Adversary Brains: Hypothesis-Based Logical Signatures for Threat Actor Detection with LLMs

The paper introduces the concept of an "adversary brain," an LLM-based model that represents a threat actor’s behavior as an evolving logical signature. These signatures function as continuous detection mechanisms, matching activity patterns of campaigns rather than single indicators, and are built using the Analysis of Competing Hypotheses framework extended to thousands of hypotheses. Language models enable the creation, evaluation, and updating of these brains, allowing them to identify new indicators such as fresh command‑and‑control domains and transform unknown threats into known ones.

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