The Echo Chamber Multi-Turn LLM Jailbreak
This addresses security vulnerabilities in deployed chatbots to prevent financial and reputational damage, presenting a novel attack method.
The paper tackles the problem of jailbreaking LLMs through multi-turn attacks by introducing Echo Chamber, a gradual escalation method, and demonstrates its effectiveness against multiple state-of-the-art models in evaluations.
The availability of Large Language Models (LLMs) has led to a new generation of powerful chatbots that can be developed at relatively low cost. As companies deploy these tools, security challenges need to be addressed to prevent financial loss and reputational damage. A key security challenge is jailbreaking, the malicious manipulation of prompts and inputs to bypass a chatbot's safety guardrails. Multi-turn attacks are a relatively new form of jailbreaking involving a carefully crafted chain of interactions with a chatbot. We introduce Echo Chamber, a new multi-turn attack using a gradual escalation method. We describe this attack in detail, compare it to other multi-turn attacks, and demonstrate its performance against multiple state-of-the-art models through extensive evaluation.