AI red teaming is the discipline of adversarially probing AI systems to find security flaws before attackers do. It borrows from the long tradition of red teaming in cybersecurity — where an independent team plays the role of the attacker — and adapts it for the unique properties of large language models.
As AI systems take on more consequential tasks — answering customer questions, drafting legal documents, executing code, and controlling business workflows — finding their failure modes before deployment has become a security imperative. This guide explains how AI red teaming works, what to test, and how to build it into your development process.
What Is AI Red Teaming?
In traditional security, a red team simulates a real attacker: they use the same techniques, tools, and motivations an adversary would use, with the goal of finding weaknesses in your defences before a real attack occurs.
AI red teaming applies this adversarial mindset to large language models and AI-powered applications. The red team crafts inputs — prompts, documents, API payloads — designed to make the AI behave in unintended, harmful, or insecure ways. Their goal is not to find traditional software bugs, but to surface AI-specific failure modes: prompt injection, jailbreaks, harmful content generation, data leakage, and unsafe agentic behaviour.
The term gained widespread visibility after Microsoft published its AI red teaming methodology in 2023, and after OpenAI, Anthropic, and Google all established internal red teams to evaluate their models before release. The practice has since become standard among organisations deploying AI in regulated or high-stakes environments.
Why AI Red Teaming Matters
LLMs fail in ways that are qualitatively different from traditional software. They are probabilistic, not deterministic. They process natural language, which cannot be sanitised the way code can. They are trained on vast corpora that may contain adversarial patterns. And their failure modes are often not obvious until someone deliberately looks for them.
Without red teaming, organisations discover these failures one of two ways: through user reports after deployment, or through attacker exploitation. Neither is acceptable for high-stakes AI applications.
Regulators are taking notice. The EU AI Act requires conformity assessments for high-risk AI systems that include adversarial testing. The NIST AI Risk Management Framework recommends red teaming as a core evaluation practice. And the White House's AI executive order directs federal agencies to conduct red team evaluations of AI systems before deployment.
AI Red Teaming vs Traditional Penetration Testing
Traditional penetration testing focuses on known vulnerability classes — SQL injection, XSS, authentication bypass, privilege escalation — and uses automated scanners alongside manual techniques to find instances of those classes in the target system.
AI red teaming is structurally different in three ways:
The attack surface is the model's behaviour, not its code. You are not looking for buffer overflows or unvalidated inputs in the traditional sense. You are looking for inputs that cause the model to produce outputs that violate intended behaviour — which requires understanding what intended behaviour is.
The vulnerability space is open-ended. Traditional pen testers work from a finite list of CVEs and vulnerability classes. AI red teamers face a combinatorially large space of possible prompts, contexts, and attacker goals. Creativity and domain expertise matter more than tool proficiency.
Findings are probabilistic. An LLM might produce a harmful output 1 in 100 attempts, or 1 in 10,000. Red teamers need to run enough probes to estimate rates, not just confirm binary pass/fail.
What to Test in an AI Red Team Exercise
Prompt Injection and Jailbreaks
The primary test category for any LLM application. The red team attempts to override the system prompt, extract confidential instructions, or cause the model to take actions outside its intended scope — using direct user inputs, embedded instructions in documents, or indirect injection via retrieved content.
Jailbreak testing specifically targets the model's content safety guardrails: can the red team get the model to produce content it is supposed to refuse? This includes testing roleplay scenarios, hypothetical framings, multilingual inputs, and encoding tricks that bypass surface-level filters.
Context and System Prompt Leakage
Can the red team get the model to reveal the contents of its system prompt? Can they extract information from earlier conversation turns, from retrieved documents in a RAG pipeline, or from other users' sessions in a multi-tenant application? Context leakage is one of the most common findings in AI security assessments.
Content Policy Violations
Does the model produce content that violates the organisation's content policy? This category depends heavily on the application: for a customer service bot, policy violations might include disparaging competitors, discussing emotionally sensitive topics, or making unauthorised commitments. For a code assistant, they might include generating malware or bypassing security controls.
Agentic and Tool-Use Risks
For AI agents with tool access — web browsing, email sending, file manipulation, API calls — the red team tests whether prompt injection can hijack those tools. Can an attacker embed instructions in a web page that cause an AI assistant to send an email on their behalf? Can a malicious document instruct an AI agent to exfiltrate data from a file system? These are no longer theoretical risks.
RAG Pipeline Manipulation
Retrieval-augmented generation systems are particularly vulnerable. The red team tests whether malicious content injected into the knowledge base — a poisoned document, a crafted web page, a manipulated database record — can influence the model's responses. They also test whether the model will reveal the contents of retrieved documents to unauthorised users.
AI Red Teaming Methodology
Step 1: Define Scope and Goals
Start with a clear scope document. What application is being tested? What does it do? Who are its users? What data does it have access to? What actions can it take? What are the most serious failure modes — the ones that would cause the most harm if realised?
Define success criteria for the red team. Are you trying to demonstrate that high-severity vulnerabilities exist? Estimate the rate of policy violations? Confirm that a specific attack class is mitigated? Clear goals lead to more useful findings.
Step 2: Build or Brief the Red Team
Effective AI red teams combine several types of expertise: security engineering (to understand attack methodologies), domain expertise (to understand what harmful outputs look like in context), and ML knowledge (to understand model behaviour). No single person typically has all three; build a team rather than relying on one person.
Brief the team fully on the target application. The more context they have — the system prompt, the tool schema, the data sources — the more realistic and targeted their probing will be.
Step 3: Run Structured Adversarial Testing
Structure testing around the OWASP LLM Top 10 vulnerability classes. For each class, develop a set of probes — specific prompts or scenarios designed to trigger that class of vulnerability — and run them systematically.
Log every probe and its result. Track which probes succeeded in producing unintended behaviour, at what rate, and under what conditions. This data is essential for prioritising remediation and for regression testing after fixes are applied.
Step 4: Document and Prioritise Findings
Each finding should document: the attack vector (the prompt or sequence of prompts used), the expected behaviour, the actual behaviour, the severity (using a consistent severity framework), and the potential real-world impact.
Prioritise findings by the combination of severity and exploitability. A catastrophic failure that requires 10,000 attempts to trigger may be lower priority than a high-severity failure that succeeds reliably.
Step 5: Remediate and Retest
For each finding, the development team implements a fix — whether that is a system prompt update, an input validation rule, an output filter, or an architectural change — and the red team retests to confirm the fix is effective and has not introduced regressions.
Build a regression test library from confirmed findings. Every vulnerability that is fixed should become an automated test case that runs on every subsequent model update.
Tooling for AI Red Teams
The AI red teaming tooling ecosystem is maturing rapidly. Several categories of tools are now available:
Adversarial prompt libraries — curated collections of known jailbreaks, injection attempts, and adversarial probes. Useful as a starting point, though experienced attackers will have developed probes that do not appear in public libraries.
Automated red teaming frameworks — tools that generate adversarial prompts systematically, often using a separate LLM as the attacker model. Microsoft's PyRIT and Garak are widely used open-source options.
Input screening tools — tools that classify inputs for injection risk before they reach the model. Prompt Shields' Prompt Scorer provides a real-time risk score that can be used both in production defence and in red team testing workflows.
Monitoring platforms — tools that log and analyse model inputs and outputs at scale, enabling red teams to run large-scale probing campaigns and analyse results systematically.
Automated vs Manual Red Teaming
Both have a role. Automated red teaming can cover enormous volumes of prompts — millions of adversarial inputs — and identify systematic weaknesses efficiently. But automated tools are limited to known attack patterns and may miss novel techniques that a creative human attacker would find.
Manual red teaming by skilled humans finds the unexpected failures: the context-specific jailbreak that requires understanding the application's domain, the multi-turn conversation that progressively erodes the model's guardrails, the indirect injection that only works because of a specific interaction between the system prompt and a particular data source.
Best practice combines both: automated scanning to cover the known threat landscape efficiently, followed by manual expert testing to probe for novel and application-specific vulnerabilities.
How Often Should You Red Team?
The answer depends on how frequently your AI system changes and how high-stakes its failures would be. As a minimum:
- Before every production launch of an AI feature
- After every change to the system prompt
- After every change to the model (including fine-tuning or model version updates)
- After every extension of the model's tool access or data access
- Periodically on a schedule (quarterly for most production systems, monthly for high-risk applications)
For teams running continuous delivery of AI features, red teaming should be integrated into the CI/CD pipeline — an automated baseline run on every pull request, supplemented by periodic deep manual assessments.
Conclusion
AI red teaming is no longer optional for organisations deploying large language models in production. The attack techniques are public, the tooling is accessible, and the consequences of undetected vulnerabilities — in regulated industries especially — are significant.
The good news is that a structured red teaming programme does not require a large, specialised team. A combination of automated tooling, a clear methodology, and periodic expert testing can provide meaningful coverage for most AI deployments.
Prompt Shields' Promptly platform helps security teams run structured AI red team exercises with built-in adversarial prompt libraries, scoring, and reporting. If you are starting from scratch, the Prompt Scorer tool gives you an immediate, free way to evaluate the injection risk of any prompt.