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WeSCE introduces a critical benchmark for evaluating security drift in LLM-driven code editing, addressing the gap where models optimize purely functional objectives while inadvertently introducing vulnerabilities. By quantifying risk changes across average-case and worst-case scenarios during refactoring or feature updates, this work provides essential metrics for assessing safety trade-offs in automated software development pipelines.
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WeSCE: A Benchmark for Measuring Security Drift in LLM-Driven Code Editing

arXiv1 min read

arXiv:2608.15092v1 Announce Type: cross Abstract: In this work, we introduce WeSCE, a benchmark for quantifying security drift in code editing under weak-security constraints, where tasks specify only functional objectives without explicit security requirements. WeSCE consists of 400 executable programs derived from real-world code, covering feature addition, feature removal, bug fixing, and refactoring. To quantify security drift, we propose a continuous risk representation that aggregates heterogeneous vulnerability signals through a unified formulation, and define drift measures capturing changes in overall risk, worst-case severity, and vulnerability distribution under code transformations, providing a multi-scale view of security spanning average-case behavior to worst-case emphasis.

Abstract:In this work, we introduce WeSCE, a benchmark for quantifying security drift in code editing under weak-security constraints, where tasks specify only functional objectives without explicit security requirements. WeSCE consists of 400 executable programs derived from real-world code, covering feature addition, feature removal, bug fixing, and refactoring. To quantify security drift, we propose a continuous risk representation that aggregates heterogeneous vulnerability signals through a unified formulation, and define drift measures capturing changes in overall risk, worst-case severity, and vulnerability distribution under code transformations, providing a multi-scale view of security spanning average-case behavior to worst-case emphasis.

Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Software Engineering (cs.SE)

arXiv:2608.15092 [cs.CR]

(or arXiv:2608.15092v1 [cs.CR] for this version)

https://doi.org/10.48550/arXiv.2608.15092 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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Full text under the publisher's open licence · 196 words

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