What is AI red teaming?
AI governance and risk
AI red teaming is adversarial testing designed to find the ways an AI model or AI system can fail, be misused, or have its safeguards bypassed. It borrows the mindset of an attacker or hostile user and applies it in a controlled way to uncover weaknesses that normal happy path testing often misses. In practice it can involve experts, crowdworkers, automated attack generation, or mixed methods, and it usually feeds into fixes, new guardrails, and stronger repeatable evals.
What this means
A practical way to understand AI red teaming is to think of it as pressure testing with an adversarial mindset. Ordinary testing asks, "Does this system work as intended?" Red teaming asks, "How could this system be pushed, tricked, misused, or broken?" That shift in attitude is what makes it different. The aim is not to prove the system is good. The aim is to discover the failure paths before a real attacker, careless user, or public controversy does.
In AI, the attack surface is broader than many leaders first expect. It can include prompt attacks, jailbreaks, multi turn manipulation, prompt injection through retrieved content, misleading images or audio, tool misuse, privacy leakage, unsafe code execution, or policy bypass in another language or cultural context. Anthropic's and OpenAI's materials both show that red teaming now spans manual, automated, multimodal, and domain specific methods.
Red teaming is related to AI evals, but it is not identical. Evals are the wider practice of structured testing. Red teaming is the adversarial branch of that practice. It is especially useful for surfacing novel risks and generating the harder test cases that later become repeatable automated evaluations.
The field is still maturing. Anthropic explicitly notes the lack of standardised practices, and OpenAI notes that methods, goals, and outputs vary across organisations and sectors. That means leaders should treat red teaming as a discipline to design carefully, not as a box you tick by hiring someone to "try a few jailbreaks".
Why it matters
Red teaming matters because AI systems can appear safe under normal use while failing badly under hostile or unusual use. A support assistant may answer ordinary questions well but leak internal material when manipulated by a carefully written prompt. A coding agent may complete routine tasks but take unsafe actions once given tool access. A multilingual chatbot may hold its boundaries well in English and fail in another language. Standard quality testing often misses those paths.
It also matters because safeguards are a moving target. Providers improve refusal behaviour, monitoring, moderation, and tool restrictions over time, but attackers adapt too. AISI's work on safeguard evaluation and frontier testing reflects this reality. Safeguards need to be measured against realistic attempts to bypass them, not just checked against policy documents.
For leaders, the real advantage is prioritisation. Red teaming helps distinguish theoretical worries from reachable failure modes in your actual system. That makes it easier to decide which risks deserve engineering effort, which need process controls, and which should block release.
How it works
The first step is threat modelling. OpenAI's external red teaming paper treats this as the structured process of identifying and prioritising potential risks and vulnerabilities, and its campaign design guidance starts with open questions, threat models, and scope. In simple terms, before testing begins you need to decide what you are worried about, who the likely attacker or misuser is, and whether you are red teaming a base model, a deployed system, or both.
The second step is choosing the red team. Good red teaming is not only about technical cleverness. It is also about perspective. OpenAI highlights the importance of diverse cohorts and domain expertise. Anthropic describes using subject matter experts for high risk areas such as policy vulnerability testing, national security related threats, multilingual testing, and multimodal risks. So the right team depends on the harms you care about. A procurement copilot may need finance, legal, and security perspectives. A public facing assistant may need multilingual testers and people familiar with harassment or misinformation patterns.
The third step is deciding access and environment. Some campaigns happen against early checkpoints. Some use deployed models. Some use internal tools or sandboxes. Some use the full product experience. OpenAI's GPT 4o system card describes phased external red teaming across different checkpoints and product experiences, which shows why access level matters. Early access can help surface deeper issues, while product level access tests the system users will actually meet.
The fourth step is choosing methods. OpenAI distinguishes manual, automated, and mixed methods. Manual red teaming involves human testers crafting prompts and attack sequences. Automated red teaming uses models or templates to generate adversarial inputs at scale, sometimes with classifiers to assess outputs. Mixed approaches often start with expert manual probing, then scale promising attack patterns into broader automated testing. Anthropic describes a similar iterative loop from qualitative red teaming to quantitative evaluation.
The fifth step is executing scenarios and documenting findings. NIST's ARIA pilot describes red teaming as stress testing to induce adverse behaviour and break guardrails in controlled scenarios. In a business setting, those scenarios might include getting a system to reveal confidential information, take an unauthorised action, provide unsafe advice, fabricate facts with confidence, or bypass content restrictions. Good documentation records the prompt path, system state, harm category, reproducibility, and severity. Without that record, engineering teams struggle to convert findings into fixes.
The sixth step is turning discoveries into durable controls. Anthropic and OpenAI both emphasise that red teaming should feed an iterative loop. The strongest finding types become regression tests or automated evaluations. Mitigations are added. The system is retested. This matters because red teaming is resource intensive. Its value grows when the organisation converts one person's clever attack into a repeatable test that can run on every future change.
There are also boundaries on interpretation. OpenAI warns that a single red teaming effort is not a panacea for AI risk assessment and that findings can age quickly as systems change. Anthropic notes the lack of standardised practice and the difficulty of objectively comparing safety across systems. That means red teaming should inform release decisions, not masquerade as absolute proof that a system is safe because "the red team did not find anything".
Examples
A company deploying an internal assistant with access to file search and messaging tools might red team for prompt injection, privilege escalation, and unintended disclosure of confidential material. The relevant question is not only whether the language model refuses bad prompts. It is whether the full system, including retrieval and tool permissions, can be manipulated into doing something it should not.
A customer service chatbot may be red teamed in multiple languages to see whether the same safety standards hold across English, Mandarin, Tamil, or Malay. Anthropic points to multilingual and multicultural testing as a way to widen representation and catch failure modes that English only testing can miss.
A coding assistant used by engineers may be red teamed inside a sandbox to test unsafe shell commands, exfiltration prompts, or attempts to subvert repository controls. That is closer to system level adversarial testing than to a simple prompt quality review.
A public facing assistant may also be red teamed to seed future safety evals. Once the team sees repeated patterns such as a certain jailbreak family or policy loophole, those attacks can be codified into repeatable regression tests.
Common misunderstandings
One misunderstanding is that red teaming just means trying rude prompts until the system slips. That is a very narrow slice of the practice. Serious red teaming can involve tools, context attacks, multimodal inputs, domain experts, and longer attack chains.
Another is that red teaming is the same as a penetration test. There can be overlap, especially when AI features are embedded in software systems, but AI red teaming also covers behavioural, policy, and sociotechnical weaknesses that ordinary security testing may not target.
A third is that passing one red teaming exercise means the system is safe. OpenAI explicitly notes the limits of point in time campaigns, and Anthropic notes the lack of standardisation across methods. Red teaming gives evidence, not final certainty.
A fourth is that only frontier model labs need to do this work. Any organisation deploying AI in a way that could create material risk should think adversarially, even if its version is smaller in scope and more tightly focused on the failure modes that matter to its own workflow.
Risks and boundaries
Red teaming is powerful, but it is not cheap. It takes time, expertise, controlled environments, and clear rules. OpenAI explicitly describes the practice as resource intensive. A weakly scoped campaign can burn money, generate anecdotal horror stories, and still leave the organisation unsure what to do next.
Coverage is another limit. A red team never explores every attack path, in every language, against every future system version. The testers' skill and perspective shape what gets found. Anthropic's own writing highlights the challenge of representation and the need for wider testing contexts. That is why retesting and combination with broader evals matter.
There is also a safety boundary. Some tests should only happen in secure sandboxes with approved supervision, especially where cyber, privacy, or harmful domain knowledge is involved. This article is a practical explainer, not security or legal advice.
What to do next
Start with your highest risk AI workflows and write down three to five concrete abuse or failure scenarios for each one. Do not start with a generic "break this model" brief. Start with the ways your actual system could cause harm or breach policy.
Choose a mixed team. Bring in product, security, domain owners, and if the stakes justify it, external expertise. Define rules of engagement, environment, severity levels, and what evidence red teamers must capture.
Most importantly, plan the feedback loop before the exercise starts. Decide how discoveries become mitigations, regression tests, and release decisions. Red teaming creates most value when it changes the product, not when it ends as a PDF.
FAQs
Is red teaming the same as AI evals?
No. Red teaming is a specific adversarial form of evaluation that looks for failures, abuse paths, and guardrail bypasses.
Should we use internal or external red teamers?
Often both. Internal teams help early and know the system well, while external teams can add independence and specialist expertise.
Can small organisations do AI red teaming?
Yes, but the scope should match the risk. A focused scenario based exercise is usually better than a vague, broad campaign.
What kinds of issues does red teaming find?
Common targets include jailbreaks, privacy leakage, unsafe tool use, fabricated facts under pressure, and policy bypasses in unusual contexts.
How often should we red team?
Repeat when the model, tools, safeguards, or deployment context change materially, especially before major releases.
If a system passes red teaming, is it safe?
No. It is better tested, not guaranteed safe. Red teaming is one input into a larger assurance and risk management process.
Sources
Assessing Risks and Impacts of AI: Pilot Evaluation Report (NIST). Primary. Stress testing and guardrail breaking in controlled red teaming scenarios.
Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile (NIST). Primary. Relationship between red teaming, TEVV, and governance roles.
AI Safety Institute approach to evaluations (UK Government). Primary. Positioning red teaming as one part of a broader evaluation toolkit.
Principles for safeguard evaluation (AI Security Institute). Primary. Current practice around evaluating misuse safeguards before and after deployment.
Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations (NIST). Primary. Distinction between wider adversarial ML security issues and red teaming practice.