4 citations · 6 across the 4 of their papers we have counts for
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cs.SE2026
SpecCloak: Probing Harmful Code Generation through Engineering Specifications
Wenbin Chen, Jiexin Wang, Guanting Wu +2
The code generation capabilities of large language models (LLMs) can equally be misused to implement harmful functionality, underscoring the need to understand their safety behavio…
cs.SE2024★ 4 cited
Toward Secure Code Generation: Bridging Correctness and Security via Task-Adaptive Vulnerability Modeling and Execution-Based Benchmarking
Jiexin Wang, Xitong Luo, Liuwen Cao +9
Large language models (LLMs) are increasingly used for program synthesis, yet they often generate code that is functionally plausible but insecure. Progress in secure code generati…
cs.SE2023★ 2 cited
Enhancing Large Language Models for Secure Code Generation: A Dataset-driven Study on Vulnerability Mitigation
Jiexin Wang, Liuwen Cao, Xitong Luo +4
Large language models (LLMs) have brought significant advancements to code generation, benefiting both novice and experienced developers. However, their training using unsanitized…