A neuro-symbolic framework reconstructs semantics from opaque binaries via abstract interpretation, reflexive LLM prompting, typed knowledge graphs, and Graphormer reasoning to outperform baselines in vulnerability detection and APT matching for industrial control systems.
IEEE Transactions on Software Engineering , volume=
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 2
citation-polarity summary
years
2026 2roles
background 2polarities
background 2representative citing papers
VulTriage combines control dependency extraction, CWE knowledge retrieval, and semantic summarization to improve LLM accuracy on vulnerability detection, reaching SOTA on PrimeVul and generalizing to Kotlin.
citing papers explorer
-
Securing the Dark Matter: A Semantic-Enhanced Neuro-Symbolic Framework for Supply Chain Analysis of Opaque Industrial Software
A neuro-symbolic framework reconstructs semantics from opaque binaries via abstract interpretation, reflexive LLM prompting, typed knowledge graphs, and Graphormer reasoning to outperform baselines in vulnerability detection and APT matching for industrial control systems.
-
VulTriage: Triple-Path Context Augmentation for LLM-Based Vulnerability Detection
VulTriage combines control dependency extraction, CWE knowledge retrieval, and semantic summarization to improve LLM accuracy on vulnerability detection, reaching SOTA on PrimeVul and generalizing to Kotlin.