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Red teaming AI systems is hard because a useful test must cover the right target, realistic threats, the system’s actual operating context, and the decisions that follow from a finding. A jailbreak test of a model alone cannot establish that an application is safe: failures may arise from its integrations, permissions, infrastructure, users, or tools, and attackers can adapt their methods after a test is complete.
Why is red teaming an AI system difficult?
“AI red teaming” can mean probing a model, testing an application built around it, assessing infrastructure, or examining a deployed system in use. Those are different targets. A result is only meaningful when it makes clear what was tested, which threats were considered, what the testers could access, and what decision the exercise was meant to inform.
The challenge is not simply finding a clever prompt. A deployed system may retrieve documents, call tools, rely on external services, apply access controls, and interact with people. A weakness can emerge from the way those pieces work together even if a model-level test does not reveal it. Red teaming is therefore evidence about a defined system and test—not a general certificate of safety.
What parts of the system need to be in scope?
Model behavior
Model-level testing probes how a model responds to inputs, including attempts to elicit harmful outputs, expose sensitive information, or bypass safeguards. It can identify important weaknesses, but it does not test every component that shapes behavior in an application.
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Implementation and infrastructure
Application code, retrieval pipelines, data sources, permissions, integrations, deployment configuration, and logging can all affect risk. OWASP’s GenAI Red Teaming Guide organizes testing across four areas: model evaluation, implementation testing, infrastructure assessment, and runtime behavior analysis. The practical implication is to state which of these areas were included rather than describing a model-only exercise as a full-system assessment.
Runtime and human context
How people use a system, what information they provide, and how its outputs or actions affect them can change the risk. A 2025 paper on sociotechnical red teaming argues for examining systems across the development lifecycle as well as testing models at a more focused level. These perspectives are complementary: broad system analysis can surface interactions, while model-focused testing can isolate particular behaviors.
How do scope and purpose shape the test?
Red teaming may focus on security, safety, privacy, bias, robustness, or deployment impacts. A broad label does not show which of those risks were actually examined. Feffer, Sinha, Deng, Lipton, and Heidari describe variation in red-teaming purpose, evaluation target, setting, and the decisions informed by results. They caution against treating the practice as a complete solution to every AI risk.
Before testing, define the decision it will support: for example, whether to remediate a finding, restrict a capability, delay deployment, or add monitoring. Then specify what counts as a finding and how serious outcomes will be distinguished. Without that connection, teams can collect impressive attack counts without knowing whether the results change a real risk decision.
- System boundary: Identify the model, application, integrations, data sources, infrastructure, and operational controls included.
- Threats and harms: Name the risks being tested, such as data exposure, prompt injection, unsafe tool use, bias, or robustness failures.
- Out-of-scope areas: State what was not examined so readers do not mistake limited coverage for comprehensive assurance.
- Decision and follow-up: Explain how results affect remediation, disclosure, deployment, governance, and retesting.
Why do testers, access, and instructions matter?
Testers can only find and document what their access and guidance let them observe. OpenAI’s paper on external red teaming identifies team composition, access levels, and tester guidance as design considerations. Domain knowledge may be important when a risk depends on specialized context; a broader mix of perspectives may reveal concerns a narrow team overlooks. Neither a large tester pool nor expert participation alone guarantees complete coverage.
For an interpretable exercise, report who tested, what materials and interfaces they could use, whether they saw system instructions or other internal information, what tasks they were assigned, and how the team triaged their findings. If testing is external, explain how testers submitted evidence and how the organization handled disclosure and follow-up. Those details help readers distinguish a genuine adversarial exercise from a limited demonstration.
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Why can a passing test become outdated?
Attack methods and system behavior change. A fixed benchmark can show performance against its particular test set, but passing that set does not establish resilience to new attacks or to changes in the model, application, or environment. NIST’s Center for AI Standards and Innovation (CAISI) describes this problem in its agent-hijacking work: previously tested attacks did not necessarily capture weaknesses in an upgraded system.
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For results to remain interpretable, record the system version, test environment, attack set, task, number of attempts, and what success meant. Repeat or adapt tests when a relevant model, tool, permission, or workflow changes. NIST also notes that testing multiple attempts may provide a more realistic view than relying on a single try.
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Why are agents especially difficult to red-team?
An agent can act on information it reads and use tools to carry out tasks. That creates a path from malicious content to an unintended action: an instruction embedded in an email, file, or webpage may be ingested as task data and influence the agent. This is indirect prompt injection. The important question is not only whether the model produced a problematic sentence, but whether the full workflow allowed an unwanted action.
Testing should trace the data and action path: what the agent can read, which instructions it treats as authoritative, what tools it can call, what permissions those tools have, and which actions require confirmation. Include realistic tasks and measure outcomes at the task level. NIST’s examples range from an innocuous email to data theft, malicious code execution, and personalized phishing; the consequences differ substantially, even if they all count as successful attacks in an aggregate measure.
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An overall success rate compresses unlike outcomes into one number. A low-impact nuisance and a serious compromise may both register as a successful attack, while a high aggregate score can hide which tasks or assets are most exposed. Report frequency alongside the consequence of success and the context in which it occurred.
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- Describe the task and environment in which each consequential failure occurred.
- Identify the affected data, account, tool, or other asset where known.
- Separate attempted attacks from successful actions and explain the success criteria.
- Show task-specific results in addition to any aggregate measure.
- Explain the severity assumptions used to prioritize findings.
How do human teams and automated evaluations complement each other?
Human testers can explore unfamiliar scenarios and uncover risks that a fixed test set does not anticipate. Automated evaluations can make checks repeatable and easier to run across versions. Each approach has limits: human exploration may be difficult to reproduce or scale, while automated tests can miss risks absent from their cases and scoring rules. OpenAI’s work on external red teaming treats it as one input to broader model and system assessment, not a substitute for it.
| Approach | Useful contribution | Challenge to account for |
|---|---|---|
| Internal red team | Can test against knowledge of the system’s intended use and operational context. | State the team’s access, expertise, and scope; internal knowledge does not guarantee broad coverage. |
| External testers | Can bring perspectives and probing strategies outside the system’s development team. | Access, guidance, team composition, and finding-handling procedures shape what they can discover and report. |
| Automated evaluation | Can support repeatable checks and comparisons across test runs. | Results remain bounded by the test cases, scoring criteria, and system version evaluated. |
This is not a choice of one method over another. A credible evaluation can use exploratory human testing to reveal new cases, then turn suitable findings into repeatable checks, while retaining broader system and user-context assessment.
What do current NIST and OWASP guidance add?
NIST’s 2025 ARIA pilot report describes evaluation through model testing, red teaming, and field testing. It covered three scenarios—TV Spoilers, Meal Planner, and Pathfinder—and five participating organizations submitted seven AI applications. The report discusses dialogue annotation, tester questionnaires, and measurement trees. Those participation figures describe the pilot, not a statistically representative sample of AI applications.
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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsNIST’s planning manual, published September 18, 2026, describes an approach using Model Testing, Red Teaming, and User Testing. The labels differ between the manual and the 2025 pilot report, so they should not be treated as interchangeable descriptions of a single fixed procedure. The manual presents evaluation elements to support customized assessments.
OWASP’s provider and tool criteria, version 1.0 dated February 4, 2026, addresses chatbots, retrieval-augmented generation (RAG) applications, tool-calling agents, MCP architectures, and multi-agent workflows. It directs attention to threat models, evaluation rigor, tooling quality, and governance, and distinguishes substantive adversarial work from “jailbreak-only” offerings. For organizations assessing an external service, those dimensions are more informative than a claim of broad coverage without evidence.
How can teams turn findings into risk reduction?
Red teaming is most useful as a closed loop rather than a one-off test. Keep evidence and limitations attached to each finding so the people making deployment and remediation decisions can understand what happened and what remains unknown.
Quick Recap
- Set scope: Define the system boundary, intended use, test environment, risks, and the decision the exercise will inform.
- Design representative tests: Include realistic tasks, relevant tools and data flows, and threats suited to the system rather than relying solely on generic prompts.
- Document evidence: Record the system version, access provided to testers, procedures, attempts, task-level outcomes, and limits on coverage.
- Prioritize by consequence: Consider affected assets and plausible downstream harm, not only attack frequency.
- Remediate and retest: Verify that changes address the observed failure and check for related regressions.
- Feed results into operations: Use findings to inform monitoring, governance, disclosure, and decisions about deployment or capability restrictions.
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