← 研踪 PaperTrailACL Anthology · ACL 2026:acl:2026.acl-long.1481 · 记录 #1
SeCuRepair: Semantics-Aligned, Curriculum-Driven, and Reasoning-Enhanced Vulnerability Repair Framework
来源日期（仅到月份） 2026-07
来源记录日期 2026-07
雷达观测 2026-11-01 21:53
摘要
The rapid accumulation of software vulnerabilities has outpaced manual remediation, creating an urgent need for Automated Vulnerability Repair (AVR). However, existing methods suffer from syntactic overfitting, mimicking surface forms without understanding the underlying repair logic, and fail to generalize to complex fixes. To transcend these limitations, we propose SeCuRepair, a reliable, scalable, and efficient RL-based AVR framework. By introducing a semantic-aware reward, SeCuRepair optimizes for code semantic equivalence rather than lexical mimicry. Furthermore, SeCuRepair incorporates an expert-aligned reasoning mechanism that explicitly grounds patch generation in a structured diagnosis. Finally, SeCuRepair introduces a difficulty-based curriculum that progressively disentangles the optimization barriers of entangled multi-hunk repairs. Extensive evaluations on a rigorous repository-level split show that SeCuRepair substantially outperforms state-of-the-art baselines, as confirmed by both automatic evaluation and human study.
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记录 #1 · 2026-07 · 最新观测版本
SeCuRepair: Semantics-Aligned, Curriculum-Driven, and Reasoning-Enhanced Vulnerability Repair Framework
The rapid accumulation of software vulnerabilities has outpaced manual remediation, creating an urgent need for Automated Vulnerability Repair (AVR). However, existing methods suffer from syntactic overfitting, mimicking surface forms without understanding the underlying repair logic, and fail to generalize to complex fixes. To transcend these limitations, we propose SeCuRepair, a reliable, scalable, and efficient RL-based AVR framework. By introducing a semantic-aware reward, SeCuRepair optimizes for code semantic equivalence rather than lexical mimicry. Furthermore, SeCuRepair incorporates an expert-aligned reasoning mechanism that explicitly grounds patch generation in a structured diagnosis. Finally, SeCuRepair introduces a difficulty-based curriculum that progressively disentangles the optimization barriers of entangled multi-hunk repairs. Extensive evaluations on a rigorous repository-level split show that SeCuRepair substantially outperforms state-of-the-art baselines, as confirmed by both automatic evaluation and human study.
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