From Trace to Line: LLM Agent for Real-World OSS Vulnerability Localization

• URL: http://arxiv.org/abs/2510.02389v1
• Date: Tue, 30 Sep 2025 22:27:18 GMT
• Title: From Trace to Line: LLM Agent for Real-World OSS Vulnerability Localization
• Abstract summary: We present T2L-Agent, a project-level, end-to-end framework that plans its own analysis and narrows scope from modules to exact vulnerable lines.<n>We benchmark T2L-ARVO, a diverse, expert-verified 50-case benchmark spanning five crash families and real-world projects.<n>On T2L-ARVO, T2L-Agent achieves up to 58.0% detection and 54.8% line-level localization, substantially outperforming baselines.
• Score: 14.474705451897691
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models show promise for vulnerability discovery, yet prevailing methods inspect code in isolation, struggle with long contexts, and focus on coarse function- or file-level detections - offering limited actionable guidance to engineers who need precise line-level localization and targeted patches in real-world software development. We present T2L-Agent (Trace-to-Line Agent), a project-level, end-to-end framework that plans its own analysis and progressively narrows scope from modules to exact vulnerable lines. T2L-Agent couples multi-round feedback with an Agentic Trace Analyzer (ATA) that fuses runtime evidence - crash points, stack traces, and coverage deltas - with AST-based code chunking, enabling iterative refinement beyond single pass predictions and translating symptoms into actionable, line-level diagnoses. To benchmark line-level vulnerability discovery, we introduce T2L-ARVO, a diverse, expert-verified 50-case benchmark spanning five crash families and real-world projects. T2L-ARVO is specifically designed to support both coarse-grained detection and fine-grained localization, enabling rigorous evaluation of systems that aim to move beyond file-level predictions. On T2L-ARVO, T2L-Agent achieves up to 58.0% detection and 54.8% line-level localization, substantially outperforming baselines. Together, the framework and benchmark push LLM-based vulnerability detection from coarse identification toward deployable, robust, precision diagnostics that reduce noise and accelerate patching in open-source software workflows.

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