Challenges In Red Teaming Ai Systems

Challenges in Red Teaming AI Systems \ Anthropic Policy Challenges in red teaming AI systems Jun 12, 2024

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. What is red teaming? 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. 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. Red teaming methods this post covers: Domain-specific, expert red teaming Trust & Safety: Policy Vulnerability Testing National security: Frontier threats red teaming Region-specific: Multilingual and multicultural red teaming

Using language models to red team Automated red teaming

Red teaming in new modalities Multimodal red teaming

Open-ended, general red teaming Crowdsourced red teaming for general harms Community-based red teaming for general risks and system limitations

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). Domain-specific, expert teaming 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. Policy Vulnerability Testing for Trust & Safety risks High-risk threats, such as those that pose severe harm to people or negatively impact society, warrant sophisticated red team methods and collaboration with external subject matter experts. Within the Trust & Safety space, we adopt a form of red teaming called “Policy Vulnerability Testing” (PVT). PVT is a form of in-depth, qualitative testing we conduct in collaboration with external subject matter experts on a variety of policy topics covered under our Usage Policy . We work with experts such as Thorn on issues of child safety, Institute for Strategic Dialogue on election integrity , Global Project Against Hate and Extremism on radicalization, among others.

Frontier threats red teaming for national security risks 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.

Multilingual and multicultural red teaming The majority of our red teaming work takes place in English and typically from the perspective of people based in the United States. One method to better understand, and ideally address, this lack of representation is by red teaming in other languages and cultural contexts. Capacity building efforts led by the public sector can encourage local populations to test AI systems for language skills and topics relevant to a specific community. As one example, we were pleased to partner with Singapore’s Infocomm Media Development Authority (IMDA) and AI Verify Foundation on a red teaming project across four languages (English, Tamil, Mandarin, and Malay) and topics relevant to a Singaporean audience and user base. We look forward to IMDA and AI Verify Foundation publishing more on this work and insights from red teaming more broadly.

Using language models to red team Using language models to red team involves leveraging the capabilities of AI systems to automatically generate adversarial examples and test the robustness of other AI models, potentially complementing manual testing efforts and enabling more efficient and comprehensive red teaming. Automated red teaming As models become more capable, we’re interested in ways we might use them to complement manual testing with automated red teaming performed by models themselves. Specifically, we hope to understand how effective red teaming might be for reducing harmful behavior. To do this, we employ a red team / blue team dynamic, where we use a model to generate attacks that are likely to elicit the target behavior (red team) and then fine-tune a model on those red teamed outputs in order to make it more robust to similar types of attack (blue team). We can run this process repeatedly to devise…