1 citations · 1 across the 3 of their papers we have counts for
4 papers
How Do Semantically Equivalent Code Transformations Impact Membership Inference on LLMs for Code?
Hua Yang, Alejandro Velasco, Thanh Le-Cong +3
The success of large language models for code relies on vast amounts of code data, including public open-source repositories, such as GitHub, and private, confidential code from co…
Secure or Suspect? Investigating Package Hallucinations of Shell Command in Original and Quantized LLMs
Md Nazmul Haque, Elizabeth Lin, Lawrence Arkoh +2
Large Language Models for code (LLMs4Code) are increasingly used to generate software artifacts, including library and package recommendations in languages such as Go. However, rec…
How Quantization Impacts Privacy Risk on LLMs for Code?
Md Nazmul Haque, Hua Yang, Zhou Yang +1
Large language models for code (LLMs4Code) rely heavily on massive training data, including sensitive data, such as cloud service credentials of the projects and personal identifia…
Closing the Chain: How to reduce your risk of being SolarWinds, Log4j, or XZ Utils
Sivana Hamer, Jacob Bowen, Md Nazmul Haque +3
Software supply chain frameworks, such as the US NIST Secure Software Development Framework (SSDF), detail what tasks software development organizations are recommended or mandated…