.svg){kind=icon}
Learn
AI red teaming is a structured adversarial testing exercise in which specialists deliberately probe an AI system to find flaws and vulnerabilities, such as inaccurate, harmful, or exploitable behavior, before an adversary does and before the system is trusted with a mission.
Why it matters to your mission. An AI model that scored well in evaluation can still be manipulated by a crafted input, leak data it was never meant to reveal, or fail in ways no benchmark asked about. AI red teaming deliberately attacks the system in a controlled setting to surface those failures first. The outcome is a capability whose weaknesses are found by your own team, documented, and fixed before an adversary finds them in operations.
AI red teaming is a structured adversarial testing exercise in which specialists deliberately probe an AI system to find flaws and vulnerabilities, such as inaccurate, harmful, or exploitable behavior, before an adversary does and before the system is trusted with a mission.
Ordinary evaluation asks whether a model is correct on the data you expect. Red teaming asks a harder question: what happens when someone is actively trying to break it. For defense and government use, that distinction is decision risk. A model fielded against a motivated adversary will face inputs chosen specifically to make it fail, and a benchmark score says nothing about how it behaves under that pressure. Red teaming exists to close that gap, and to leave a record of what was tried, what broke, and what was fixed.
AI red teaming is a subset of the broader AI test and evaluation (T&E) discipline, not a replacement for it. T&E measures how well a system performs and where it fails across mission-representative conditions; red teaming is the adversarial part of that measurement, aimed squarely at the failures an attacker would try to induce. The Cybersecurity and Infrastructure Security Agency makes this relationship explicit: it describes AI red teaming as a sub-component of AI testing, evaluation, verification, and validation (TEVV), which is itself grounded in established software TEVV practice (see CISA, AI Red Teaming: Applying Software TEVV for AI Evaluations). This page covers the adversarial slice; the T&E page covers the whole measurement discipline it sits inside.
Key takeaways
AI red teaming is a structured testing exercise used to probe an AI system to find flaws and vulnerabilities such as inaccurate, harmful, or exploitable outputs, usually in a controlled environment and in collaboration with the system's developers. The National Institute of Standards and Technology defines it in almost exactly those terms in its Generative AI Profile, where red teaming is one of the structured feedback methods that inform whether a system is approved, maintained, or decommissioned (NIST, AI Risk Management Framework: Generative AI Profile, NIST AI 600-1).
The name borrows from a long-standing security practice. In cybersecurity, a red team plays the attacker so the defenders learn where they are exposed. AI red teaming applies the same adversarial posture to a model or an AI-enabled system: instead of confirming the system works as intended, the red team works to make it fail, then reports how. NIST notes that red teaming can be performed before or after a system is released, and that its value depends heavily on the expertise and diversity of the red team itself, because a tester who does not understand the deployment context will miss the failures that matter in it.
For a mission, the controlled-environment part is not a detail. Red teaming surfaces exactly the behaviors you do not want loose in production, so it has to run where those behaviors can be studied and contained, with the findings captured rather than lost.
Test and evaluation measures how a system performs across the conditions it is expected to meet; red teaming is the adversarial part of that measurement, where a tester deliberately works to make the system fail. T&E tells you whether the capability is good enough under normal and representative conditions. Red teaming tells you whether it holds up when someone is attacking it on purpose. You need both: a system that passes evaluation and has never been red-teamed has measured only the failures it anticipated.
The two differ in intent, in who runs them, and in what they produce. The contrast below is the one evaluators most often need to make explicit, because the words are sometimes used loosely.
| Dimension | AI test and evaluation (T&E) | AI red teaming |
|---|---|---|
| Primary question | How well does it perform, and where does it fail? | How can an adversary make it fail or misbehave? |
| Posture | Cooperative measurement against expected conditions | Adversarial; actively tries to break the system |
| Who runs it | Testers and evaluators measuring performance | Specialists playing the attacker, often independent of the builders |
| What it probes | Accuracy, calibration, robustness, drift, operational fit | Prompt injection, jailbreaks, data extraction, adversarial examples, misuse |
| Typical output | Performance scores and a pass or fail decision | A documented set of discovered vulnerabilities and their severity |
| Relationship | The broader discipline | A sub-component of T&E / TEVV, focused on adversarial failure |
The takeaway is not that one replaces the other. Red teaming is how the adversarial questions get asked inside a T&E program; the two belong together, and CISA frames red teaming as sitting inside the larger TEVV effort for exactly that reason.
It tests for the ways an AI system can be induced to fail or be turned against its user, not just the ways it can be inaccurate. The NIST Generative AI Profile calls for red teaming to assess a system's resilience against abuse to facilitate attacks, direct attacks on the model such as prompt injection, and machine-learning attacks such as adversarial examples. In a defense context the categories worth probing are concrete:
Each of these is a mission liability if it is discovered in operations. Red teaming is where they are discovered on purpose, in a setting where the finding can be documented and fixed.
Specialists who bring adversarial intent and relevant domain expertise, which NIST groups into expert, general-public, and combined exercises depending on the use case. Expert red teaming is performed by people with specific domain or security expertise, for example in cybersecurity or in the mission area the system supports. General-public red teaming draws on ordinary users who bring their own perspectives and surface failures experts would not think to try. Many programs combine the two.
NIST is explicit that the quality of the output depends on the makeup of the team: a red team with the right domain and contextual expertise finds the failures that matter in the real deployment, while a mismatched team produces findings that look thorough but miss the point. For defense use this argues for red teams that understand both AI attack techniques and the mission the system serves, and for keeping the findings and the resulting fixes under the customer's control rather than in a vendor's hands.
Before fielding, to decide whether the system is cleared, and on a recurring cadence after fielding, because both the model and the threat keep changing. The NIST Generative AI Profile calls for conducting adversarial testing at a regular cadence to map and measure risks over a system's life, not as a one-time gate. A model that was red-teamed once and then retrained is, from an adversary's point of view, a new target that has not been tested.
This cadence mirrors the way federal policy and oversight treat AI testing generally. OMB Memorandum M-25-21 sets minimum risk-management practices for high-impact federal AI, including pre-deployment testing and ongoing monitoring; adversarial red teaming is how the pre-deployment and recurring testing requirements get their teeth against a thinking opponent. The U.S. Government Accountability Office makes the point from the oversight side: its AI Accountability Framework (GAO-21-519SP) treats performance and monitoring as standing pillars of federal AI oversight rather than one-time gates, which is the same reason adversarial testing cannot be a single event. In practice, effective red teaming means running it before the authorization decision, repeating it after each material change to the model or its environment, and feeding every finding back into the system's risk record.
Torch.AI builds government-owned reasoning infrastructure, so the artifacts that red teaming produces, the discovered vulnerabilities, the attack library, and the record of what was fixed, stay inside the mission environment and under the customer's control rather than inside a vendor's platform. That ownership is the point: an accreditation case that rests on adversarial findings the government cannot inspect or retain is not a defensible one.
Two parts of the approach map to this page. Because the reasoning layer is designed to be auditable, with each determination carrying a trail a reviewer can follow, a red-team finding can be traced to the specific behavior that produced it and re-checked after a fix, rather than asserted and forgotten. And because institutional knowledge, the record of how past adversarial tests were run and what they found, is kept usable in context, a new round of red teaming can build on the last instead of starting blind. Across the portfolio the approach complements existing systems of record rather than replacing them, so findings stay traceable to their source. You can see how this is packaged as a capability on the Torch.AI software page.
AI red teaming is the adversarial complement to the broader measurement discipline. For how the full program fits together, including evaluation on mission data, a model and agent registry, and continuous monitoring, see AI test and evaluation (T&E).
The guidance above explains why adversarial testing matters and where it sits; the checklist below is what to actually require when scoping a capability.
Evaluating a capability? The seven requirements above are the backbone of an AI red-teaming evaluation you can score against. Bring them to a scoping call and we will walk each one against your environment: request a technical walkthrough.
AI red teaming is a structured adversarial testing exercise in which specialists deliberately probe an AI system to find flaws and vulnerabilities, such as inaccurate, harmful, or exploitable behavior, before an adversary does and before the system is trusted with a mission. NIST defines it as a structured exercise used to probe an AI system to find flaws, usually in a controlled environment and in collaboration with the system's developers.
Test and evaluation measures how a system performs across expected conditions; red teaming is the adversarial part of that measurement, where a tester deliberately works to make the system fail. CISA describes AI red teaming as a sub-component of AI testing, evaluation, verification, and validation (TEVV). You need both; a system that passes evaluation but has never been red-teamed has measured only the failures it anticipated.
It tests for the ways an AI system can be induced to fail or be turned against its user: prompt injection and jailbreaks, training-data and sensitive-data extraction, adversarial examples, model inversion and inference attacks, misuse to facilitate other attacks, and failure under drift. The NIST Generative AI Profile calls for assessing resilience against abuse, direct model attacks such as prompt injection, and machine-learning attacks such as adversarial examples.
Specialists who bring adversarial intent and relevant domain expertise. NIST groups red teaming into expert, general-public, and combined exercises, and is explicit that output quality depends on the team having the domain and contextual expertise to find the failures that matter in the real deployment.
Before fielding, to decide whether the system is cleared, and on a recurring cadence afterward, because both the model and the threat keep changing. The NIST Generative AI Profile calls for adversarial testing at a regular cadence over a system's life, and OMB M-25-21 requires pre-deployment testing and ongoing monitoring for high-impact federal AI.
Describe the problem.
Define the constraints. We will structure the session.
No demo.
No slide deck.
Real work.
Describe the problem.
Define the constraints. We will structure the session.
No demo.
No slide deck.
Real work.
