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LLM vs. SAST: A Technical Analysis on Detecting Coding Bu...
[Submitted on 18 Jun 2025 (v1), last revised 14 Jul 2026 (this v · 2025-06-18 · via cs.CR updates on arXiv.org

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Abstract:Large language models (LLMs) are increasingly used for code understanding, yet their practical effectiveness for vulnerability detection relative to Static Application Security Testing (SAST) remains insufficiently quantified. We present a controlled comparative study between GPT-4 (Advanced Data Analysis) and two SAST tools (SonarQube and Cloud Defence) on 32 curated security scenarios representing common coding pitfalls. Each scenario is scored with a binary detection rule, the two SAST outputs are aggregated using a logical OR baseline, and paired outcomes are evaluated using McNemar's test for statistical significance. In our dataset, GPT-4 correctly detected 30 of 32 scenarios (93.75\%), while the aggregated SAST baseline detected 11 of 32. The paired comparison shows a statistically significant difference in detection performance in favour of GPT-4. We also discuss security considerations and operational constraints for integrating LLM-enhanced vulnerability scanning into secure software development workflows.

Submission history

From: William Buchanan Prof [view email]
[v1] Wed, 18 Jun 2025 07:47:12 UTC (250 KB)
[v2] Tue, 14 Jul 2026 12:11:00 UTC (257 KB)