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NeuroLog: Reasoning You Can Audit -- Neuro-Symbolic Vulnerability Discovery via LLM Facts, Datalog, and SMT

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arXiv:2606. 00669v1 Announce Type: new Abstract: Vulnerability discovery on C/C++ source asks the analyst to choose between heavyweight static analysers, which need a working build before a single query runs, and free-form LLMs, which read source readily but invent details and lose track of cross-function dataflow on real codebases.

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Authors: Sanjay Rawat
Released: 2026-06-02 (1mo ago)
Topic: Related to current project
Relation to my work: Read next because NeuroLog: Reasoning You Can Audit -- Neuro-Symbolic Vulnerability Discovery via LLM Facts, Datalog, and SMT overlaps with clean result "LoRA persona trained on <A> alone emits <B> at 23.5% when a co-trained partner learns <A>...<B>, vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)", clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: code, class, good, source, line, extraction, emit, candidate. Source: arxiv cs.CR (Cryptography and Security).
arXiv: 2606.00669

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