What Is AI Red Teaming? Complete 2026 Guide
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Most organizations building with generative AI have already shipped a chatbot, a coding assistant, or a document-search tool that can be pushed into saying, revealing, or doing something it should never have said, revealed, or done — and most of them found out from a user, not from a test. AI red teaming is the discipline built to find that failure before a user does, by deliberately probing an AI system the way a motivated adversary, a curious edge-case user, or an unlucky combination of inputs would. This guide explains what AI red teaming actually is, how it differs from the cybersecurity red teaming many teams already know, what it tests, who should do it, and how to run one well, grounded in guidance from NIST, OWASP, MITRE, Microsoft, Google, Anthropic, and OpenAI.
TL;DR
AI red teaming is structured, adversarial testing of an AI system to find security, safety, and misuse failures before real users or attackers do.
It covers more than jailbreaks: prompt injection, data leakage, RAG poisoning, tool misuse by agents, bias, and hallucination all fall inside its scope.
It differs from traditional cybersecurity red teaming because AI systems fail probabilistically, through natural language, and through emergent behavior, not just through code-level bugs.
No single test proves an AI system is safe; red teaming is one layer in a broader assurance program that includes evaluation, monitoring, and governance.
NIST, OWASP, MITRE ATLAS, Microsoft, Google, Anthropic, and OpenAI each publish red-teaming guidance, and their terms do not always mean exactly the same thing.
A useful red-teaming program combines manual expert testing, automated tooling, and continuous production monitoring, not any one of these alone.
Quick Answer: What Is AI Red Teaming?
AI red teaming is the structured practice of testing an AI system — a model, an application, or a full production pipeline — by deliberately trying to make it fail, in order to find security vulnerabilities, safety gaps, and misuse pathways before real users or attackers do. It combines security testing, adversarial machine learning, and human judgment, and it is one part of a broader AI assurance program, not a one-time proof of safety.
Table of Contents
What Is AI Red Teaming?
AI red teaming is the practice of deliberately attacking, probing, or stress-testing an AI system to uncover the ways it can fail, be misused, or be manipulated, before that failure happens in production. The term borrows from military and cybersecurity "red teams," which play the role of an adversary against their own side's defenses. Applied to AI, a red team plays the role of an attacker, a malicious user, or simply an unlucky combination of inputs, and tries to make the system do something harmful, insecure, or embarrassing.
A more precise, technical definition: AI red teaming is a structured evaluation activity, using human testers, automated tools, or both, that generates adversarial or edge-case inputs against an AI model, application, or end-to-end system, and analyzes the outputs against defined risk categories such as security, safety, privacy, and misuse. The goal is not to score the system on a benchmark. The goal is to discover concrete, reproducible failure modes and feed them back into the people who can fix them.
The term does not mean one single thing across the industry. The OWASP GenAI Security Project describes red teaming across four areas: model evaluation, implementation testing, infrastructure assessment, and runtime behavior analysis. Microsoft's AI Red Team, one of the industry's oldest, tests everything from individual models to full copilots and connected infrastructure. OpenAI and Anthropic use red teaming to describe both internal pre-release testing and structured programs with external domain experts. NIST's Generative AI Profile treats red teaming as one input into a broader risk-management process, not a stand-alone guarantee.
Note: When someone says "we red-team our AI," ask what they tested — the base model, a fine-tuned version, the customer-facing application, or the full production system with its data pipelines and tools. Each answer implies a very different scope of assurance.
Why the Scope of Testing Matters
A base foundation model, a fine-tuned model, an application built on it, and the complete production system are four different things to test — passing one does not mean the others are safe.
Base model testing looks at the raw capabilities and failure modes of the model itself, independent of any product built on top of it.
Fine-tuned model testing checks whether task-specific training introduced new weaknesses or removed safety behavior that existed in the base model.
Application testing covers the system prompt, guardrails, input and output filters, and business logic wrapped around the model.
Full system testing covers the application plus its data sources, tools, plugins, authentication, infrastructure, and monitoring — the parts that most often produce real incidents.
Microsoft's AI red team found that many of the most damaging failures they uncovered while testing over 100 generative AI products came from system-level integration issues rather than exotic attacks on the model itself, a pattern that argues strongly for testing the whole system rather than the model in isolation (Bullwinkel et al., 2025).
Pre-Deployment Versus Post-Deployment Testing
Red teaming can happen before a system ships and after it is live. Pre-deployment testing catches known categories of failure before real users are exposed to them. Post-deployment, or continuous, red teaming catches failures that only appear with real traffic and data — model updates, new tool integrations, and newly discovered attack techniques all shift the risk picture after launch.
The Role of Human Judgment and Automated Testing
Automated tools can generate thousands of adversarial prompts and score responses quickly, which matters given how much surface area modern AI systems expose. But Microsoft's red-team lessons paper is explicit that tools like its own PyRIT framework expand coverage without replacing the human judgment needed to prioritize risks and evaluate harms requiring subject-matter or cultural expertise (Bullwinkel et al., 2025). OpenAI's external red-teaming paper makes a similar point: automated evaluations are often built from findings that human red teamers surface first (OpenAI, 2025).
Why Model-Level Testing Alone Is Insufficient
A model that passes every safety benchmark can still power an application that leaks customer records, because the leak comes from a misconfigured retrieval index or an overly permissive tool, not from the model's training. Serious programs test the model, the prompts around it, the data it retrieves, the tools it can call, and the infrastructure it runs on as one connected system.
Why AI Red Teaming Matters
Generative AI systems fail in ways that traditional software testing was never built to catch. A conventional application has a fixed set of code paths; a bug is a deterministic, reproducible defect in that code. A large language model produces different outputs for the same input, follows instructions embedded in the data it processes as readily as instructions from its developer, and can be steered by phrasing alone rather than by exploiting a coding error. Vulnerability scanners built for web applications do not know how to test any of that.
Regulators now require exactly this kind of testing for the highest-risk models. The EU AI Act obligates providers of general-purpose AI models carrying "systemic risk" to perform model evaluation using state-of-the-art protocols, explicitly including documented adversarial testing, under Article 55. NIST's Generative AI Profile, published July 26, 2024, gives organizations more than 200 suggested actions for risks unique to generative AI, spanning confabulation, data privacy, harmful bias, and information security (NIST, 2024).
The stakes are concrete, not hypothetical. Prompt injection has been demonstrated against real, deployed systems, not just prototypes: researchers showed Bing's GPT-4-powered chat and code-completion engines could be compromised through content an attacker placed on a web page the assistant summarized, with impacts including data theft (Greshake et al., 2023). That paper reframed how security researchers think about the boundary between "data" and "instructions" in an AI-integrated application.
Warning: Treating a passed red-team exercise as proof that a system is "safe" is itself a risk. A red team samples a vast space of possible inputs; it cannot exhaustively test every prompt, every user, or every future model update. Absence of a finding is not evidence of absence of a vulnerability.
What Organizations Actually Get From Red Teaming
A concrete, reproducible list of ways the system currently fails, ranked by severity and likelihood, rather than a vague sense that "the model is pretty good."
Evidence for governance, insurance, procurement, and — for the largest general-purpose models — regulatory obligations.
Input for automated regression tests and evaluation suites, so that a manually discovered failure does not have to be manually rediscovered after every model update.
A realistic picture of what an application-integrated model can be made to do, which frequently differs from what the underlying model provider's own safety testing measured.
AI Red Teaming vs Traditional Cybersecurity Red Teaming
Traditional cybersecurity red teaming and AI red teaming share a mindset — think like an adversary — but differ in what they test and how failure looks. A cybersecurity red team targets networks, applications, and people, chasing exploitable code vulnerabilities, misconfigurations, and social-engineering openings, usually against a fixed, deterministic target: the same input produces the same output every time. An AI red team targets a model's behavior in addition to its infrastructure, and that behavior is probabilistic — the same prompt can produce different responses on different runs, and a phrasing change with no security-relevant meaning can flip a refusal into compliance.
AI red teaming also draws on adversarial machine learning, a research field with its own decade of literature on how models can be fooled, poisoned, or made to leak their training data, catalogued in NIST's taxonomy of attacks including evasion, poisoning, privacy, and abuse (Vassilev et al., NIST AI 100-2e2025). None of that has a direct analogue in conventional penetration testing.
How AI Red Teaming Compares to Related Practices
Practice | Primary focus | Typical output |
Traditional cybersecurity red teaming | Networks, code, infrastructure, people | Exploited vulnerabilities, attack paths |
Penetration testing | Known vulnerability classes in a defined scope | Vulnerability report with severity ratings |
Vulnerability scanning | Automated, signature-based detection | List of known CVEs or misconfigurations |
Threat modeling | Structured analysis of what could go wrong, before testing | Threat model diagrams, risk register |
Model evaluation | Standardized benchmark performance | Scores on fixed test sets |
Safety evaluation | Policy-violating or harmful model outputs | Rate of unsafe outputs on defined categories |
AI red teaming | Model, prompts, data, tools, and system together, adversarially | Concrete failure cases across security, safety, and misuse |
Quality assurance | Functional correctness against a spec | Bug reports, test-pass rates |
Responsible-AI assessment | Fairness, bias, transparency, societal impact | Impact assessment, mitigation recommendations |
Compliance audit | Conformance to a standard or regulation | Audit findings, certification status |
Bug bounty | Crowdsourced vulnerability discovery, reward-based | Disclosed vulnerabilities, payouts |
Blue teaming | Detection and defense of a live system | Detection rules, incident response improvements |
Purple teaming | Collaborative red-and-blue exercise | Improved detection informed by attack knowledge |
Two distinctions are worth stating plainly. First, penetration testing is usually performed by external firms against a defined, static scope for audit purposes, while AI red teaming is more often conducted by practitioners closely tied to the product team, iterating alongside development (Promptfoo, 2025). Second, AI red teaming is not the same as safety benchmarking: a model can score well on a fixed evaluation set and still fail against a red teamer who adapts based on the system's responses, because benchmarks are static and adversaries are not.
What Does an AI Red Team Test?
An AI system is not just a model; it is a stack of interacting layers, and weaknesses often emerge from the interaction between layers rather than from any single one. A red team that only pokes at the model and ignores the rest of the stack will miss most of the failures that matter in production.
The layers a thorough AI red team considers include:
Data — training data, fine-tuning data, and any documents or records the system can retrieve at run time.
Model — the weights and architecture themselves, including their tendency toward hallucination, bias, or unsafe completions.
Prompts and system instructions — the hidden instructions that shape the model's behavior, which can themselves be leaked, overridden, or bypassed.
Retrieval and knowledge sources — the documents, databases, or search indexes a retrieval-augmented system draws on.
Application code — the business logic that calls the model and interprets its output.
APIs — the interfaces that connect the application to the model and to other services.
Plugins, tools, and external services — anything an agentic system can invoke, from a calculator to a code interpreter to an email client.
Identity and permissions — who and what is authenticated to do what, including whether the AI system itself has more access than it needs.
Infrastructure — the servers, containers, and cloud services the system runs on.
User interface — how outputs are displayed, and whether the interface itself can be abused, for example through rendered links or embedded scripts.
Monitoring and logging — whether anomalous behavior would even be visible to a human after the fact.
Human workflows — the people who review, approve, or act on AI-generated output, and where they might over-trust it.
Runtime behavior — how the system behaves in live conditions that differ from the test environment.
A useful way to see why interactions matter more than any single layer: a model with excellent built-in safety training, connected through an over-permissioned plugin to a customer database, with no output filtering and no monitoring, is not a safe system, even though the model component would pass most standalone evaluations. The vulnerability lives in the gaps between components, not inside any one of them.
The Main Risks AI Red Teams Investigate
The risks an AI red team investigates fall into a few natural groups: risks to the integrity and confidentiality of the model and its data, risks from the content the system produces, risks from giving the system too much autonomy, and risks to availability and cost.
Data, model, and input integrity risks. Prompt injection is an attempt to override a system's intended instructions using crafted input. Indirect prompt injection hides that instruction inside a document, web page, or email the AI processes on a user's behalf, rather than typing it directly — the class Greshake and colleagues demonstrated against real production systems in 2023. Jailbreaking gets a model to bypass its safety training. Model or data poisoning corrupts training or fine-tuning data to embed a hidden, attacker-chosen behavior. Evasion and adversarial examples are inputs crafted to be misclassified. Training-data leakage lets a model be prompted into reproducing memorized training snippets, including personal information.
Output and content risks. Insecure output handling happens when an application trusts model output enough to execute, render, or pass it to another system without validation — treating generated text as safe HTML or a database query, for example. Hallucination and fabricated citations sound authoritative but are false, with obvious consequences in legal, medical, or financial contexts. Misinformation, bias and discrimination, and toxic content are each evaluated separately because each needs different detection and remediation.
Agency and system-integration risks. Excessive agency describes an agent given more autonomy or tool access than its task requires. Tool misuse and permission failures are how excess agency becomes an incident — an agent that can send email being tricked into exfiltrating data through that same channel, for instance. Cross-tenant leakage happens when a multi-customer system lets one customer's data reach another's session.
Retrieval-specific risks. RAG data poisoning and retrieval manipulation target the documents a retrieval-augmented system indexes and serves, on the theory that if you cannot attack the model directly, you can attack what it is told to trust. NIST's adversarial machine learning taxonomy extends its generative-AI coverage specifically to this category, alongside direct and indirect prompt injection and misuse violations (OECD.AI summary of NIST AI 100-2e2025).
Availability, cost, and supply-chain risks. Denial of service, resource exhaustion, and cost-amplification attacks — deliberately triggering expensive model calls to run up a victim's bill — are unique to metered, compute-intensive AI services. Supply-chain risks cover the security of models, datasets, and libraries an organization did not build itself. Multimodal attacks target systems accepting images, audio, or video alongside text. Agent memory risks and goal manipulation apply to systems that persist state, where a poisoned memory can steer many future actions. Unsafe autonomous actions and cybersecurity misuse round out the highest-impact category: could this system write functional malware or take a harmful action on its own initiative?
Note: This is a categorization scheme for organizing an investigation, not a checklist to complete once. Real findings usually combine two or three of these categories at once — for example, an indirect prompt injection that triggers excessive agency, which then causes cross-tenant data leakage.
Types of AI Red Teaming
No single method of red teaming covers every risk, which is why mature programs combine several.
Manual and expert-led red teaming relies on skilled humans crafting adversarial inputs by hand. It remains essential for discovering genuinely new categories of harm, since a human can notice that a system's behavior is troubling in a way no existing test was written to check for — exactly the kind of discovery Microsoft's red team credits to human creativity in its published lessons.
External and community red teaming brings in testers who were not involved in building the system, on the theory that fresh eyes and outside expertise catch what an internal team's assumptions might miss. OpenAI's Red Teaming Network formalizes this as an ongoing relationship with outside domain experts rather than a one-off engagement before each launch (OpenAI, Red Teaming Network).
Automated and model-assisted red teaming uses software, sometimes including other AI models, to generate and test large volumes of adversarial prompts quickly. Microsoft's PyRIT (Python Risk Identification Tool) is an open-source example, built specifically because fully manual testing became impractical at the scale and pace of Microsoft's AI product development (Bullwinkel et al., 2025). Automation trades some depth of judgment for breadth of coverage, and works best paired with human review of what it finds.
Black-box, grey-box, and white-box testing describe how much internal knowledge the tester starts with — none, partial, or full access to the model's architecture, training data, and system prompts respectively — mirroring the same distinctions used in conventional penetration testing.
Open-ended exploration versus scenario-based testing is a distinction in structure rather than access. Open-ended exploration lets testers follow their instincts wherever the system's behavior looks interesting or wrong. Scenario-based testing works from a predefined persona and goal, such as "a user trying to extract another customer's data through the support chatbot," and is easier to reproduce and compare across test rounds.
Policy-vulnerability testing checks specifically whether a system violates its own stated usage policies, which is a narrower and more legally relevant question than "can this system be made to say something bad." Capability elicitation asks a different question again: what is this model actually capable of, independent of whether it wants to comply, which matters most for frontier-capability risk assessment. Multilingual and multicultural testing matters because safety training is frequently strongest in the languages and cultural contexts best represented in a model's training and evaluation data, leaving weaker coverage elsewhere. Multimodal testing extends all of the above to image, audio, and video inputs and outputs. Continuous production red teaming treats testing as an ongoing process against the live system rather than a pre-launch checkpoint, which is the only way to catch failures introduced by model updates, new integrations, or shifting user behavior after release.
How AI Red Teaming Works: A Step-by-Step Process
A red-teaming exercise is not one activity; it is a sequence of stages, and several of them repeat rather than run once in a straight line.
Define the purpose. Decide what decision the exercise supports — a launch go/no-go, a compliance obligation, or ongoing monitoring.
Establish authorization and rules of engagement. Written sign-off on what may be tested, by whom, and with what data.
Identify the system boundary. Decide whether the exercise covers the base model, the application, or the full production system.
Build an asset inventory and data-flow view. Map what data the system touches, since you cannot test what you have not mapped.
Develop a threat model. Identify plausible attackers, their goals, and likely methods given the system's actual deployment context.
Identify stakeholders and potentially affected groups. Consider who could be harmed, not only who could exploit a failure.
Select risk categories and test objectives. Choose which risk categories described earlier apply most to this system.
Define attacker and user personas and realistic scenarios. Ground abstract risk categories in concrete, testable situations.
Establish baselines and success criteria. Decide in advance what a "finding" looks like.
Prepare safe testing environments and data-handling rules. Use non-production data and isolated environments where possible.
Perform open-ended discovery. Let skilled testers explore without a fixed script, to surface unknown failure categories.
Conduct structured and automated testing. Run systematic, repeatable tests against the categories chosen in step 7.
Validate and reproduce findings. Confirm a failure is real and repeatable, not a fluke of model non-determinism.
Assess severity, likelihood, exploitability, exposure, and impact. Score each finding so remediation can be prioritized.
Prioritize remediation. Fix based on the assessment in step 14, not on ease of explanation.
Retest mitigations. Confirm the fix closes the gap rather than just changing the symptom.
Convert recurring failures into evaluations and regression tests. Automate the check so the issue does not silently reappear.
Report residual risk and limitations. State plainly what was tested, what was not, and what remains uncertain.
Monitor the deployed system. Watch for the failure patterns found during testing, and for new ones, once live.
Repeat when conditions change. A model update, new tool integration, new data source, or newly disclosed attack technique should trigger another pass.
Steps 11 through 16 are genuinely iterative — testers frequently loop between discovery, structured testing, and validation multiple times within a single engagement — while steps 1 through 10 are largely sequential setup, and steps 17 through 20 extend past the end of any single exercise into the system's ongoing operation.
AI Red Teaming for LLMs, RAG Systems, and AI Agents
Testing needs change as a system moves from a plain conversational large language model to a knowledge assistant built on retrieval-augmented generation to a tool-using autonomous agent, because each architecture adds a new attack surface on top of the last.
Large language models on their own are tested mainly for jailbreaking, direct prompt injection, hallucination, bias, toxic content, and training-data leakage. Ganguli and colleagues at Anthropic documented some of the earliest systematic work here, finding that models trained with reinforcement learning from human feedback became progressively harder to red-team as they scaled, while other training approaches showed a flatter trend (Ganguli et al., 2022). That paper also released a public dataset of nearly 39,000 red-team attacks.
Retrieval-augmented generation systems add a new attack surface: the knowledge source the model retrieves from before answering. RAG-specific testing checks whether an attacker can poison the underlying documents or index, whether retrieval can be manipulated to surface attacker-chosen content, and whether one tenant's documents stay isolated from another's in a shared knowledge base. Because a RAG system often trusts retrieved text like its own instructions, indirect prompt injection through poisoned documents is one of the most consequential risks here — the failure class Greshake and colleagues first demonstrated in production systems.
Tool-using AI agents add the largest attack surface, because an agent takes actions through APIs and plugins rather than just generating text. Testing means asking what happens if its plan is hijacked mid-task, whether it respects the permission boundaries of its account, and whether its memory can be poisoned to influence future tasks. Excessive agency matters most here: an agent that can read email, browse the web, and send messages combines capabilities that together enable exfiltration none of them could alone.
A concrete defensive example. Consider an enterprise knowledge assistant built on RAG, connected to a company wiki and given a tool to draft emails. A red team would check whether a document uploaded by a lower-privileged employee can contain hidden instructions the assistant later follows for a higher-privileged user; whether the assistant reveals wiki content the querying user is not authorized to see; and whether the email tool can be triggered to send content externally based on manipulated retrieved text rather than the user's actual request. Each maps to a distinct mitigation — retrieval access control, output filtering before tool calls, and least-privilege scoping of the email tool.
How to Build an Effective AI Red Team
An effective AI red team is deliberately cross-disciplinary, because the failure modes span security, safety, fairness, law, and product design, and no single specialty covers all of them.
Useful participants include AI/ML engineers who understand how the model works; security and application-security specialists bringing conventional exploit technique; responsible-AI and trust-and-safety experts covering harm categories beyond pure security; privacy professionals; legal and compliance staff who translate findings into regulatory exposure; product managers grounded in real user workflows; domain experts for specialized fields such as medicine or finance; social scientists and human-factors specialists; members of communities the system could disproportionately affect; independent external testers; and an executive risk owner accountable for acting on findings.
Diversity of expertise changes what a red team finds, because different backgrounds surface different plausible-attacker and plausible-harm scenarios a homogeneous team would not think to test. Microsoft's red-teaming lessons emphasize that cultural context, subject-matter accuracy in fields like medicine, and emotional nuance require expertise automation and generalist testers cannot substitute for.
Representative Roles and Responsibilities
Role | Primary responsibility |
AI/ML engineer | Understands model architecture, training, and technical limitations |
Security/AppSec specialist | Applies exploit techniques and secure-development practice to the system |
Responsible-AI / trust-and-safety expert | Defines and evaluates harm categories beyond security |
Privacy professional | Assesses data handling, retention, and disclosure risk |
Legal / compliance lead | Translates findings into regulatory and contractual exposure |
Product manager | Grounds testing in real user workflows and business context |
Domain expert | Validates accuracy and risk in specialized subject areas |
Independent external tester | Brings perspective free of internal assumptions |
Executive risk owner | Accountable for prioritizing and acting on findings |
A small organization will not staff every one of these as a dedicated role, and that is fine; the point is to make sure each perspective is represented by someone, even if one person covers several, rather than to hire a large standing team before running a first exercise.
AI Red Teaming Tools, Frameworks, and Standards
Tools and frameworks make AI red teaming more consistent and scalable, but none of them replace the judgment described in the previous section — they organize and accelerate it.
NIST's AI Risk Management Framework (January 2023) and its Generative AI Profile (NIST AI 600-1, July 26, 2024) give organizations a structured way to govern, map, measure, and manage AI risk, identifying twelve risk areas specific to generative AI including confabulation, data privacy, and information security (DLA Piper, 2024). NIST AI 100-2e2025 adds a formal taxonomy of adversarial machine learning attacks and mitigations across predictive and generative AI (Vassilev et al., NIST).
The OWASP GenAI Security Project's Red Teaming Guide, first published January 22, 2025, is a practitioner-oriented guide covering model evaluation, implementation testing, infrastructure assessment, and runtime behavior analysis, aimed at cybersecurity professionals, AI/ML engineers, red team practitioners, and CISOs (OWASP GenAI Security Project, 2025). The related OWASP Top 10 for LLM Applications (2025) ranks the most critical LLM-specific risks, led by prompt injection and sensitive information disclosure (OWASP, 2025).
MITRE ATLAS (Adversarial Threat Landscape for Artificial-Intelligence Systems) is a living, ATT&CK-style knowledge base of adversary tactics and techniques against AI-enabled systems, built from real-world attack observations and red-team demonstrations (MITRE ATLAS). It gives red teams a shared vocabulary for describing attacks and mapping them to documented real-world case studies.
Microsoft's AI Red Team publishes both operational lessons and tooling. Its whitepaper "Lessons From Red Teaming 100 Generative AI Products" distills eight practical lessons from testing over 100 products, and its open-source PyRIT framework automates prompt orchestration and response scoring for red-team operations (Bullwinkel et al., 2025).
Google's Secure AI Framework (SAIF) is a conceptual framework for securing AI systems across six core elements, with red teaming positioned inside its assurance controls; Google also runs continuous automated red teaming against its own Gemini models as part of its safety process (Google, SAIF).
Anthropic and OpenAI each publish red-teaming research rather than a packaged tool: Anthropic's early work on red-teaming language models to reduce harms established scaling behaviors and released a public attack dataset (Ganguli et al., 2022), while OpenAI's white paper on external red teaming documents its process for selecting red-team composition, access levels, and guidance for outside experts (OpenAI, 2025).
Note: No tool on this list, including PyRIT or any commercial scanner, constitutes a complete red-team exercise by itself. Running an automated scan and calling it "AI red teaming" skips the threat modeling, human judgment, and system-level testing that give the practice its value.
How to Measure and Report AI Red Teaming Results
Measuring red-teaming results well means resisting false precision. A raw count of successful attacks is not a prevalence estimate for how often real users would trigger the same failure; it depends heavily on how hard the tester tried and how the test was designed.
Useful metrics include test coverage against the risk categories chosen for the exercise; attack success rate and policy-violation rate for specific, defined scenarios; severity and reproducibility of each finding; exposure, meaning how easily a real user or attacker could trigger it; detectability and time to detection, meaning whether existing monitoring would even notice; time to remediation and regression rate, meaning whether fixed issues stay fixed; and differences in outcomes across languages, user groups, and modalities, since safety behavior is rarely uniform across all of these.
A simple, consistent finding-record template keeps results usable across a team and across time. Useful fields include: Finding ID, Scenario, Preconditions, Affected component, Observed behavior, Expected behavior, Evidence, Impact, Severity, Reproducibility, Recommended mitigation, Owner, Status, and Retest result. Recording qualitative findings alongside the numbers matters too — a red teamer's description of *why* a failure felt concerning often carries information a severity score alone cannot capture, and confidence and uncertainty about a finding should be recorded honestly rather than smoothed over.
Turning Red-Team Findings Into Effective Mitigations
Mitigation for AI systems works best as defense in depth, because no single control reliably closes every gap a red team can find, and simplistic fixes such as keyword filters or a tweaked system prompt rarely address a system-level problem on their own.
Layered mitigations include model-level safeguards from training or fine-tuning; carefully designed system prompts and a clear instruction hierarchy that separates developer instructions from user and retrieved content; input and output controls, including filtering and validation before content reaches a user or a downstream tool; retrieval controls and data filtering for RAG systems; access control and least privilege for both users and the AI system's own permissions; tool restrictions that limit what an agent can actually do; human approval gates for high-impact actions; sandboxing of risky operations; rate limits to blunt automated abuse; and monitoring, logging, and detection so that a failure that slips past every other control is at least visible afterward. User-interface design, policy changes, staff training, incident response planning, ongoing evaluation and regression testing, and vendor and supply-chain controls round out a genuinely layered program.
Every recurring red-team finding is also an opportunity: converting it into a permanent automated regression test means the same failure gets caught automatically after the next model update, rather than waiting to be manually rediscovered.
AI Red Teaming Examples and Scenarios
These scenarios are illustrative, defensive testing patterns, not accounts of specific real-world incidents.
A customer-support chatbot. Objective: protect account data and prevent policy violations. Failure mode: persistent reframing convinces the bot to issue an out-of-policy refund or reveal another customer's order details. Testing approach: scenario-based personas probing authorization boundaries. Evidence: transcript logs showing the triggering phrasing. Mitigation: tightened authorization checks plus output filtering on personal data. Retest: the same and paraphrased phrasing after the fix ships.
An enterprise RAG assistant. Objective: answer only from authorized internal documents. Failure mode: a document from one team leaks into answers served to another team without the right access level. Testing approach: grey-box testing with knowledge of the retrieval index's permission model. Evidence: a low-privilege test account retrieving content it should not see. Mitigation: document-level access control inside the retrieval layer itself, not only the application's front door. Retest: the same query plus adjacent queries reaching the same document indirectly.
A coding assistant. Objective: generate secure, functional code. Failure mode: the assistant reproduces an insecure pattern, such as an injection-prone database query, for a common task. Testing approach: structured testing against known-vulnerable code patterns. Evidence: generated snippets flagged by static analysis. Mitigation: guardrails blocking known-insecure patterns, plus developer training on reviewing AI-generated code. Retest: the same prompt category after guardrails ship.
A document-processing workflow. Objective: extract and summarize documents accurately. Failure mode: hidden text in a document triggers an unintended action, such as altering the summary instructions. Testing approach: indirect-prompt-injection testing with embedded instructions. Evidence: output reflecting the injected instruction rather than the user's actual request. Mitigation: treat all document content as untrusted data, clearly separated from system instructions. Retest: varied injection phrasing and formats, including hidden text.
A tool-using AI agent. Objective: complete multi-step tasks without exceeding intended scope. Failure mode: a manipulated intermediate result, such as content from a visited web page, redirects the agent toward a higher-impact action. Testing approach: scenario-based testing that manipulates tool outputs mid-task. Evidence: an action trace showing where the plan diverged. Mitigation: least-privilege tool scoping and human approval before high-impact actions. Retest: the same manipulation against the patched permission model.
Legal, Ethical, and Governance Considerations
Every red-teaming exercise needs written authorization defining its scope and rules of engagement before testing starts, along with data-minimization practices, confidentiality protections, and a clear plan for handling any genuinely harmful material the testing surfaces. Tester wellbeing matters too, since red teamers repeatedly exposed to disturbing content can experience real psychological strain, and responsible programs plan for that rather than treating it as an afterthought.
Programs also need a responsible-disclosure process for any third-party vulnerabilities discovered along the way, explicit permission before testing any system the organization does not own, clear record retention and auditability, defined escalation paths for serious findings, and attention to independence and conflicts of interest when the same team that built a system also tests it.
Legal and regulatory obligations differ meaningfully by jurisdiction. The EU AI Act's Article 55 creates binding adversarial-testing obligations specifically for providers of general-purpose AI models with systemic risk (EU AI Act, Article 55), and other jurisdictions are developing their own expectations at different speeds. This article does not constitute legal advice; organizations should obtain qualified legal counsel for their specific circumstances and jurisdiction.
Common AI Red Teaming Mistakes and Limitations
Even a well-run exercise has real limitations, and ignoring them is itself a common mistake. Coverage is always incomplete relative to the space of possible inputs. Testers carry their own biases about what is worth testing. Results are a snapshot in time, since the model, data, and connected tools all keep changing. Probabilistic failures are hard to reproduce reliably, and test data can leak into future training data, contaminating repeated tests. Automated benchmarks can be gamed or overfit. Automation has blind spots exactly where human judgment matters most, and there are always unknown unknowns — failure modes nobody thought to test for. Findings are context-dependent: a mitigation that works in one deployment may not transfer to another. Estimating real-world prevalence from a red-team exercise is difficult, since testers try harder than an average user. Mitigations can also displace risk rather than eliminate it. The most consequential mistake is false confidence: treating a "passed" exercise as a permanent guarantee rather than a time-bound, scope-bound finding.
The Future of AI Red Teaming
Several directions look like likely near-term trends rather than settled facts. Automated and model-assisted testing will likely keep expanding, since fully manual testing already cannot keep pace with deployed AI systems' number and complexity, a dynamic Microsoft and OpenAI both describe directly. Human-AI collaboration will likely deepen rather than move toward full automation, since the organizations investing most in tooling also emphasize that human judgment remains essential.
Continuous, production-level red teaming is likely to become more standard than pre-launch-only testing, especially for frequently updated systems. Testing for agentic and multimodal systems will likely grow more sophisticated, an area MITRE ATLAS is already expanding with agent-focused techniques. Greater standardization through MITRE ATLAS, NIST's taxonomy, and OWASP's guides, plus more reusable shared evaluations, is a plausible path toward less duplicated effort industry-wide. Cross-organization information sharing and independent third-party assessment both look likely to expand as obligations like the EU AI Act's Article 55 push toward documented, externally verifiable testing. Red teaming is also likely to integrate more tightly with secure software development practice, alongside a growing focus on systemic and societal risks beyond any single system.
Frequently Asked Questions
What is AI red teaming, in simple terms?
AI red teaming means deliberately trying to break an AI system — get it to say something harmful, leak data, or take a bad action — before real users or attackers find those weaknesses. It combines security testing, adversarial machine learning, and human judgment across the model, its data, and the application built around it.
How is AI red teaming different from penetration testing?
Penetration testing targets known vulnerability classes in networks and applications, usually by external auditors against a fixed scope. AI red teaming also tests probabilistic model behavior and natural-language manipulation, and often runs iteratively alongside product development rather than as a one-time audit.
Is AI red teaming only for large language models?
No. It applies to any AI system, including classifiers, recommendation engines, computer-vision models, and retrieval-augmented or agentic systems. LLMs receive the most attention today because of their rapid, widespread deployment, but the same adversarial-testing principles apply to other AI architectures.
When should AI red teaming happen?
Both before and after deployment. Pre-deployment testing catches known failure categories before launch. Post-deployment, or continuous, red teaming catches issues that only appear with real traffic, data, model updates, and evolving attack techniques, and should repeat whenever the system changes materially.
Who should participate in an AI red team?
Effective teams mix AI/ML engineers, security specialists, responsible-AI and privacy experts, legal staff, product managers, relevant domain experts, and often independent external testers. Smaller organizations can cover these perspectives with fewer people rather than a large dedicated team.
Can AI red teaming be automated?
Partially. Tools such as Microsoft's open-source PyRIT can generate and score large volumes of adversarial prompts quickly, expanding coverage. However, prioritizing risks, designing novel system-level attacks, and judging nuanced or culturally specific harms still require human expertise that automation has not replaced.
How often should AI red teaming be repeated?
As often as the system changes: after model updates, new tool or data integrations, policy changes, or newly disclosed attack techniques. Many organizations also run continuous, lower-intensity testing against production systems between larger, more formal exercises.
How long does an AI red-teaming exercise take?
It varies widely with scope, from a focused few-day scenario-based exercise on a single feature to multi-week engagements covering a full production system with external testers. The purpose defined at the start of the exercise should determine its depth and duration, not a fixed template.
What outputs does AI red teaming produce?
A well-run exercise produces a documented list of findings with severity, reproducibility, and recommended mitigations, a rules-of-engagement record, and often new automated regression tests derived from the findings. It should not produce a single pass/fail certification.
Can small organizations do AI red teaming?
Yes. A small team can run a scoped exercise using open-source tools, published guides such as the OWASP GenAI Red Teaming Guide, and a handful of internal staff wearing multiple hats, rather than needing a large dedicated red team or expensive external vendor engagement.
What permission is required before red-teaming an AI system?
Written authorization defining scope and rules of engagement for any system an organization owns or is explicitly authorized to test. Testing a third-party system without that organization's permission is not acceptable practice and can carry legal consequences.
Does AI red teaming prove an AI system is safe?
No. It reduces uncertainty by finding concrete failure modes within the tested scope, but cannot exhaustively cover every input, user, or future model update. Absence of a finding is not proof no vulnerability exists.
What is the difference between AI red teaming and AI safety evaluation?
Safety evaluation typically measures a model against fixed, standardized test sets to produce comparable scores. Red teaming adapts based on the system's responses, actively searching for novel failure modes rather than scoring against a predetermined benchmark.
What is prompt injection, and why does it matter for red teaming?
Prompt injection is an attempt to override a system's intended instructions using crafted input, including input hidden inside documents or web content the system processes. It matters because it is currently ranked as the top risk in the OWASP Top 10 for LLM Applications and has been demonstrated against real, deployed systems.
Key Takeaways
AI red teaming is adversarial testing of an AI system's model, data, prompts, tools, and infrastructure together, not just the model in isolation.
It differs from traditional cybersecurity red teaming because AI systems fail probabilistically and through natural-language manipulation, not only through code-level bugs.
NIST, OWASP, MITRE ATLAS, Microsoft, Google, Anthropic, and OpenAI each contribute distinct, complementary guidance rather than one unified standard.
Effective programs combine manual expert testing, automated tooling like PyRIT, and continuous testing of the live production system.
Retrieval-augmented generation and tool-using agents each introduce attack surfaces beyond what plain language-model testing covers.
No red-teaming exercise proves a system is safe; it reduces uncertainty within a defined, time-bound, and scope-bound test.
Findings are only valuable once converted into prioritized mitigations, retested fixes, and permanent regression tests.
Regulatory obligations, including the EU AI Act's Article 55, now require documented adversarial testing for the highest-risk general-purpose AI models.
Actionable Next Steps
Define the purpose of your first red-teaming exercise and get written sign-off on its scope and rules of engagement.
Inventory every AI system in use, including third-party tools, and classify each by the data and permissions it touches.
Select one system-level risk category to start with, such as prompt injection or excessive agent permissions, rather than trying to test everything at once.
Assemble a small cross-functional team covering engineering, security, and at least one non-technical perspective, even if some people cover multiple roles.
Run an initial manual, scenario-based exercise before investing in automated tooling, to build shared understanding of what a finding looks like.
Record every finding using a consistent template, and prioritize remediation by severity and exploitability rather than ease of fix.
Retest every mitigation before considering a finding closed.
Convert recurring findings into automated regression tests so future model or system updates are checked automatically.
Establish a cadence for repeating the exercise, tied to model updates, new integrations, and newly disclosed attack techniques.
Review applicable legal and regulatory obligations for your jurisdiction and system classification with qualified counsel.
Glossary
Adversarial example — An input deliberately crafted to cause a model to produce an incorrect or unintended output.
[AI agent](https://articsledge.com/post/intelligent-agent) — A system that uses an AI model to plan and take actions, often through tools or APIs, toward a goal.
AI red teaming — Structured, adversarial testing of an AI system to find security, safety, and misuse failures.
Attack surface — The full set of points where an attacker could try to interact with or influence a system.
Blue team — The defensive team responsible for detecting and responding to attacks on a live system.
Evaluation — Systematic measurement of a model's performance or behavior against defined test sets.
Excessive agency — Giving an AI agent more autonomy, permissions, or tool access than its task requires.
Guardrail — A control designed to keep a model's behavior within intended, safe boundaries.
Hallucination — A model output that is fabricated or false but presented as if it were factual.
Indirect prompt injection — Prompt injection delivered through content the system processes, rather than typed directly by the user.
Jailbreak — A technique used to bypass a model's safety training and elicit disallowed content.
Large language model (LLM) — A machine-learning model trained on large volumes of text to generate and understand language.
Model extraction — Attempting to reconstruct or steal a proprietary model's behavior or parameters through queries.
Model poisoning — Corrupting training or fine-tuning data to embed a hidden, attacker-chosen behavior.
Multimodal model — A model that can process or generate more than one type of data, such as text and images.
Penetration testing — Authorized, simulated attacks against a defined scope to find exploitable vulnerabilities.
Prompt injection — An attempt to override a system's intended instructions using crafted input.
Purple team — A collaborative exercise combining red-team attacks with blue-team defense to improve detection.
RAG poisoning — Corrupting the documents or index a retrieval-augmented system draws on to manipulate its answers.
[Retrieval-augmented generation](https://articsledge.com/post/naive-retrieval-augmented-generation-rag) (RAG) — An architecture where a model retrieves external documents to ground its answers.
Risk — The potential for a threat to exploit a vulnerability and cause harm.
Sensitive information disclosure — Unintended exposure of private, proprietary, or confidential data through model output.
System prompt — Hidden instructions that shape a model's behavior before a user's own input is added.
Threat model — A structured analysis of plausible attackers, their goals, and their likely methods against a system.
Tool misuse — An AI agent using a connected tool or plugin in an unintended or harmful way.
Vulnerability — A weakness in a system that could be exploited to cause harm.
Sources & References
[1] National Institute of Standards and Technology. "AI Risk Management Framework." NIST, updated 2026 (GenAI Profile released July 26, 2024). https://www.nist.gov/itl/ai-risk-management-framework
[2] DLA Piper. "NIST releases its Generative Artificial Intelligence Profile: Key points." DLA Piper, July 30, 2024. https://www.dlapiper.com/en-us/insights/publications/ai-outlook/2024/nist-releases-its-generative-artificial-intelligence-profile
[3] Vassilev, A., Oprea, A., Fordyce, A., Anderson, H., Davies, X., Hamin, M. "Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations." NIST AI 100-2e2025, National Institute of Standards and Technology, March 2025. https://www.nist.gov/publications/adversarial-machine-learning-taxonomy-and-terminology-attacks-and-mitigations-0
[4] OECD.AI Policy Observatory. "Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations (NIST AI 100-2e2025)." OECD.AI, accessed 2026. https://oecd.ai/en/dashboards/policy-initiatives/adversarial-machine-learning-a-taxonomy-and-terminology-of-attacks-and-mitigations-nist-ai-100-2e2025
[5] OWASP GenAI Security Project. "GenAI Red Teaming Guide." OWASP, January 22, 2025. https://genai.owasp.org/resource/genai-red-teaming-guide/
[6] OWASP. "OWASP Top 10 for LLM Applications 2025." OWASP Foundation, 2025. https://owasp.org/www-project-top-10-for-large-language-model-applications/assets/PDF/OWASP-Top-10-for-LLMs-v2025.pdf
[7] Promptfoo. "OWASP Red Teaming: A Practical Guide to Getting Started." Promptfoo, March 25, 2025. https://www.promptfoo.dev/blog/owasp-red-teaming/
[8] MITRE Corporation. "MITRE ATLAS." MITRE, accessed 2026. https://atlas.mitre.org/
[9] Bullwinkel, B., Minnich, A., Chawla, S., et al. "Lessons From Red Teaming 100 Generative AI Products." Microsoft, arXiv:2501.07238, January 13, 2025. https://arxiv.org/html/2501.07238v1
[10] Microsoft Security Blog. "3 takeaways from red teaming 100 generative AI products." Microsoft, January 13, 2025. https://www.microsoft.com/en-us/security/blog/2025/01/13/3-takeaways-from-red-teaming-100-generative-ai-products/
[11] Google Safety Center. "Google's Secure AI Framework (SAIF)." Google, accessed 2026. https://safety.google/intl/en/safety/saif/
[12] Google Cloud Blog. "The Prompt: Insights from our AI Red Team's first report (Q&A)." Google Cloud, July 26, 2023. https://cloud.google.com/blog/transform/prompt-findings-our-ai-red-teams-first-report-qa
[13] Ganguli, D., Lovitt, L., Kernion, J., et al. "Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned." Anthropic, arXiv:2209.07858, 2022. https://arxiv.org/abs/2209.07858
[14] OpenAI. "OpenAI Red Teaming Network." OpenAI, accessed 2026. https://openai.com/index/red-teaming-network/
[15] Ahmad, L., et al. "OpenAI's Approach to External Red Teaming for AI Models and Systems." OpenAI, January 24, 2025. https://cdn.openai.com/papers/openais-approach-to-external-red-teaming.pdf
[16] OpenAI. "Advancing red teaming with people and AI." OpenAI, 2025. https://openai.com/index/advancing-red-teaming-with-people-and-ai/
[17] Greshake, K., Abdelnabi, S., Mishra, S., Endres, C., Holz, T., Fritz, M. "Not What You've Signed Up For: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection." Proceedings of the 16th ACM Workshop on Artificial Intelligence and Security (AISec '23), arXiv:2302.12173, 2023. https://arxiv.org/abs/2302.12173
[18] European Union. "Article 55: Obligations for Providers of General-Purpose AI Models with Systemic Risk." EU Artificial Intelligence Act, 2024. https://artificialintelligenceact.eu/article/55/