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arXiv · 2608.25730

From Verdict to Diagnosis: Attributable Security Review of Pull Requests

Zhuo Chen·Boyang Wang·Xiyue Zhang·Xiaoyun Xu·Ahmad-Reza Sadeghi·Stjepan Picek·Lichao Wu
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Abstract

Automated code reviewers are increasingly used as gates on pull requests (PRs), yet evaluations measure whether they block a malicious change. A block may be triggered by an unrelated issue rather than the vulnerability that makes the PR unsafe; fixing the reported issue can leave the target defect exploitable. We call this discrepancy the Verdict-Diagnosis (VD) gap. We present MalPR-Bench, a mechanism-grounded benchmark of 89 malicious PRs and 50 paired benign controls across 44 repositories and eight language families. Each malicious case has a pre-committed rubric specifying the target vulnerability, accepted mechanism descriptions, required repository evidence, and off-target findings receiving no credit. Reviews are scored separately for verdict correctness, target-vulnerability identification, and evidence validation; an attributable block requires all three. We introduce PRGuard, an attributable PR security reviewer that constructs candidate vulnerabilities and validates their premises against repository evidence using deterministic, non-executing tools and bounded retrieval. Across 31 common-coverage held-out malicious PRs, PRGuard and CodeRabbit produce similar blocking totals (22/31 vs. 24/31), but PRGuard identifies 22 target vulnerabilities versus 16 for CodeRabbit, a 1.38x difference. On 14 absence-type cases, both block 9, while PRGuard identifies 9 targets versus 3. CodeRabbit identifies 16/24 targets when required evidence lies within touched files and 0/7 when validation requires evidence outside them. Finally, PRGuard uncovers twelve previously undisclosed, proof-of-concept-backed vulnerabilities across five projects. PRGuard/DeepSeek and CodeRabbit both block 10/12 discovery PRs, but produce 10/12 and 4/12 attributable blocks, respectively. Thus, verdict-only evaluation can substantially overstate the security value of automated review.

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Zhuo Chen, Boyang Wang, Xiyue Zhang, Xiaoyun Xu, Ahmad-Reza Sadeghi, Stjepan Picek, Lichao Wu. 2026-08-26. From Verdict to Diagnosis: Attributable Security Review of Pull Requests. https://arxiv.org/abs/2608.25730

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