Recent advancements in LLM Red-Teaming: Techniques, Defenses, and Ethical Considerations

2024/10/09 by Tarun Raheja, Raheja, Tarun, Nilay Pochhi +2 · 5 citations
Computer Science · Business, Management and Accounting · #Law, AI, and Intellectual Property #Dispute Resolution and Class Actions

paper · pdf · doi:10.48550/arxiv.2410.09097

Abstract

Large Language Models (LLMs) have demonstrated remarkable capabilities in natural language processing tasks, but their vulnerability to jailbreak attacks poses significant security risks. This survey paper presents a comprehensive analysis of recent advancements in attack strategies and defense mechanisms within the field of Large Language Model (LLM) red-teaming. We analyze various attack methods, including gradient-based optimization, reinforcement learning, and prompt engineering approaches. We discuss the implications of these attacks on LLM safety and the need for improved defense mechanisms. This work aims to provide a thorough understanding of the current landscape of red-teaming attacks and defenses on LLMs, enabling the development of more secure and reliable language models.

Cited by

• Replicating TEMPEST at Scale: Multi-Turn Adversarial Attacks Against Trillion-Parameter Frontier Models
• LLM Harms: A Taxonomy and Discussion
• LLM in the Middle: A Systematic Review of Threats and Mitigations to Real-World LLM-based Systems
• Layer-Wise Perturbations via Sparse Autoencoders for Adversarial Text Generation
• SKATE, a Scalable Tournament Eval: Weaker LLMs differentiate between stronger ones using verifiable challenges

Related

• RedTWIZ: Diverse LLM Red Teaming via Adaptive Attack Planning
• Red Teaming the Mind of the Machine: A Systematic Evaluation of Prompt Injection and Jailbreak Vulnerabilities in LLMs
• Operationalizing a Threat Model for Red-Teaming Large Language Models (LLMs)
• Jailbreaking and Mitigation of Vulnerabilities in Large Language Models
• Survey of Vulnerabilities in Large Language Models Revealed by Adversarial Attacks