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What should an AI red-team exercise cover?
Set the boundary around the system that will actually be deployed. Depending on the architecture, that includes the model, application logic, prompts and other configuration, connected tools and data, staging and deployment pipelines, infrastructure, and runtime safeguards such as access controls, output checks, logging, detection, and response. A test of the base model alone can miss vulnerabilities introduced by the application or its integrations.
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Include ordinary confidentiality, integrity, and availability risks alongside AI-specific attacks. NIST’s security guidance identifies risks to systems and to training or output data, as well as vulnerabilities in underlying software and hardware. NIST also notes that existing guidance does not comprehensively cover every AI attack surface or machine-learning attack; its security page was updated August 14, 2026. Treat any test plan as a context-specific selection, not an exhaustive checklist.
How do I plan an authorized exercise?
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Document purpose, permission, and boundaries
Get written authorization from the system owner before testing. Record the system and version, intended users and tasks, environments in scope, tester access, test window, data-handling rules, points of contact, logging expectations, reporting route, and conditions that require testing to stop. Specify whether connected tools, third-party services, production data, or production infrastructure may be touched. Agree on deconfliction and communications so a test is not mistaken for an active incident. Decide how test data and collected evidence will be retained or disposed of.
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OWASP’s AI testing guidance treats authorization, data logging, reporting, deconfliction, communications and operational security, and data disposition as scoping concerns. Its retrieved guide is marked RC3c; confirm the project’s current revision when planning an exercise.
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Threat-model the actual deployment
Map users, assets, sensitive information, model and application components, integrations, trust boundaries, and the ways data or instructions move between them. Identify what an attacker could reach, change, infer, disrupt, or cause the system to do. Consider intended use and plausible misuse, including indirect paths such as documents retrieved by the application or actions taken by an agent.
For each important asset or boundary, ask what could go wrong for confidentiality, integrity, or availability, and which control is supposed to prevent, detect, or limit the impact. Include conventional software and infrastructure weaknesses; an AI-specific test does not substitute for testing authentication, authorization, input handling, secrets, dependencies, or deployment configuration where those are in scope.
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Choose testers who fit the risks
Match the team to the system’s domain and likely attackers. Cybersecurity expertise helps identify exploit paths and assess impact; knowledge of the deployment domain helps testers understand sensitive workflows and harmful outcomes. Representative users can expose assumptions specialists miss. AI-assisted testing may help explore inputs at scale, but it does not replace human review of context, evidence, and impact.
NIST describes expert, general-public, combined, and human/AI red-team approaches. They are options rather than a single prescribed team design: select based on access, domain fit, attack-surface coverage, and the ability to interpret findings. NIST advises analyzing results before using them in organizational governance and risk-management decisions.
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Turn the threat model into test cases
Prioritize scenarios by plausible impact and exposure, then define what each test is meant to learn, what evidence counts as success, and which controls should respond. Include both attempts to cause unwanted model behavior and attempts to exploit the surrounding product. For each case, name the expected safe behavior and the boundary the system must preserve.
Which attacks and controls should the team test?
Choose applicable attack paths rather than treating a long list as a universal requirement. Relevant cases include:
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1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errors- Prompt injection and safeguard bypass: Try adversarial direct inputs and, where applicable, instructions embedded in content the system processes. Check whether the model follows untrusted instructions, crosses an authorization boundary, or produces disallowed output—and whether application-level controls contain the result.
- Unsafe cyber assistance: Probe whether the system can be induced to provide malicious-code assistance or materially enhance phishing. Assess the response against the product’s intended use and safeguards rather than counting every sensitive-looking answer as a vulnerability.
- Sensitive or training-data exposure: Test attempts to elicit protected information, including data accessible through the application or integrations. Keep membership inference distinct: it tests whether an attacker can infer that particular data was used in training, not simply whether a model repeats a visible string.
- Data poisoning: Where training or adaptation data is in scope, assess whether malicious or manipulated data could affect model behavior and whether data controls detect or limit that influence.
- Model extraction: Where an exposed model interface makes it relevant, test whether repeated queries could reveal or approximate protected model behavior or information beyond what the service intends to expose.
- Agents and connected tools: If the system can call tools, retrieve data, or take actions, test whether untrusted inputs can trigger unauthorized access or actions. Verify permissions, confirmation steps, and boundaries at the tool and application layers, not only in the model’s response.
- Conventional application and infrastructure paths: Test relevant weaknesses in access controls, data handling, dependencies, infrastructure, and deployment processes. Trace whether a failure could expose or alter data, compromise the model or its configuration, or interrupt service.
- Controls after fine-tuning: If the deployed model has been fine-tuned or otherwise adapted, rerun relevant safety and security cases. Confirm that adaptation has not weakened safeguards or access boundaries.
Test prevention and response together: a blocked attack may still reveal gaps in logging, alerting, containment, or recovery. Record which control was expected to work, what happened, and whether the failure was in model behavior, application logic, a connected service, or operations.
How is red-teaming different from other AI evaluation?
NIST’s ARIA evaluation framework distinguishes model testing, red-teaming, and field testing as different evaluation levels. They answer related but different questions; one does not replace the others.
| Approach | What it contributes | When it fits |
|---|---|---|
| Model testing | Examines model behavior through defined tests. | Useful for controlled evaluation of model capabilities and responses; by itself, it may not expose failures in the deployed application, integrations, or infrastructure. |
| Red-teaming | Uses structured testing, often adversarial, to find flaws, vulnerabilities, undesirable behavior, or risks from misuse. | Useful for probing attack paths against the system in a specified context, including interactions between components. |
| Field testing | Evaluates a system in a real-use setting. | Useful for learning how behavior and risks appear in deployment conditions; it is distinct from a bounded pre-deployment adversarial exercise. |
NIST’s glossary, attributing the definition to NIST AI 100-2e2025, describes AI red-teaming as a structured testing effort that often uses adversarial methods to find flaws and vulnerabilities in an AI system, including unforeseen or undesirable behavior and potential misuse risks. NIST AI 600-1 also states that red-teaming can occur before or after broader public availability; its discussion here focuses on pre-deployment contexts.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should findings be measured and documented?
Keep enough information for another reviewer to understand and, where safe, reproduce a finding. A useful record includes:
- Test case, date, tester, system and model versions, configuration, and environment.
- Relevant input and observed output, with sensitive data handled under the agreed rules.
- Expected behavior, actual behavior, affected asset or boundary, and the control that failed or succeeded.
- Potential impact, severity rationale, reproduction conditions, and any uncertainty about exploitability.
- Recommended remediation, accountable owner, retest status, and residual risk.
Select metrics that answer questions specific to the system and intended use. OWASP defines attack success rate, also called jailbreak success rate, as the percentage of adversarial inputs that successfully exploit vulnerabilities or elicit undesired behavior. Define the test set and what counts as success before comparing results; a rate without those details can be misleading. The cited guidance does not establish a universal acceptable rate or release threshold for every system.
What happens after testing?
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Assign and fix findings
Route each confirmed issue to an owner who can change the relevant model, application, integration, infrastructure, or operational control. Prioritize according to impact and exposure, not just how easy a test was to demonstrate.
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Retest the mitigation
Repeat the original case against the changed system and run related regression cases. A fix that blocks one prompt or input may leave a neighboring path open. Save the new version and configuration with the retest evidence.
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Record residual risk and decide on release
Give decision-makers the findings, remediation state, unresolved risks, and evidence needed to judge deployment in the intended context. Record who accepts residual risk and any release conditions. NIST advises further analysis of red-team results before incorporating them into governance and risk-management decisions.
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Continue ordinary security work and monitoring
Red-teaming is one part of security assurance, not a substitute for secure engineering, other evaluation levels, or monitoring after release. Track relevant runtime signals and define how the team will investigate and respond when behavior or threats change.
What a pre-deployment exercise can—and cannot—establish
A well-scoped exercise can provide documented evidence about tested attack paths, controls, and conditions at a particular point in time. It cannot establish that every attack has been found, certify a system as secure, or prove that a future version or changed deployment is safe. Tailor coverage to the model, architecture, deployment context, authorized access, risk tolerance, and applicable obligations; the cited guidance does not establish a universal threat list, legal requirement, or certification.
The NIST Generative AI Profile is dated July 26, 2024. Its security material and OWASP’s testing guidance offer useful starting points, but teams should apply them alongside context-specific threat modeling and their own governance and security processes.
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