从100个生成式AI产品中汲取的教训

Core Insights - AI red teaming has become a critical practice for assessing the security and robustness of generative AI systems, with insights drawn from testing over 100 products [8][14][24] - The report outlines eight key lessons learned from these red team operations, emphasizing the importance of understanding system capabilities and application contexts [8][12][26] Red Team Operations - The operations conducted since 2021 have focused on both model and system categories, with a significant increase in the number of products tested following the rise of generative AI applications [24][25] - The report highlights the shift towards integrating large language models (LLMs) with other software components, which has expanded the attack surface and introduced new security risks [25][81] Key Lessons - Understanding what AI systems can do and their application areas is crucial for identifying potential risks [8][33] - Simpler techniques often prove effective in real-world attacks, as attackers may not rely on complex gradient-based methods [38][39] - Automation tools like PyRIT have been developed to enhance the efficiency of red team operations, allowing for broader risk coverage [57][60] Case Studies - Case studies illustrate various vulnerabilities, such as the ability of visual language models to generate harmful content when manipulated through image inputs [41][42] - Another case study demonstrates how LLMs can be exploited to automate scams, highlighting the risks associated with insufficient security training [51][53] - The report also examines gender bias in text-to-image generators, showcasing the potential for AI to perpetuate stereotypes [72][73] Responsible AI Challenges - The report discusses the pervasive yet difficult-to-measure harms associated with responsible AI, emphasizing the need for ongoing evaluation and adaptation of security measures [4][76][80] - It identifies the dual nature of actors in responsible AI violations, including both malicious users and those who inadvertently trigger harmful outputs [74][75] Future Directions - The report calls for further exploration of new harm categories and the development of tools to measure them, as the landscape of AI risks continues to evolve [45][80] - It emphasizes the importance of human judgment in red team operations, particularly in specialized fields where domain expertise is essential [61][62]