SAGE uses sparse autoencoders to boost vulnerability signals in LLMs, raising internal SNR 12.7x and delivering up to 318% MCC gains on vulnerability detection benchmarks.
Automated Software Engineering31(04 2024)
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A PRISMA-guided review of 21 papers shows RL work on C/C++ vulnerabilities focuses on fuzzing rather than detection or localization, proposes a taxonomy, and flags the lack of CFG-based state representations for vulnerable node identification.
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SAGE: Signal-Amplified Guided Embeddings for LLM-based Vulnerability Detection
SAGE uses sparse autoencoders to boost vulnerability signals in LLMs, raising internal SNR 12.7x and delivering up to 318% MCC gains on vulnerability detection benchmarks.
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Reinforcement Learning for Software Vulnerability Analysis: A Systematic Review with Emphasis on C/C++ Source Code and Static Analysis
A PRISMA-guided review of 21 papers shows RL work on C/C++ vulnerabilities focuses on fuzzing rather than detection or localization, proposes a taxonomy, and flags the lack of CFG-based state representations for vulnerable node identification.