The paper "Against The Achilles’ Heel: A Survey on Red Teaming for Generative Models" provides a comprehensive survey of the field of red teaming, focusing on the vulnerabilities and safety concerns of generative models. The authors, from various institutions, have reviewed over 120 papers to address the gaps in existing literature and propose a structured approach to understanding and mitigating these risks. Key contributions of the paper include: 1. **Comprehensive Taxonomy**: A detailed taxonomy of attack strategies grounded in the inherent capabilities of language models, such as completion compliance, instruction indirection, generalization glide, and model manipulation. 2. **Searcher Framework**: Development of a unified framework for automated red teaming, breaking down the process into state space, search goal, and search operation. 3. **Emerging Areas**: Special attention to emerging topics like multimodal attacks, overkill of harmless queries, and the safety of downstream applications powered by LLMs. 4. **Future Directions**: Identification of key areas for future research, including cybersecurity, persuasive capabilities, privacy, and domain-specific applications. The paper is structured into several sections, covering background, risk taxonomy, attack strategies, evaluation methods, defensive approaches, and future directions. It highlights the importance of a cohesive narrative on the safety landscape of LLMs and provides a detailed analysis of various attack techniques and their effectiveness. The authors also discuss the challenges and limitations of current methods, emphasizing the need for adaptive evaluation frameworks and advanced defenses to address evolving risks.The paper "Against The Achilles’ Heel: A Survey on Red Teaming for Generative Models" provides a comprehensive survey of the field of red teaming, focusing on the vulnerabilities and safety concerns of generative models. The authors, from various institutions, have reviewed over 120 papers to address the gaps in existing literature and propose a structured approach to understanding and mitigating these risks. Key contributions of the paper include: 1. **Comprehensive Taxonomy**: A detailed taxonomy of attack strategies grounded in the inherent capabilities of language models, such as completion compliance, instruction indirection, generalization glide, and model manipulation. 2. **Searcher Framework**: Development of a unified framework for automated red teaming, breaking down the process into state space, search goal, and search operation. 3. **Emerging Areas**: Special attention to emerging topics like multimodal attacks, overkill of harmless queries, and the safety of downstream applications powered by LLMs. 4. **Future Directions**: Identification of key areas for future research, including cybersecurity, persuasive capabilities, privacy, and domain-specific applications. The paper is structured into several sections, covering background, risk taxonomy, attack strategies, evaluation methods, defensive approaches, and future directions. It highlights the importance of a cohesive narrative on the safety landscape of LLMs and provides a detailed analysis of various attack techniques and their effectiveness. The authors also discuss the challenges and limitations of current methods, emphasizing the need for adaptive evaluation frameworks and advanced defenses to address evolving risks.