LLMs fail to refuse inferred harmful objectives in long contexts

A new research paper explores a critical safety gap in long-context Large Language Models (LLMs). The study, titled "Do Reasoning LLMs Refuse What They Infer in Long Contexts?", introduces "compositional reasoning attacks." These attacks embed fragments of harmful requests within long, otherwise neutral contexts, forcing the LLM to infer the malicious objective. The research found that while LLMs generally refuse explicitly stated harmful requests, their refusal rates drop significantly when the harmful intent must be reconstructed from inferred fragments, especially in longer contexts. This suggests a vulnerability where models can understand harmful objectives but fail to refuse them when they are not directly stated. AI

IMPACT Highlights a critical safety vulnerability in long-context LLMs, potentially requiring new evaluation methods and mitigation strategies.

RANK_REASON Research paper detailing a new attack vector and safety concern for LLMs. [lever_c_demoted from research: ic=1 ai=1.0]

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