GenAI Red Teaming

Security Assessment for Your AI Systems

AI systems are increasingly used to improve efficiency, reduce costs, develop new products and services, and strengthen market positions. However, their deployment comes with risks: poorly secured or inadequately trained systems can unintentionally leak confidential information, trigger unexpected actions, or produce harmful or inappropriate responses that damage your assets or reputation — and may even have legal consequences.

What is Red Teaming for GenAI?

Imagine a malicious actor trying to manipulate the large language model (LLM) you use for your chatbot into revealing sensitive information or generating statements that could harm your company. That’s where red teaming comes in. In the context of GenAI, red teaming involves applying targeted attacks to assess how vulnerable models are to different threats, such as:

• Jailbreak attempts: trying to bypass safety mechanisms in the AI to get it to act in unintended ways.
• Data extraction: manipulating the model to expose sensitive training data.
• Misinformation, hallucinations, and bias: identifying errors, misleading outputs, or biased content in model responses.
• Prompt injection attacks: subtly manipulating user input to confuse or mislead the model into undesirable behavior.

Despite the differences, the core of red teaming remains the same: by exposing and combining vulnerabilities, relevant attack paths are identified that help improve a system’s security and robustness.

Why Should You Test Your AI Systems?

Even if your AI systems only use public data and have no internal access, they are still built on highly complex models — which often know and do more than expected. Reasons include:

1. Training on massive, external datasets: Generative AI models are trained on vast and diverse sources, including websites, books, articles, and social media. Their knowledge often extends far beyond your organization’s intended input.
2. Generative nature of models: These models create new content rather than retrieve predefined answers, which can result in misleading, biased, or even harmful conclusions.
3. Implicit knowledge and associations: During training, models learn patterns, associations, and opinions that can lead to unintended, unrepresentative, or even offensive outputs.
4. Prompt injection and jailbreaking: Malicious actors may craft prompts to override instructions and cause harmful or unwanted behaviors — like revealing sensitive info or giving illegal advice.

How Do We Test Your Systems?

To efficiently test generative AI systems, we follow the OWASP GenAI Red Teaming Guide using the following approach:

Our process consists of these key steps:

1. Threat modeling and risk analysis: Systematically identifying potential threats and vulnerabilities with your domain experts.
2. Execution of targeted attack scenarios: Simulating the defined attacks to assess your AI systems’ defenses and reactions.
3. Implementation and integration assessment: Evaluating existing security controls and suggesting improvements based on test results.

Our security experts combine years of experience in red teaming with the benefits of using generative AI to enhance and accelerate offensive testing. Learn more in our article: “Efficient LLM Red Teaming with Offensive LLM and PyRIT”.

Contact Us

Your GenAI systems are likely more vulnerable than you think! Let us help you identify potential risks and improve their security — together. Contact us!