LLM-Driven Adaptive Source-Sink Identification and False Positive Mitigation for Static Analysis

Static analysis is effective for discovering software vulnerabilities but notoriously suffers from incomplete source–sink specifications and excessive false positives (FPs). We present $\text{textsc}AdaTaint$, an LLM-driven taint analysis framework that adaptively infers source/sink specifications and filters spurious alerts through neuro-symbolic reasoning. Unlike LLM-only detectors, $\text{textsc}AdaTaint$ grounds model suggestions in program facts and constraint validation, ensuring both adaptability and determinism. We evaluate $\text{textsc}AdaTaint$ on Juliet 1.3, SV-COMP-style C benchmarks, and three large real-world projects. Results show that $\text{textsc}AdaTaint$ reduces false positives by $\mathbf{\text{43.7\%}}$ on average and improves recall by $\mathbf{\text{11.2\%}}$ compared to state-of-the-art baselines (CodeQL, Joern, and LLM-only pipelines), while maintaining competitive runtime overhead. These findings demonstrate that combining LLM inference with symbolic validation offers a practical path toward more accurate and reliable static vulnerability analysis.