TombRaider: Entering the Vault of History to Jailbreak Large Language Models
This work addresses vulnerabilities in LLM safety for AI red teaming, though it is incremental as it builds on existing jailbreak techniques.
The paper tackles the problem of jailbreak attacks on large language models by introducing TombRaider, a technique that uses historical knowledge to bypass safety filters, achieving nearly 100% attack success rates on bare models and over 55.4% against defenses.
Warning: This paper contains content that may involve potentially harmful behaviours, discussed strictly for research purposes. Jailbreak attacks can hinder the safety of Large Language Model (LLM) applications, especially chatbots. Studying jailbreak techniques is an important AI red teaming task for improving the safety of these applications. In this paper, we introduce TombRaider, a novel jailbreak technique that exploits the ability to store, retrieve, and use historical knowledge of LLMs. TombRaider employs two agents, the inspector agent to extract relevant historical information and the attacker agent to generate adversarial prompts, enabling effective bypassing of safety filters. We intensively evaluated TombRaider on six popular models. Experimental results showed that TombRaider could outperform state-of-the-art jailbreak techniques, achieving nearly 100% attack success rates (ASRs) on bare models and maintaining over 55.4% ASR against defence mechanisms. Our findings highlight critical vulnerabilities in existing LLM safeguards, underscoring the need for more robust safety defences.