Black Hat · August 27, 2026

Black Hat Asia 2026 | Graph-Aware LLM for Windows Logon with a Closed-Loop Guarded Detection Agent

video AI Engineering Model Evaluation Agent Security

Black Hat Asia 2026 | Graph-Aware LLM for Windows Logon with a Closed-Loop Guarded Detection Agent video thumbnail

Why it matters

JPCERT/CC's framework compresses millions of Windows authentication events into a user-host graph, then lets a guarded agent iteratively generate database queries, evaluate results, and explore suspicious paths. It reduces the corpus to a small set of logons and returns an evidence timeline, severity, and attack narrative intended to remain auditable.

My takeaway: Use graph reduction and typed queries to keep the evidence outside the model and make each conclusion replayable. Bound query cost and iteration count, require every narrative claim to cite events, measure false positives on representative environments, and keep analysts responsible for containment or account actions.

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