4 papers
SoK: AI Secure Code Generation: Progress, Pitfalls, and Paths Forward
Rupam Patir, Keyan Guo, Haipeng Cai +1
The increasing use of AI systems for code generation raises a central security question: what can today's models and coding agents actually do to produce secure code, where do they…
DualGauge: Automated Joint Security-Functionality Benchmarking of Specification-Only Code Generation by LLMs and Coding Agents
Rupam Patir, Keyan Guo, Suvadra Barua +5
Large language models (LLMs) and LLM-based coding agents are now used to generate code from natural-language specifications, yet ensuring such code is both functionally correct and…
Fortifying LLM-Based Code Generation with Graph-Based Reasoning on Secure Coding Practices
Rupam Patir, Keyan Guo, Haipeng Cai +1
The code generation capabilities of Large Language Models (LLMs) have transformed the field of software development. However, this advancement also presents significant security ch…
Interpretability of Blackbox Machine Learning Models through Dataview Extraction and Shadow Model creation
Rupam Patir, Shubham Singhal, C. Anantaram +1
Deep learning models trained using massive amounts of data tend to capture one view of the data and its associated mapping. Different deep learning models built on the same trainin…