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20242026
most citedCWEval: Outcome-driven Evaluation on Functionality and Security of LLM Code Generation

3 citations · 5 across the 9 of their papers we have counts for

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5 papers · 1 filter

cs.CR2025

Your Compiler is Backdooring Your Model: Understanding and Exploiting Compilation Inconsistency Vulnerabilities in Deep Learning Compilers

Simin Chen, Jinjun Peng, Yixin He +2

Deep learning (DL) compilers are core infrastructure in modern DL systems, offering flexibility and scalability beyond vendor-specific libraries. This work uncovers a fundamental v…

cs.CR2025

Proactive defense against LLM Jailbreak

Weiliang Zhao, Jinjun Peng, Daniel Ben-Levi +2

The proliferation of powerful large language models (LLMs) has necessitated robust safety alignment, yet these models remain vulnerable to evolving adversarial attacks, including m…

cs.SE2025

kAgent: An execution-guided crash resolution agent for the Linux kernel

Alex Mathai, Chenxi Huang, Suwei Ma +7

Fuzzing frameworks like syzkaller have uncovered thousands of Linux kernel crashes, many of which are critical and security-sensitive. However, the ability to rapidly repair these…

cs.SE2025

EditLord: Learning Code Transformation Rules for Code Editing

Weichen Li, Albert Jan, Baishakhi Ray +3

Code editing is a foundational task in software development, where its effectiveness depends on whether it introduces desired code property changes without changing the original co…

cs.SE2025★ 3 cited

CWEval: Outcome-driven Evaluation on Functionality and Security of LLM Code Generation

Jinjun Peng, Leyi Cui, Kele Huang +2

Large Language Models (LLMs) have significantly aided developers by generating or assisting in code writing, enhancing productivity across various tasks. While identifying incorrec…