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paperJuly 2026Unreviewed

Poster: Rethinking Security in LLM Code Generation through Real-World Risk Scenarios

Lixun Ma, Ruolong Ma, Bei Wang, Feng Wei, Zhenguang Liu, Lorenzo Cavallaro, Wentao Chen

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Abstract

Large Language Models (LLMs) are widely used for code generation, yet their security behavior in realistic development workflows remains underexplored. Existing benchmarks often rely on explicitly specified security requirements, failing to capture real-world scenarios where prompts are frequently ambiguous or incomplete. In this paper, we adopt a developer-centric perspective and identify three representative risk scenarios that commonly lead to security vulnerabilities in LLM-generated code: A

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Benchmarks & Evaluation

Cite

@misc{ma2026poster,
  title = {{Poster: Rethinking Security in LLM Code Generation through Real-World Risk Scenarios}},
  author = {Lixun Ma and Ruolong Ma and Bei Wang and Feng Wei and Zhenguang Liu and Lorenzo Cavallaro and Wentao Chen},
  year = {2026},
  month = jul,
  eprint = {2607.23088},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2607.23088}
}