificate.
Frameworks and guidance to anchor the work
NIST AI Risk Management Framework
NIST describes the AI RMF as voluntary guidance for managing risks to individuals, organizations and society. The Generative AI Profile, NIST AI 600-1, was released on July 26, 2024. NIST’s framework page also says AI RMF 1.0 is being revised, so an engagement should name the edition and profile used rather than referring generally to “the NIST standard.”
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NIST Measure function
The Measure function organizes ways to analyze, assess, benchmark and monitor AI risks and impacts. It is not a universal penetration-test checklist. Use it to explain why particular metrics, qualitative reviews, test conditions and monitoring intervals were selected.
NIST adversarial-machine-learning taxonomy
AI 100-2 E2023 provides terminology for adversarial machine-learning risks and clarifies the relationship between traditional red teaming and penetration testing. It is useful when an engagement needs to distinguish organization-wide security exercises from testing a specific AI system.
OWASP GenAI red-teaming guidance
The OWASP GenAI Red Teaming Guide revision identified as RC3c emphasizes harmful or misleading responses and treats model output as part of the attack surface. Because project revisions can change, record the guide revision used in the report and update the reference when adopting a later edition.
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Worked example: an AI customer-support assistant
A penetration test of the assistant’s web application might examine authentication, session management, API authorization, injection flaws, exposed storage and rate limiting. Those findings describe the security of the application and its interfaces.
An AI red-team assessment would add scenarios such as requesting another customer’s account details, persuading the assistant to bypass a business rule, inducing a misleading answer about a refund, or causing an authorized tool to perform an action outside the user’s entitlement. Testers would record the model version, system instructions, retrieval content, user role, tool permissions and whether a human approval step intervened.
If the assistant passed the application penetration test but produced unauthorized refunds when a particular conversational sequence was used, the application report alone would not explain the failure. Conversely, if the model behaved safely in isolation but the production retrieval connector returned confidential records, a model-only red-team report would miss the cause. The two assessments answer different questions and can share evidence where their scopes overlap.
Common mistakes and how to correct them
- Treating the label as the scope. Correction: list every component, permission and modality tested.
- Testing only the model. Correction: exercise the deployed interfaces, retrieval, tools, identity controls and monitoring when those components affect risk.
- Testing only the application. Correction: add model-behavior and misuse scenarios, including harmful or misleading outputs relevant to the use case.
- Using ordinary traffic only. Correction: include authorized adversarial and unusual conditions, while documenting safeguards and limits.
- Reporting a single benchmark score. Correction: pair quantitative measures with qualitative review, examples, uncertainty and deployment conditions.
- Ignoring version drift. Correction: record model, application, policy and data versions, then repeat selected tests after changes.
- Claiming production assurance from a laboratory result. Correction: state whether the work was model testing, integrated red teaming or field testing and identify what remains untested.
- Leaving out uncertainty. Correction: document stochastic variation, sampling choices, evaluator disagreement and conditions that could change the result.
Which assessment should an organization commission?
| Primary question | Most suitable starting point | Additional coverage to consider |
|---|---|---|
| Can an attacker compromise this web application or API? | Penetration test against the defined application and infrastructure. | AI behavior testing if model outputs or tool actions affect impact. |
| Can the model produce unsafe, misleading or unexpected behavior? | AI red-team assessment with model-specific scenarios and output evaluation. | Integrated testing to determine whether product controls change the result. |
| Is the entire AI service safe in its intended workflow? | Integrated AI red teaming covering model, application, data, tools and users. | Field testing and ongoing monitoring in representative operations. |
| Has the organization’s overall security posture been challenged? | Broad enterprise red-team engagement with physical, cyber and operational scope as authorized. | Separate model and AI-product assessments for AI-specific behavior. |
The practical answer is often a coordinated program rather than a choice between labels. Use penetration testing for conventional application and infrastructure weaknesses, AI red teaming for model and misuse behavior, and field evaluation when context and real workflows determine the risk. State the boundaries in every report so decision-makers know what the evidence supports.
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