aiqbench analysis
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.
WeSCE: A Benchmark for Measuring Security Drift in LLM-Driven Code Editing
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)
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Full text under the publisher's open licence · 196 words
Related coverage
[AINews] High Taste Testers Ep. 1
Latent Space · Oct 17, 2026

Article: Multi-Agent AI for Production Security Operations: an A2A and MCP Architecture in a 5G Core
InfoQ · Jul 23, 2026
Links #3: 2026/06 Part 1
LessWrong · Jun 15, 2026
Benchmarking Kimi-K3 Across vLLM, SGLang, and ATOM on MI350X
AMD · Sep 22, 2026