CodeQL detected 171 CVEs total, with 83 caught by a prior version before the fix; detections were often actionable within the vulnerable file but not stable across tool versions.
Semgrep*: Improving the limited performance of static application security testing (SAST) tools
5 Pith papers cite this work. Polarity classification is still indexing.
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representative citing papers
NESA presents a neuro-symbolic framework that decomposes static analyses into policy-defined sub-problems solved by parsers and LLMs to enable compilation-free customizable analysis with reduced hallucinations.
PRISM detects and stops credential leakage during LLM generation in multi-agent pipelines using per-token risk scores from lexical, structural, and behavioral signals, achieving zero observed leaks and F1 of 0.832 on a 2000-task benchmark.
LLM approaches ExArch and ArTEMiS reach F1 scores of 0.86 and 0.81 for architecture entity recognition and traceability, matching or approaching baselines that require manual models.
citing papers explorer
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Longitudinal Analyses of SAST Tools: A CodeQL Case Study
CodeQL detected 171 CVEs total, with 83 caught by a prior version before the fix; detections were often actionable within the vulnerable file but not stable across tool versions.
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NESA: Relational Neuro-Symbolic Static Program Analysis
NESA presents a neuro-symbolic framework that decomposes static analyses into policy-defined sub-problems solved by parsers and LLMs to enable compilation-free customizable analysis with reduced hallucinations.
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PRISM: Generation-Time Detection and Mitigation of Secret Leakage in Multi-Agent LLM Pipelines
PRISM detects and stops credential leakage during LLM generation in multi-agent pipelines using per-token risk scores from lexical, structural, and behavioral signals, achieving zero observed leaks and F1 of 0.832 on a 2000-task benchmark.
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Who's Who? LLM-assisted Software Traceability with Architecture Entity Recognition
LLM approaches ExArch and ArTEMiS reach F1 scores of 0.86 and 0.81 for architecture entity recognition and traceability, matching or approaching baselines that require manual models.
- REALISTA: Realistic Latent Adversarial Attacks that Elicit LLM Hallucinations