SAGE: Signal-Amplified Guided Embeddings for Vulnerability Detection

Abstract

Software vulnerabilities are a primary threat to modern infrastructure. While static analysis and Graph Neural Networks have long served as the foundation for vulnerability detection, the emergence of Large Language Models (LLMs) has introduced a transformative paradigm driven by superior semantic reasoning and cross-environment generalization. However, in the context of LLM-based vulnerability detection, we identify a fundamental bottleneck in these models termed Signal Submersion : a state where features related to vulnerability are activated internally but numerically overwhelmed by dominant functional semantics. To address this, we propose SAGE ( S ignal- A mplified G uided E mbeddings), a framework that shifts from passive signal submersion to active signal recovery. SAGE integrates task-conditional Sparse Autoencoders (SAEs) to isolate and amplify these faint vulnerability signals. Extensive evaluations on BigVul, PrimeVul, and PreciseBugs demonstrate that SAGE achieves state-of-the-art performance. Notably, SAGE mitigates Signal Submersion by increasing the internal Signal-to-Noise Ratio (SNR) by 12.7× via sparse manifold projection. This mechanistic intervention enables a 7B model to achieve up to 318% Matthews Correlation Coefficient (MCC) gains on unseen distributions and a 319% gain on classic datasets. By maintaining robust performance across 13 programming languages and outperforming 34B baselines, SAGE establishes a more efficient and scalable path to software security than simple parameter scaling.