Red Teaming for Generative AI: A Practical Approach to AI Security
Generative AI has provided great benefits to a number of industries, however, as we have discussed in previous commentaries, AI agents come with security risks. Threat actors can potentially induce such agents to reveal sensitive information, generate harmful content, or spread false information. As dangerous as this is, this doesn’t necessarily mean we should abandon AI as a concept. It is possible to probe AI for weaknesses before it’s released to the public, and the GenAI Red Teaming Guide by OWASP lays out the means of doing so.
Red Teaming, or penetration testing, is the process of discovering weaknesses in a piece of software before threat actors can, in order to close them. In the case of Generative AI Red Teaming, the flaws come in several broad categories: protection from adversarial attacks, AI model alignment risks, data exposure risks, interaction risks, and knowledge risks. Testing generative AI models requires a diverse toolset, involving threat modeling, scenario-based testing, and automated tooling, supported by human expertise. Red teams require a multidisciplinary team, robust engagement frameworks, and iterative processes to adapt to evolving threats.
The process used by a red team to test a generative AI model would be very similar to the attacks used by threat actors against the model. The biggest risk is prompt injection: using carefully crafted queries to trick the AI into breaking its own rules, also known as AI jailbreaking. Beyond that, AIs have to be tested for data leakage to make sure they don’t accidentally leak private information, as well as for hallucinations so they don’t make up incorrect information. There’s also bias and toxicity testing, to make sure that training data doesn’t cause an AI to produce unfair or offensive content. These are essential processes in the development of an AI model that should absolutely not be skipped, especially as AI becomes more complex and occupies a greater role in the lives of individuals and organizations. By investing in red teaming, enterprises can ensure trust in their AI systems both internally and externally.
