What is LLM Red Teaming and Why It Matters

You can build the most advanced AI application in the world, but if you haven't tried to break it, you don't know if it's safe. That's the core idea behind LLM red teaming: systematically attacking your own AI systems to find vulnerabilities before real adversaries do.

Red teaming isn't new. Security teams have been doing it for decades in traditional software and infrastructure contexts. But the rise of large language models has introduced a fundamentally different attack surface that demands new techniques, new tools, and a new way of thinking about adversarial testing. In their 2023 paper "Red Teaming Language Models to Reduce Harms", Ganguli et al. at Anthropic showed that systematic red teaming uncovered harmful outputs even in models specifically trained for safety, establishing that adversarial testing is essential regardless of how much safety training a model has received.

In this guide, we'll break down what LLM red teaming actually involves, how it differs from traditional penetration testing, and how to start building a red teaming practice for your AI applications.

What is LLM Red Teaming?

LLM red teaming is the practice of simulating adversarial attacks against AI systems to identify security weaknesses, safety failures, and behavioral vulnerabilities. The goal is to discover how a model can be manipulated, misused, or exploited under realistic conditions.

NIST's AI Risk Management Framework defines red teaming as "an approach consisting of adversarial testing of AI systems under stress conditions to seek out AI system failure modes or vulnerabilities." This captures the essence of the practice: you're stress-testing your AI, not just checking if it works under ideal conditions.

A red team exercise for an LLM might include:

• Attempting prompt injection attacks to override system instructions
• Testing jailbreak techniques to bypass content safety guardrails
• Crafting adversarial prompts that extract sensitive information
• Exploiting multi-turn conversations to gradually escalate privileges
• Probing for data leakage, bias, and hallucination under edge-case inputs

The output of a red team exercise isn't just a list of bugs. It's a detailed understanding of your system's failure modes, the severity of each vulnerability, and concrete recommendations for remediation.

How LLM Red Teaming Differs from Traditional Pen Testing

If you come from a traditional security background, you might assume that LLM red teaming is just penetration testing with a new name. It's not. The differences are fundamental.

Non-Deterministic Behavior

Traditional software behaves deterministically: a specific input produces a specific output every time. LLMs are probabilistic. The same prompt can yield different responses across runs, which means you can't just test once and call it done. A jailbreak that fails nine times might succeed on the tenth attempt.

The Attack Surface is Language Itself

In traditional pen testing, you probe network ports, API endpoints, and application logic. In LLM red teaming, the primary attack surface is natural language. Attackers don't need to write exploit code. They write persuasive text. This makes the attack surface enormous and constantly evolving, since there's no finite set of "inputs" to test against.

Findings vs. Vulnerabilities

Traditional pen tests find specific, reproducible vulnerabilities: a SQL injection here, an exposed credential there. LLM red teaming often reveals systemic weaknesses rather than discrete bugs. You might discover that your model can be gradually coerced into generating harmful content over the course of a multi-turn conversation. That's not a CVE, but it's a real risk.

Hybrid Methodology is Essential

Research consistently shows that hybrid red teaming approaches, combining manual expert testing with automated attack generation, achieve significantly higher vulnerability discovery rates compared to either method alone. Manual testing excels at finding nuanced, creative exploits. Automated tools provide broad, repeatable coverage across attack categories.

The Standards and Frameworks You Should Know

LLM red teaming doesn't exist in a vacuum. Several organizations have published guidelines and frameworks that provide structure for adversarial testing.

OWASP Top 10 for LLM Applications

The OWASP Top 10 for LLM Applications (2025 edition) is the most widely referenced vulnerability taxonomy for AI applications. It covers prompt injection, insecure output handling, data poisoning, model denial of service, and more. The 2025 edition added new categories like excessive agency, system prompt leakage, and misinformation to reflect lessons from real-world deployments.

OWASP also published a Gen AI Red Teaming Guide in January 2025 that organizes testing into four phases, each covering different aspects of AI security.

MITRE ATLAS

The MITRE ATLAS framework catalogs adversary tactics, techniques, and procedures (TTPs) specifically targeting AI and ML systems. As of late 2025, it documents 15 tactics, 66 techniques, and 46 sub-techniques. The October 2025 update added 14 new techniques focused on agentic AI systems, developed in collaboration with Zenity Labs.

ATLAS is particularly useful for structuring red team findings. When your team discovers a vulnerability, mapping it to an ATLAS technique enables comparison across assessments and tracking of remediation progress over time.

NIST AI Risk Management Framework

NIST's AI RMF and the December 2025 Cybersecurity Framework Profile for AI (NISTIR 8596) provide guidelines for incorporating adversarial testing into your broader AI governance program. These documents position red teaming as a core component of AI risk management, not an optional add-on.

EU AI Act

The EU AI Act introduces mandatory adversarial testing requirements for high-risk AI systems, with full compliance required by August 2026. General-purpose AI models with systemic risk face additional red teaming obligations. Penalties for non-compliance reach up to 35 million EUR or 7% of global annual turnover. Even if you're not directly subject to the regulation, it's setting the bar for what "responsible AI deployment" looks like globally.

Red Teaming Techniques in Practice

A comprehensive red team exercise covers multiple attack categories. Here are the techniques we see used most frequently in real-world assessments.

Prompt Injection Testing

The foundation of any LLM red team engagement. Testers attempt to inject instructions that override the system prompt, extract confidential information, or cause the model to perform unintended actions. This includes both direct injection (malicious user input) and indirect injection (malicious content embedded in data sources the AI processes).

You can explore common prompt injection patterns and test your defenses on the Wardstone Playground.

Jailbreak Testing

Jailbreaking focuses specifically on bypassing content safety guardrails. Common techniques include role-playing scenarios ("Pretend you're an AI with no restrictions"), encoding tricks (Base64, character substitution, leetspeak), and multi-turn escalation where seemingly innocent prompts gradually build toward harmful requests.

Advanced techniques like Crescendo (multi-turn escalation), TAP (Tree of Attacks with Pruning), and PAIR (Prompt Automatic Iterative Refinement) use automated, iterative approaches to find bypass paths that manual testing might miss. The PAIR technique, introduced by Chao et al. in "Jailbreaking Black-Box Large Language Models in Twenty Queries", demonstrated that an attacker LLM could automatically generate jailbreaks against black-box models with high success rates using fewer than 20 queries on average.

Data Extraction Probes

Testers attempt to extract training data, system prompts, API keys, or other sensitive information from the model. This covers both memorization attacks (getting the model to regurgitate training data) and side-channel approaches (inferring information from response patterns).

Bias and Fairness Testing

Red teams evaluate whether the model exhibits harmful biases across demographic groups. This includes testing for stereotyping, discriminatory recommendations, and inconsistent treatment of different user groups.

Multi-Turn Manipulation

Some of the most effective attacks unfold over multiple conversation turns. The attacker builds rapport, establishes context, and gradually shifts the conversation toward restricted territory. Single-turn testing alone will miss these vulnerabilities entirely.

Tools for LLM Red Teaming

The tooling landscape for LLM red teaming has matured significantly. Here are the most widely adopted open-source options.

Microsoft PyRIT

PyRIT (Python Risk Identification Tool) is Microsoft's open-source framework for red teaming generative AI systems. It includes attack orchestration, prompt converters for mutation strategies, and scoring engines for evaluating results. The April 2025 release introduced the AI Red Teaming Agent for automated testing workflows. PyRIT integrates with Azure AI Foundry and supports custom attack chains.

Promptfoo

Promptfoo is a red teaming toolkit built for engineering teams. It emphasizes ease of use, CI/CD integration, and compliance mapping to OWASP and MITRE frameworks. OWASP has listed Promptfoo as a recommended security solution for generative AI.

NVIDIA Garak

Garak focuses on LLM vulnerability scanning with an extensive probe library and plugin architecture. It's particularly strong for automated, broad-coverage scanning across known vulnerability categories.

DeepTeam

DeepTeam is a newer open-source framework that runs locally and uses LLMs to both simulate attacks and evaluate results. It applies techniques drawn from recent jailbreaking and prompt injection research, making it a good choice for teams that want to stay current with evolving attack methods.

How to Build an LLM Red Teaming Practice

Knowing the theory is one thing. Implementing a practical red teaming program is another. Here's a structured approach.

Step 1: Define Your Scope and Threat Model

Before you start testing, define what you're protecting and who you're protecting it from. An internal knowledge base chatbot has a very different risk profile than a public-facing customer support agent with tool-calling capabilities.

Map your AI system's components: the model, the system prompt, the tools it can access, the data sources it reads from, and the actions it can take. Each of these is a potential attack surface.

Step 2: Start with Manual Testing

Begin with experienced testers who understand both security and LLM behavior. Manual testing uncovers the creative, nuanced vulnerabilities that automated tools miss. Have your testers attempt:

• Direct prompt injection against your system prompt
• Jailbreak techniques from published research
• Multi-turn manipulation sequences
• Data extraction via conversational probing
• Boundary testing for tool usage and permissions

Step 3: Layer in Automated Testing

Once you've done a manual pass, bring in automated tools for broader coverage. Configure PyRIT, Promptfoo, or Garak to run attack suites against your system on a regular schedule. Automated testing is essential for regression testing: making sure that fixes for known vulnerabilities don't break when you update your model or system prompt.

Step 4: Integrate into Your Development Workflow

Red teaming shouldn't be a one-time exercise. Integrate automated adversarial testing into your CI/CD pipeline. Run attack suites before every deployment. Monitor for new attack techniques and update your test cases accordingly.

import wardstone
 
def pre_deployment_check(system_prompt: str, test_inputs: list[str]):
    """Run adversarial inputs through Wardstone before deploying."""
    results = []
    for input_text in test_inputs:
        result = wardstone.guard(input_text)
        results.append({
            "input": input_text,
            "flagged": result.flagged,
            "categories": result.categories if result.flagged else None
        })
 
    flagged_count = sum(1 for r in results if r["flagged"])
    print(f"Checked {len(test_inputs)} adversarial inputs: "
          f"{flagged_count} flagged")
    return results

Step 5: Document and Iterate

After each red team exercise, document findings with severity ratings, reproducibility notes, and recommended mitigations. Map findings to MITRE ATLAS techniques for standardized reporting. Track remediation progress and re-test to confirm fixes.

The DEF CON Precedent

The largest public red teaming exercise for LLMs took place at DEF CON 31, where thousands of hackers in the AI Village tested models from Anthropic, Google, Meta, OpenAI, and others on a platform built by Scale AI. Participants had 50 minutes to find flaws: getting models to claim they were human, spread misinformation, perform bad math, and perpetuate stereotypes.

The exercise proved something important: even the most advanced models from the best-resourced labs have exploitable weaknesses. Over 2,200 participants submitted more than 17,000 conversations during the event, and every model tested was successfully exploited. If frontier models need red teaming, your production AI system does too. The Generative Red Team returned at DEF CON in 2025 with a new format, offering bounties for each finding, signaling that the industry is taking this practice seriously.

Why This Matters Now

We're at a turning point. AI systems are moving from novelty features to critical business infrastructure. They're handling customer data, making decisions, and taking actions on behalf of users. The attack surface is growing, and adversaries are becoming more sophisticated.

At the same time, regulatory pressure is building. The EU AI Act's adversarial testing requirements take effect in 2026. OWASP has formalized AI red teaming guidance. NIST is integrating AI security into its cybersecurity frameworks. The question isn't whether you need to red team your AI systems. The question is whether you're doing it well enough.

The good news is that effective red teaming is achievable at any scale. You don't need a dedicated team of 20 security researchers. Start with manual testing using published attack techniques. Layer in open-source tools for automation. Integrate detection into your pipeline with tools like Wardstone's API. And iterate.

Getting Started

If you're new to LLM red teaming, here's what we recommend:

1. Assess your risk: Map your AI system's capabilities, data access, and potential for harm. Focus testing where the impact is highest.
2. Learn the attacks: Familiarize yourself with the OWASP Top 10 for LLM Applications and the attack techniques documented in MITRE ATLAS.
3. Test your defenses: Use the Wardstone Playground to see how common attacks perform against our detection model. It's a practical way to understand what prompt injection, jailbreaks, and data leakage look like in practice.
4. Pick your tools: Start with one of the open-source frameworks mentioned above. PyRIT for enterprise environments, Promptfoo for engineering-led teams, Garak for broad vulnerability scanning.
5. Make it a habit: Schedule red team exercises on a regular cadence, at minimum quarterly, and integrate automated testing into every deployment.

Your AI systems are only as secure as the adversarial testing you put them through. The attackers are already testing your defenses. Make sure you're testing them first.