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Echo Chamber: A Context-Poisoning Jailbreak That Bypasses LLM Guardrails


An AI Researcher at Neural Trust has discovered a novel jailbreak technique that defeats the safety mechanisms of today’s most advanced LLMs

Dubbed the Echo Chamber Attack, this method leverages context poisoning and multi-turn reasoning to guide models into generating harmful content, without ever issuing an explicitly dangerous prompt. This iterative process continues over multiple turns, gradually escalating in specificity and risk—until the model either reaches its safety threshold, hits a system-imposed limit, or the attacker achieves their objective. Each attempt used one of two distinct steering seeds across eight sensitive content categories, adapted from the Microsoft Crescendo benchmark: Profanity, Sexism, Violence, Hate Speech, Misinformation, Illegal Activities, Self-Harm, and Pornography.

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