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What is AI red teaming?
AI red-teaming is systematic adversarial testing designed to find how an AI system responds to malicious, misleading, or unusual inputs and workflows. The target may be a foundation model, but in a deployed product it can also include the application wrapper, retrieved or connected data, tools and APIs, infrastructure, and runtime controls.
The OWASP GenAI Red Teaming Guide organizes testing into four areas. This wider scope matters: a model that can be persuaded to produce a harmful answer may present a different risk from an agent that can use that answer to expose records or take an external action. Define what the application can access and do before deciding which attacks matter.
| Testing area | What to examine | Example question |
|---|---|---|
| Model evaluation | Behavior under adversarial, misleading, or otherwise challenging inputs. | Does the model follow an attacker’s instruction over the task’s intended constraints? |
| Implementation testing | The application’s prompts, input and output handling, retrieval, and integration logic. | Can untrusted content change how the application handles instructions or data? |
| Infrastructure assessment | Services, access controls, and resources supporting the AI system. | Could a weakness in a connected service expose data or capabilities? |
| Runtime behavior analysis | How the deployed system behaves in its operating context, including its controls and interactions. | Can monitoring or safeguards identify and limit harmful behavior in use? |
These categories are from OWASP’s guide; the example questions are prompts for scoping, not a complete test plan. A jailbreak-only exercise can miss implementation and infrastructure weaknesses. Conversely, a technically successful prompt may have little practical impact if the deployed application cannot act on the response. OWASP describes a risk-based approach and emphasizes ongoing oversight rather than treating a system as permanently finished in its January 22, 2025 guide announcement.
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How do I red-team an LLM?
Plan the exercise around the particular system and plausible harms. Record the scope and evidence so that findings can be reproduced, prioritized, and retested.
- Get authorization and define scope. Identify the target system and environment, approved accounts and APIs, in-scope data, dates, permitted techniques, rate limits, and stop conditions. Prefer an isolated or representative test environment when possible. There is no single authorization template that fits every engagement; align the scope with the system owner and applicable policies.
- Map the system and its consequences. Record whether the deployment is a chatbot, retrieval-augmented system, summarizer, classifier, or agent. Map what it can read, what tools or APIs it can call, and whether it can trigger consequential actions. Give priority to sensitive information and high-impact capabilities.
- Write a threat model and success criteria. Choose scenarios tied to the deployment, such as instruction hierarchy or prompt injection, exposure of sensitive information, unsafe output handling, harmful content, or misuse of connected tools. Separate the evaluation objective from the attack technique and the observed impact: a technique is not itself proof of a meaningful vulnerability.
- Choose complementary test methods. Use automated probes for repeatable coverage, human review for behavior whose meaning depends on context, and implementation or infrastructure tests for weaknesses outside the model’s responses. NIST’s ARIA Evaluation Planning Manual, published September 18, 2026, describes an approach combining model testing, red-teaming, and user testing to assess trustworthiness.
- Run against a recorded configuration. Capture the target and model identifier, version, relevant prompts and settings, selected probes and detectors, date, environment, and changes since the previous run. Without that context, differences in results may reflect changed conditions rather than a meaningful change in risk.
- Validate and prioritize each finding. Review the exact input and response, detector judgment, reproducibility, severity, and plausible impact in the application. Investigate a tool hit rather than treating it as automatic proof of an exploitable issue.
- Remediate and retest. Assign an owner, preserve evidence that can reproduce the issue, apply a change, and rerun the relevant tests. Continue reviewing the system as models, prompts, data, integrations, and threat patterns change, consistent with OWASP’s emphasis on ongoing oversight.
NIST’s Generative AI Profile (NIST AI 600-1), published July 26, 2024 and updated April 8, 2026, accompanies AI RMF 1.0. NIST says the AI RMF 1.0 is being revised; it is a risk-management framework, not a substitute for an engagement-specific test plan.
What should an AI red-team assessment cover?
Coverage should follow the system’s real capabilities and consequences, not a generic checklist alone. Use these questions to check whether the scope reaches beyond model responses:
- Model behavior: Are tests aimed at the behaviors the application relies on, including instruction handling, sensitive-information exposure, harmful output, and misinformation where relevant?
- Application implementation: Are prompts, retrieved content, input and output handling, and the application’s interpretation of model responses in scope?
- Data and connected capabilities: What information can the system access, and what tools, APIs, or actions can it invoke? Could a failure expose data or cause an external effect?
- Infrastructure and runtime: Are the supporting services and operational controls considered, including how the system behaves after deployment?
- Users and impact: Who may be affected, what plausible harm could occur, and how will context-dependent findings be assessed by a human?
- Evidence and follow-through: Can another reviewer reproduce the result from the recorded configuration? Is there an owner and a retest plan for material findings?
The point is not to test every conceivable attack. It is to make a defensible connection between the system’s exposure, a realistic threat, an observed behavior, and its likely consequence.
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What is Garak, and what does it test?
Garak is a command-line LLM vulnerability scanner. The NVIDIA project describes its purpose this way: “garak checks if an LLM can be made to fail in a way we don’t want.” Its probes target failure modes including hallucination, data leakage, prompt injection, misinformation, toxicity generation, and jailbreaks.
Garak’s repository lists interfaces for Hugging Face Hub generative models, Replicate text models, OpenAI API chat and continuation models, AWS Bedrock, LiteLLM, REST-accessible targets, and GGUF models. These are project-listed capabilities, not a guarantee that every integration or setup remains unchanged. Check the repository and reference documentation index for current requirements and target instructions before running a scan.
Conceptually, Garak separates the test into components: probes, generators, detectors, evaluators, and harnesses. A probe supplies a test scenario; the target interface generates responses; detectors assess responses for defined signals; evaluators organize outcomes; and a harness coordinates the run. Detector judgments are bounded by the selected tests and detection logic.
How do I use Garak to test an LLM?
Run Garak only against a target and account you are authorized to test. The following is a beginner workflow based on the project’s documented commands; it is not a claim that the commands were executed for this article. Garak’s installation, provider integrations, and probe names can change, so verify the current project documentation before use.
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1. Install Garak
The repository documents installing the stable package with:
python -m pip install -U garakIt also documents a Conda source-installation route with Python versions from 3.11 through 3.13 inclusive. Confirm the current Python requirements and installation instructions in the project documentation before choosing an installation method.
2. Inspect the available probes
List the probes available in your installation before selecting a test:
garak --list_probesProbe availability and names depend on the Garak version. Choose probes that match the threat scenarios and application context you defined; a broad default scan is not a substitute for that decision.
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3. Run a documented example against an authorized target
The repository gives this Hugging Face example:
python3 -m garak --target_type huggingface --target_name gpt2 --spec probes.dan.Dan_11_0This is a project documentation example, not a recommendation to treat that model or probe as representative of a production deployment. In general, the command needs a target interface specified with --target_type and may need a target name specified with --target_name. A provider may also require credentials. For a commercial provider, use credentials securely, check the provider’s current model naming, access, and API policies, and confirm expected costs before starting.
4. Narrow the run when you have a specific question
For a prompt-injection-related probe family, the repository gives this example:
garak --spec probes.promptinjectUse a specific --spec value only after confirming that the probe exists in your installed version. The general command form is garak <options>; consult the current repository documentation for target-specific options rather than assuming one provider’s setup applies to another.
How do I interpret Garak results?
Read each outcome as: this configured probe and detector combination observed this behavior on this target under these conditions. It is evidence about a run, not a verdict on every user, attack path, or part of the deployed system.
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According to the Garak repository, each loaded probe produces an evaluation row for detector results; undesirable behavior is marked FAIL, with a failure rate. The detailed JSONL run report records attempts and evaluations, and the tool also creates a hit log for attempts that yielded a vulnerability.
- A hit needs validation. Inspect the exact attempt, response, detector result, and application context. Determine whether the behavior is reproducible and what a real user or attacker could do with it.
- No hit is not proof of safety. Probe and detector coverage is finite. A clean result means no tested behavior was detected under those conditions, not that untested attacks or system weaknesses are absent.
- Keep the configuration with the result. Record model and version, prompts and settings, probe and detector selections, environment, and date. Behavior can vary with configuration, version, and randomness.
- Compare like with like. Do not treat raw failure rates from different targets, configurations, or probe selections as controlled comparisons.
How should I compare AI red-teaming tools or providers?
Compare methods and providers against the deployment’s risks, not just the number of prompts or a dramatic jailbreak demonstration. OWASP’s Vendor Evaluation Criteria v1.0, published February 4, 2026, covers providers and automated tools, from simple GenAI systems to advanced tool-calling or multi-agent systems. It is a comparison resource, not an endorsement of an individual provider.
- Coverage: Does the approach assess relevant model behavior, implementation, infrastructure, runtime behavior, and user-facing consequences?
- Risk fit: Are the scenarios relevant to the deployment’s users, sensitive data, connected tools, and plausible harms?
- Threat breadth and realism: Does the work extend beyond a narrow jailbreak-only demonstration where the system’s risks call for broader testing?
- Evaluation rigor: Are objectives, configurations, evidence, interpretation, and remediation guidance clear enough to review and reproduce?
- Governance: Are authorization, data handling, disclosure, and integration with the organization’s risk process addressed?
An automated scanner can contribute repeatable tests; it cannot by itself establish that all relevant layers have been assessed. Evaluate the method’s evidence and fit to your system before treating a tool result or provider claim as assurance.
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