What is AI Red Teaming?

Artificial Intelligence (AI) has become an indispensable part of daily life. Previously operating behind the scenes, AI now takes center stage with the rise of generative models and user-friendly chat interfaces, allowing people to "talk" directly with AI. Major companies have embraced AI as a core element of their strategies, integrating tools like Apple Intelligence, Google’s AI-powered search, and GitHub Copilot.

As AI systems grow more capable, the risks of misuse, corruption, and accidental failure increase. AI red teaming is a proactive practice that addresses exactly this point: it is a way to test AI systems against adversarial threats, hidden flaws, and biases.

In this post, we’ll explore the history and core concepts of AI red teaming, how it compares to traditional security testing, the core components of an AI red team exercise, some vulnerabilities, and what challenges AI red teaming practitioners face.

The Roots of Traditional Red Teaming

Traditional red teaming originally developed in the military sphere, with the U.S. military formally adopting the term in the 1960s to model Soviet behaviors versus American defenses. In a typical scenario, a “blue team” represents the defending side while the “red team” acts as a simulated adversary.

These exercises soon expanded to strategic planning, and eventually to cybersecurity, where red teams learned to infiltrate networks, trick staff into revealing credentials, and stress-test digital and physical defenses. Despite evolving methods, the goal remained constant: expose and mitigate vulnerabilities by thinking like an adversary.

The Rise of AI Red Teaming

As AI became more pervasive, classic red teaming techniques had to be adapted for machine learning. Attacks such as data poisoning, model extraction, and manipulative prompt engineering (prompt hacking) do not appear in typical penetration tests. With the emergence of Generative AI, researchers saw that even small manipulations to an AI’s inputs, often called adversarial prompts, could produce drastically incorrect or harmful outputs, making it clear that generative AI models demand specialized scrutiny.

Here's a simple example of jailbreaking, the process of getting a generative AI model to perform or produce unintended outputs through specific prompts. In this example, the prompt exploits roleplay manipulation. Even if the model is designed to block such information, a carefully worded prompt can sometimes bypass safety mechanisms.

Although no single event defined the creation of AI red teaming, the practice crystallized once academics and practitioners alike recognized these vulnerabilities. Over time, organizations developed guidelines and metrics to measure AI red teaming’s effectiveness, but a universal standard still does not exist.

Defining AI Red Teaming

AI red teaming tests the reliability, fairness, and robustness of AI systems in ways that differ from traditional cybersecurity. Conventional red teams usually focus on network breaches or unpatched software, whereas AI red teams concentrate on how a model might be manipulated or misled.

Because AI systems typically consist of complex pipelines, red teaming targets every step of the entire model pipeline, from data collection and curation to the final outputs:

• Adversarial input testing: Subtle changes to text or images that can cause wildly incorrect outputs.
• Data poisoning: Inserting malicious examples into the training set to alter a model’s behavior.
• Fairness audits: Checking whether the model behaves differently toward certain demographics.
• Model security checks: Identifying whether a model can accidentally leak proprietary or personal information.

Traditional vs. AI Red Teaming

Aspect	Traditional Red Teaming	AI Red Teaming
Focus	Physical, cyber, and procedural defenses (networks, endpoints, etc.)	AI-specific flaws: adversarial inputs, data poisoning, model extraction
Techniques	Penetration testing, phishing, social engineering, on-site intrusion	Prompt engineering, adversarial example crafting, bias audits, model theft
Primary Concern	System infiltration, data exfiltration, operational resilience	Model manipulation, unfair outcomes, data leaks, generative misuse

Core Components of an AI Red Team Exercise

Although there are many ways to organize an AI red team engagement, the work typically includes these five phases:

1. Threat modeling: The team identifies likely adversaries (from curious hobbyists to sophisticated criminals) and maps system data flows for points of weakness.

2. Vulnerability analysis: Scrutinizing model architecture, data security, and pipeline configurations to find any weaknesses.

3. Adversarial testing: Red teamers generate carefully crafted prompts and other adversarial samples (text, images, or code) and check if these inputs can disrupt the model’s performance or cause it to produce harmful content.

4. Bias and fairness testing: Undertake bias and fairness audits, simulating various demographic scenarios (racial, gender, cultural, etc.) to see if the AI is disproportionately negative or erroneous toward certain groups.

5. Incident response simulations: Test how quickly the defending “blue team” can identify and mitigate any detected attacks.

Once complete, all findings are documented and fed back to the developers so that the model architecture, training data, or oversight processes can be improved.

Vulnerabilities Discovered Through AI Red Teaming

Our experience at HackAPrompt 1.0, a global prompt hacking competition where we analyzed more than 600,000 adversarial prompts, highlighted the fragility of AI systems under minimal pressure.

For example, tricking the model into producing the required output, in our case, it's "I have been PWNED," by masking it under "special intruction":

This example showed how a model might ignore higher-level instructions if cleverly engineered prompts override them. These findings often catch model developers by surprise, underscoring that sophisticated solutions can still be undone by deceptively simple tricks.

Challenges and Limitations

AI red teaming faces multiple challenges:

• Unique Architectures: Each AI system is built using different algorithms, models, and frameworks, meaning that a vulnerability in one AI system might not exist in another. For example, a vulnerability in a neural network may not exist in a rule-based AI system.

• Lack of Common Understanding: There isn't a universally accepted definition of red teaming in the context of AI. Businesses, governmental agencies, the Department of Defense, and each of the services have their own definition of red teaming and views on how to apply it. This can lead to inconsistencies in how it is applied.

• Complexity of AI Systems: AI systems, particularly those based on machine learning, can exhibit unpredictable behaviors due to the complex interactions between the model, input data, and the environment. This makes it difficult to predict the exact behavior of a model and the vulnerabilities that might result.

In summary, the lack of uniformity in AI systems means that red teaming cannot be a standardized process. Instead, it demands a deep understanding of each specific AI system's unique characteristics and requires the development of customized testing strategies. This makes AI red teaming more complex and resource-intensive than traditional red teaming.

Conclusion

AI red teaming stands at the intersection of security, adversarial thinking, and deep domain expertise in machine learning. The field continues to evolve rapidly, adding new dimensions like generative AI safety and advanced adversarial attacks, but it keeps the same fundamental premise: to test relentlessly, learn from failures, and continuously refine AI defenses.

Additional Resources

• Prompt Hacking Guides
• Prompt Injection vs. Jailbreaking: What’s the Difference?
• What to Expect at Your First AI Red-Teaming Event?
• Key Insights from HackAPrompt 1.0
• What is HackAPrompt 2.0?

Courses

• General AI Safety
• Introduction to Prompt Hacking
• Advanced Prompt Hacking
• AI Red-Teaming and AI Safety: Masterclass

Books and Articles

• Computational Red Teaming by H. A. Abbass – Explores computational red teaming in depth.
• Red Team: How to Succeed by Thinking Like the Enemy by M. Zenko – Comprehensive overview of the red teaming mindset.
• Red Teaming Language Models to Reduce Harms by Ganguli et al. – Landmark paper on red teaming methods specific to LLMs.
• Frontier Model Forum – Ongoing publications and guidelines for ethical, secure AI development.

Other Resources

• MITRE ATLAS (Adversarial Threat Landscape for AI Systems): Framework for understanding common AI vulnerabilities.
• OWASP AI Security Project: Initial efforts to create guidelines and checklists for AI security testing.
• Adversarial Robustness Toolbox (ART): Open-source libraries for creating and testing adversarial examples.
• AI Village at DEFCON: Community dedicated to AI security research and hands-on workshops. Read about our experience participating at DEFCON AI Village.

Valeriia Kuka

Valeriia Kuka, Head of Content at Learn Prompting, is passionate about making AI and ML accessible. Valeriia previously grew a 60K+ follower AI-focused social media account, earning reposts from Stanford NLP, Amazon Research, Hugging Face, and AI researchers. She has also worked with AI/ML newsletters and global communities with 100K+ members and authored clear and concise explainers and historical articles.