mlsanityData to 7 Oct 2026

WeSCE: A Benchmark for Measuring Security Drift in LLM-Driven Code Editing

arXiv 2608.15092 ↗First posted 15 Aug 2026cs.CR cs.AI cs.SEcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 19 of its 28 references that are in the map. First posted on arXiv 15 Aug 2026.

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References · 19

  1. 2021Evaluating Large Language Models Trained on Codecited by 8,053
  2. 2021Program Synthesis with Large Language Modelscited by 3,509
  3. 2024LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Codecited by 1,906
  4. 2023Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generationcited by 1,332
  5. 2020CodeBERT: A Pre-Trained Model for Programming and Natural Languagescited by 1,286
  6. 2026DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligencecited by 877
  7. 2026Kimi K2.5: Visual Agentic Intelligencecited by 449
  8. 2021Asleep at the Keyboard? Assessing the Security of GitHub Copilot's Code Contributionscited by 417
  9. 2023Large Language Models for Code: Security Hardening and Adversarial Testingcited by 149
  10. 2024Evaluating Language Models for Efficient Code Generationcited by 69
  11. 2024EffiBench: Benchmarking the Efficiency of Automatically Generated Codecited by 62
  12. 2025CWEval: Outcome-driven Evaluation on Functionality and Security of LLM Code Generationcited by 55
7 more
  1. 2023How Effective Are Neural Networks for Fixing Security Vulnerabilitiescited by 50
  2. 2025PATCHEVAL: A New Benchmark for Evaluating LLMs on Patching Real-World Vulnerabilitiescited by 19
  3. 2026RealSec-bench: A Benchmark for Evaluating Secure Code Generation in Real-World Repositoriescited by 13
  4. 2025A.S.E: A Repository-Level Benchmark for Evaluating Security in AI-Generated Codecited by 12
  5. 2025Benchmarking Correctness and Security in Multi-Turn Code Generationcited by 4
  6. 2026RealVuln: Benchmarking Rule-Based, General-Purpose LLM, and Security-Specialized Scanners on Real-World Codecited by 4
  7. 2025Exploring Generalizable Automated Program Repair with Large Language Modelscited by 1

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