[Caice-csse] Security of AI: Echo Chamber: A Context-Poisoning Jailbreak That Bypasses LLM Guardrails

[N Narayanan]

Summary
An AI Researcher at Neural Trust has discovered a novel jailbreak technique that defeats the safety mechanisms of today's most advanced Large Language Models (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.
Unlike traditional jailbreaks that rely on adversarial phrasing or character obfuscation, Echo Chamber weaponizes indirect references, semantic steering, and multi-step inference. The result is a subtle yet powerful manipulation of the model's internal state, gradually leading it to produce policy-violating responses.
In controlled evaluations, the Echo Chamber attack achieved a success rate of over 90% on half of the categories across several leading models, including GPT-4.1-nano, GPT-4o-mini, GPT-4o, Gemini-2.0-flash-lite, and Gemini-2.5-flash. For the remaining categories, the success rate remained above 40%, demonstrating the attack's robustness across a wide range of content domains.
https://neuraltrust.ai/blog/echo-chamber-context-poisoning-jailbreak<https://nam11.safelinks.protection.outlook.com/?url=https%3A%2F%2Fneuraltrust.ai%2Fblog%2Fecho-chamber-context-poisoning-jailbreak&data=05%7C02%7Ccaice-csse%40eng.auburn.edu%7C768b4f1c289a4b7f354808ddb3347acc%7Cccb6deedbd294b388979d72780f62d3b%7C0%7C0%7C638863760157059876%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=P6x8GMnMxJ2ekxsSTqKnW31DL6Wg329I6DuM5se0I9s%3D&reserved=0>
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mailman.eng.auburn.edu/pipermail/caice-csse/attachments/20250624/0feb513a/attachment-0001.htm>