Echo Chamber Attack: A Novel Jailbreak for LLMs

An AI researcher at Neural Trust has discovered a novel jailbreak technique, dubbed the 'Echo Chamber Attack,' that bypasses the safety mechanisms of leading Large Language Models (LLMs). This method uses context poisoning and multi-turn reasoning to subtly guide models towards generating harmful content without explicitly dangerous prompts. By planting seemingly innocuous prompts that build upon each other across multiple turns, the attack gradually shapes the model's internal state, leading to policy-violating responses. Evaluations showed success rates exceeding 90% on several models, highlighting a critical vulnerability in current LLM safety.