Challenges in red teaming AI systems

In this post we detail insights from a sample of red teaming approaches that we’ve used to test our AI systems. Through this practice, we’ve begun to gather empirical data about the appropriate tool to reach for in a given situation, and the associated benefits and challenges with each approach. We hope this post is helpful for other companies trying to red team their AI systems, policymakers curious about how red teaming works in practice, and organizations that want to red team AI technology.

Red teaming is a critical tool for improving the safety and security of AI systems. It involves adversarially testing a technological system to identify potential vulnerabilities. Today, researchers and AI developers employ a wide range of red teaming techniques to test their AI systems, each with its own advantages and disadvantages.

The lack of standardized practices for AI red teaming further complicates the situation. Developers might use different techniques to assess the same type of threat model, and even when they use the same technique, the way they go about red teaming might look quite different in practice. This inconsistency makes it challenging to objectively compare the relative safety of different AI systems.

Since we released our blog post on our approach to red teaming AI systems for national security risks, we’ve continued to build out evaluation techniques to measure “frontier threats” (areas that may pose a consequential risk to national security), as well as the external partnerships that bring deep subject matter expertise to red teaming our systems. Our frontier red teaming work primarily focuses on Chemical, Biological, Radiological, and Nuclear (CBRN), cybersecurity, and autonomous AI risks. We work with experts in these domains to both test our systems and co-design new evaluation methods. Depending on the threat model, external red teamers might work with our standard deployed versions of Claude to investigate risks in “real-world” settings, or they might work with non-commercial versions that use a different set of risk mitigations.

Red teaming is a valuable technique for identifying and mitigating risks in AI systems. The various red teaming methods covered in this post highlight a handful of techniques available for different use cases and threat models. We look forward to collaborating with other actors to iterate on these techniques and work towards common standards for safety testing. By investing in red teaming, we can work towards building AI systems that are safe and beneficial to society. It is one of several tools in a larger effort to ensure AI is developed thoughtfully and with robust safeguards in place.

To support further adoption and standardization of red teaming, we encourage policymakers to consider the following proposals:

To address this, the AI field needs established practices and standards for systematic red teaming. We believe it is important to do this work now so organizations are prepared to manage today’s risks and mitigate future threats when models significantly increase their capabilities. In an effort to contribute to this goal, we share an overview of some of the red teaming methods we have explored, and demonstrate how they can be integrated into an iterative process from qualitative red teaming to the development of automated evaluations. We close with a set of recommended actions policymakers can take to foster a strong AI testing ecosystem.

In the following sections, we will cover each of these red teaming methods, examining the unique advantages and the challenges they present (some of the benefits and challenges we outline may be applicable across red teaming methods).

At a high level, domain-specific expert teaming involves collaborating with subject matter experts to identify and assess potential vulnerabilities or risks in AI systems within their area of expertise. Enlisting experts helps bring a deeper understanding of complex, context-specific issues.