LATIDIA · Ciberseguridad

¿Los LLM de razonamiento rechazan lo que infieren en contextos largos?

arXiv:2602.08874v3 Announce Type: replace-cross Abstract: Long-context Los LLM pueden inferir objetivos que no están explícitamente establecidos. Esta capacidad es útil para razonar sobre documentos, códigos y rastros de herramientas, pero también

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arXiv:2602.08874v3 Announce Type: replace-cross Abstract: Long-context LLMs can infer objectives that are not explicitly stated. This capability is useful for reasoning over documents, code, and tool traces, but it also introduces a safety risk. Harmful intent can be distributed across a context and become apparent only after the model combines the relevant information. Existing safety evaluations largely focus on explicitly stated harmful requests and therefore do not fully capture this failure mode. We introduce compositional reasoning attacks, a long-context attack setting in which harmful requests are decomposed into semantically incomplete fragments and embedded in long contexts. The final query is neutral, and the model must retrieve and combine the fragments