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One Turn Too Late: Learning When to Intervene Against Multi-Turn Malicious Intent

Shen, Xinjie et al.
arXiv (All) · Papers
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computation-and-languagecryptography-and-security
Hidden malicious intent in multi-turn dialogue poses a growing threat to deployed large language models (LLMs). Rather than exposing a harmful objective in a single prompt, attackers can distribute their intent across multiple benign-looking turns, making defense a problem not only of whether a dial
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