AutoTrace: From Patches to Triggers via Agentic Interprocedural Exploration

2026-07-13

AI Summary

This work addresses the challenge of locating cross-procedural vulnerability-triggering statements—specifically, identifying code that transforms program state into unsafe operations, particularly when situated deep within call chains outside patched functions. To this end, it proposes the first static analysis method integrating agent-guided exploration with causal evidence verification. The approach leverages large language model (LLM) agents to navigate code property graphs, while a deterministic acceptability gate ensures that all reported triggers are supported by explicit causal evidence. The study introduces SinkTrace-Bench, the first benchmark dataset of source-to-sink causal chains, comprising 1,542 balanced samples. Evaluated on InterPVD, the method achieves 75.0% VulnHit and 80.8% FuncHit, substantially outperforming existing techniques, and further exposes inherent limitations of LLMs in causal reasoning.

Abstract

Given a vulnerability-fixing commit, trigger localization asks which specific statement turns the vulnerable program state into a concrete unsafe operation. This question is harder than binary vulnerability detection because the answer demands interprocedural, causal reasoning: in a substantial fraction of real-world CVEs the triggering statement lies several call layers outside the patched function, beyond the reach of static rule sets and pattern-matching language models alike. We present AutoTrace, an agentic pipeline that localizes vulnerability triggers by exploring a code property graph layer by layer, with LLM agents deciding where to look next and deterministic admissibility gates deciding what evidence is required before a trigger can be reported. Agents never accept a trigger on their own authority; every reported trigger is backed by explicit evidence drawn from the graph, so the pipeline covers both intra- and interprocedural vulnerabilities without relying on ungrounded model judgment. On the full InterPVD benchmark, AutoTrace reaches 75.0% VulnHit and 80.8% FuncHit, surpassing the prior state of the art on the same corpus. Building on the same machinery, we construct SinkTrace-Bench, a dataset that exposes each vulnerability as a source-to-sink (S2S) causal chain from attacker-controlled input through propagation to the dangerous operation, drawn from matched vulnerable and patched program states. It comprises 1,542 verifier-confirmed, perfectly balanced vulnerable/safe samples whose label fidelity we audit against expert annotations. Benchmarking frontier LLMs on it, we find that even the strongest struggle to separate the matched pairs, exposing the causal-reasoning gap that trigger localization targets. Artifact available at https://github.com/Erroristotle/AutoTrace.

Problem

• trigger localization
• interprocedural reasoning
• vulnerability detection
• causal reasoning
• code property graph

Innovation

• trigger localization
• interprocedural reasoning
• agentic exploration
• code property graph
• causal chain