Hound: Relation-First Knowledge Graphs for Complex-System Reasoning in Security Audits

• URL: http://arxiv.org/abs/2510.09633v1
• Date: Mon, 29 Sep 2025 02:46:02 GMT
• Title: Hound: Relation-First Knowledge Graphs for Complex-System Reasoning in Security Audits
• Authors: Bernhard Mueller,
• Abstract summary: Hound is a relation-first graph engine that improves system-level reasoning across interrelated components in complexs.<n>A second contribution is a persistent belief system: long-lived vulnerability hypotheses whose confidence is updated as evidence accrues.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Hound introduces a relation-first graph engine that improves system-level reasoning across interrelated components in complex codebases. The agent designs flexible, analyst-defined views with compact annotations (e.g., monetary/value flows, authentication/authorization roles, call graphs, protocol invariants) and uses them to anchor exact retrieval: for any question, it loads precisely the code that matters (often across components) so it can zoom out to system structure and zoom in to the decisive lines. A second contribution is a persistent belief system: long-lived vulnerability hypotheses whose confidence is updated as evidence accrues. The agent employs coverage-versus-intuition planning and a QA finalizer to confirm or reject hypotheses. On a five-project subset of ScaBench[1], Hound improves recall and F1 over a baseline LLM analyzer (micro recall 31.2% vs. 8.3%; F1 14.2% vs. 9.8%) with a modest precision trade-off. We attribute these gains to flexible, relation-first graphs that extend model understanding beyond call/dataflow to abstract aspects, plus the hypothesis-centric loop; code and artifacts are released to support reproduction.

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