6 papers
Representation Matters: An Empirical Study of Program Representations for LLM Vulnerability Reasoning
Andrew Stoltman, Johnathan Tang, Haipeng Cai
Large Language Models (LLMs) are increasingly used for automated vulnerability detection, but it remains unclear how program structure and semantics should be represented for LLM-b…
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…
Local LLM Agents as Vulnerable Runtimes:A Source-Code Audit of the Agent Runtime Layer
Zhengsong Zhang, Zongze Li, Jiawei Guo +1
Local LLM agents such as OpenClaw and Nanobot run on end-user machines and act on host resources - the shell, filesystem, browser, stored credentials, and messaging applications -…
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…
An Exploratory Study of Bayesian Prompt Optimization for Test-Driven Code Generation with Large Language Models
Shlok Tomar, Aryan Deshwal, Ethan Villalovoz +3
We consider the task of generating functionally correct code using large language models (LLMs). The correctness of generated code is influenced by the prompt used to query the giv…
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…