What is AI Red Teaming?

We've seen stories of shocking discrimination from AI systems, hilarious chatbot-led deals gone wrong and disturbing declarations of "AI love" for a human reporter. If you remember some high-profile and spectacular failures involving AI, you've already been witness to the reasons for one of the most pressing needs in the "Age of Artificial Intelligence": AI red teaming. While traditional red teams simulate cyberattacks to find code-based vulnerabilities in computer systems, AI red teams constantly poke at artificial intelligence systems like Large Language Models, or LLMs, with the goal of uncovering weaknesses before malicious actors can exploit them.

AI red teaming is the systematic practice of employing adversarial testing techniques to identify vulnerabilities, weaknesses, and potential exploits in artificial intelligence systems. Unlike traditional penetration testing that focuses on breaking into systems, AI red teaming focuses on making AI systems behave in unintended, harmful, or unsafe ways through carefully crafted inputs and scenarios.

At its core, AI red teaming answers a fundamental question: What happens when someone inevitably tries to make your AI system do something it shouldn't?

The Evolution from Cybersecurity to AI-focused Red Teaming

Red teaming has deep roots in military strategy, where "red teams" would simulate enemy tactics to test defensive capabilities during war games. The concept steadily gained ground, and as the Internet Age dawned on humanity this practice eventually bled into cybersecurity during the late 1990s and especially after the "Dot-Com Bubble" of the early 2000s. These digital red teams, or penetration testers, began simulating hacker attacks to identify vulnerabilities in digital infrastructure before the actual hostile ones did.

To this day, traditional cybersecurity red teaming focuses on:

• Breaking into systems through technical vulnerabilities
• Escalating privileges and accessing sensitive data
• Testing network security and access controls
• Exploiting configuration errors and software bugs, including

Critically, these pentesters seek out and sound the alarm on "Zero-Day Vulnerabilities", named for the amount of time the software developers have to find the unexpected bug in their code - which is to say, zero days. Great!

Why AI Systems Need Different Approaches

AI systems present fundamentally different challenges that traditional red teaming methods simply can't address:

Dynamic vs. Static Targets: Traditional systems have predictable behavior based on coded logic. But AI systems are probabilistic and adaptive, their responses and actions sometimes being unknown even to their own creators ("The Black Box of AI").

New Attack Vectors: AI systems are severely vulnerable to threats like prompt injection, model poisoning, and adversarial inputs, all of which you will learn through HackAPrompt and engaging with our Discord community and none of which exist in traditional software suites.

Behavioral Focus: While traditional red teams ask "Can I break in?", AI red teams ask "Can I trick this humanlike system into behaving exactly the opposite of how it was intended?"

Low-Probability, High Risk: Traditional exploits either work or they don't. AI attacks might succeed just 10% of the time but still cause irreparable damage.

Key Areas of Focus in AI Red Teaming

Here are just some of the ways AI can be manipulated:

• Direct prompt injection: Malicious instructions embedded in user messages on AI chat platforms, such as https://chatgpt.com or https://gemini.google.com
• Indirect prompt injection: Hidden instructions in external documents or web pages that the AI then accesses in-chat
• Jailbreaking: An umbrella term which defines attempts to bypass AI safety constraints through various means, including roleplaying a villain
• System prompt extraction: Tricks to reveal the core internal instructions that guide the behavior of all Large Language Models

As a result of these methods, several harmful effects can result. Just to name a few:

• Harmful content creation: Generating violence, hate speech, or illegal material
• Misinformation generation: Creating convincing but false information
• Bias amplification: Reinforcing harmful stereotypes or discrimination
• Privacy violations: Revealing sensitive personal information

Where HackAPrompt Comes In

Platforms like HackAPrompt gamify the AI red teaming process, making these critical security skills accessible to anyone who is willing to try their hand at it. Among a wide variety of other benefits from competing, HackAPrompt challenges you to:

• Test real AI systems in controlled, sandboxed environments
• Develop creative approaches to defeating AI safety guardrails
• Learn from and share with a community of security researchers and practitioners

Every HackAPrompt challenge track represents real-world scenarios where AI systems might be exploited, from assistants that provide advice on the construction of bioweapons to agentic AI being corrupted in Slack channels. By participating, you're not just playing a game—you're helping build a safer AI ecosystem using ingenuity you may not have known you possess!

The Danger of Unsecured Artificial Intelligence is Already Here

AI red teaming has already uncovered shocking vulnerabilities across the industry:

Microsoft Bing Chat Incident: A Stanford student successfully extracted Bing Chat's internal system prompt by simply saying "Ignore previous instructions..."

ChatGPT Connector Exploits: Researchers demonstrated "AgentFlayer," a zero-click attack that used hidden prompts in Google Drive documents to extract API keys from ChatGPT users

Enterprise AI Vulnerabilities: Red teams have discovered ways to make customer service chatbots leak customer data, coerce content filters into approving harmful material, and compel decision-making systems into exhibiting blatant racial and gender bias

Getting Started with AI Red Teaming

Whether you're a developer, security professional, or simply curious about jailbreaking AI, learning the ropes of red teaming principles helps you:

• Think adversarially about AI system design and deployment. When everybody focuses on defense, somebody needs to simulate the offense.
• Recognize potential risks in AI applications you encounter. Because there will be many.
• Contribute meaningfully to AI safety discussions and initiatives, which will only grow in urgency.

AI red teaming isn't just about breaking things (though that can be fun!); it's about building better, safer AI systems that we can trust with increasingly important decisions. As AI gets pushed into more and more aspects of our society, from healthcare to finance and everything else, red teamers are already an invaluable asset. Use HackAPrompt to build your skills now, to be an irreplaceable part of the future!