Paper
LLMs & Texto
Safeguards Based on Copyable Context Cannot Provide Reliable Safety for LLMs
Large language model safeguards decide whether to answer before seeing how an answer will be used.
Hugging Face · Daily Papers
·Pingyu Wu, Lingyao Zhu
·
·▲ 1 upvotes
Este artigo está em destaque na seleção diária de papers do Hugging Face, curada pela comunidade de pesquisa em IA.
Autores: Pingyu Wu, Lingyao Zhu, Weiming Zhang, Nenghai Yu
- 1 upvotes da comunidade
Resumo
Resumo original (em inglês), extraído do paper:
Large language model safeguards decide whether to answer before seeing how an answer will be used. This creates a basic problem for dual-use tasks: the same answer can help an authorized professional or an attacker, while an attacker can imitate a benign request and interaction history. We separate the capability released by the model from the evidence available about downstream use. When that evidence is copyable, we derive the exact worst-case floor on attacker assistance while preserving useful answers. The result yields a safety trilemma: Useful Capability, Reliable Safety, and Open Access cannot coexist. We then show how a trusted credential can complement existing safeguards by adding hard-to-copy information that predicts actual downstream use, and identify the stronger condition needed to eliminate the floor. Evidence from dual-use evaluations, adaptive attacks, and deployed trusted-access programs supports the practical relevance of these conditions.
