10 papers
Surgical Repair of Insecure Code Generation in LLMs
Gustavo Sandoval, Brendan Dolan-Gavitt, Siddharth Garg
Large language models write production code, and yet they routinely introduce well-known vulnerabilities. We show that this is not a knowledge deficit: the same models that generat…
VeriInteresting: An Empirical Study of Model Prompt Interactions in Verilog Code Generation
Luca Collini, Andrew Hennesee, Patrick Yubeaton +2
Rapid advances in language models (LMs) have created new opportunities for automated code generation while complicating trade-offs between model characteristics and prompt design c…
AI In Cybersecurity Education -- Scalable Agentic CTF Design Principles and Educational Outcomes
Haoran Xi, Minghao Shao, Kimberly Milner +11
Large language models are rapidly changing how learners acquire and demonstrate cybersecurity skills. However, when human--AI collaboration is allowed, educators still lack validat…
Exploring the Agentic Frontier of Verilog Code Generation
Patrick Yubeaton, Siddharth Garg, Chinmay Hegde
Large language models (LLMs) have made rapid advancements in code generation for popular languages such as Python and C++. Many of these recent gains can be attributed to the use o…
GroundCount: Grounding Vision-Language Models with Object Detection for Mitigating Counting Hallucinations
Boyuan Chen, Minghao Shao, Siddharth Garg +2
Vision Language Models (VLMs) exhibit persistent hallucinations in counting tasks, with accuracy substantially lower than other visual reasoning tasks (excluding sentiment). This p…
TrojanLoC: LLM-based Framework for RTL Trojan Localization
Weihua Xiao, Zeng Wang, Minghao Shao +6
Hardware Trojans (HT s) are a persistent threat to integrated circuits, especially when inserted at the register-transfer level (RTL). Existing methods typically first convert the…