Red-Teaming for Generative AI: Silver Bullet or Security Theater? (2401.15897v3)

Abstract: In response to rising concerns surrounding the safety, security, and trustworthiness of Generative AI (GenAI) models, practitioners and regulators alike have pointed to AI red-teaming as a key component of their strategies for identifying and mitigating these risks. However, despite AI red-teaming's central role in policy discussions and corporate messaging, significant questions remain about what precisely it means, what role it can play in regulation, and how it relates to conventional red-teaming practices as originally conceived in the field of cybersecurity. In this work, we identify recent cases of red-teaming activities in the AI industry and conduct an extensive survey of relevant research literature to characterize the scope, structure, and criteria for AI red-teaming practices. Our analysis reveals that prior methods and practices of AI red-teaming diverge along several axes, including the purpose of the activity (which is often vague), the artifact under evaluation, the setting in which the activity is conducted (e.g., actors, resources, and methods), and the resulting decisions it informs (e.g., reporting, disclosure, and mitigation). In light of our findings, we argue that while red-teaming may be a valuable big-tent idea for characterizing GenAI harm mitigations, and that industry may effectively apply red-teaming and other strategies behind closed doors to safeguard AI, gestures towards red-teaming (based on public definitions) as a panacea for every possible risk verge on security theater. To move toward a more robust toolbox of evaluations for generative AI, we synthesize our recommendations into a question bank meant to guide and scaffold future AI red-teaming practices.

Introduction

Generative AI (GenAI) technologies, such as LLMs, image and video generation models, and audio generation models, have seen substantial advancements and growing public attention. However, alongside their potential to facilitate remarkable innovations, these technologies also risk inducing a host of societal harms. Incidents of discrimination and the spread of harmful stereotypes generated by AI systems have highlighted the urgent need for robust evaluation and regulation methods.

Red-teaming, a structured approach originally rooted in cybersecurity, has been increasingly referred to as a principal mechanism for assessing and managing the safety, security, and trustworthiness of AI models. It involves a controlled, adversarial process meant to uncover system flaws. However, significant uncertainties persist regarding the definition, effectiveness, and procedures of AI red-teaming, as well as how these practices should be integrated within the broader framework of AI policy and regulation.

AI Red-Teaming Practices

An extensive literature survey reveals a fragmented landscape where AI red-teaming practices lack uniformity in their goals, execution, and impact on model safety measures. Red-teaming activities vary significantly, from exploring specific threats like national security vulnerabilities to broader, more undefined targets such as "harmful" behavior. The composition of red-teaming participants ranges from subject matter experts and crowdsourced contributors to AI models themselves, each bringing a distinct perspective and set of capabilities to the evaluation.

While red-teamers adopt different tactics, spanning brute-force attempts, automated AI-driven tests, algorithmic searches, and targeted attacks, findings show that all these approaches yield incomplete and potentially misleading assessments of the models in question. Moreover, the employed practices do not consistently translate into clear and actionable guidelines for enhancing model safety or informing regulatory decisions.

Outcomes and Guidelines

Despite the aim to identify AI system vulnerabilities, red-teaming exercises exhibit deficiencies in comprehensive risk evaluation, consistent disclosure of findings, and systematic approaches to mitigation. Current practices often miss significant risks due to the breadth of threat models and biases introduced by evaluators. Moreover, there is no standard convention for reporting the results of red-teaming, leaving stakeholders with incomplete information about the nature and extent of identified vulnerabilities and the measures taken to address them.

Given these challenges, researchers advocate for a more structured approach to red-teaming, emphasizing the need for generating a robust set of guiding questions and considerations for future practices. These should ideally encompass the entire lifecycle of the evaluation process - from pre-activity planning through post-activity assessment - and address factors such as team composition, available resources, success criteria, and communication of outcomes to relevant stakeholders.

Conclusion

Red-teaming, in its present state, falls short of providing a silver bullet solution to the complex safety and security challenges faced by contemporary AI systems. Current practices often skirt the verge of security theater, offering more reassurance than tangible safety improvements. To move toward more effective evaluation and regulation of generative AI, the field requires not only a set of pragmatic and structured guidelines but also a collaborative effort among developers, researchers, policymakers, and the public to refine and implement these recommendations. Such efforts should ensure that red-teaming, in conjunction with other evaluative methodologies, can offer a meaningful contribution to building trustworthy and reliable AI technologies.