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20242026
most citedNYU CTF Bench: A Scalable Open-Source Benchmark Dataset for Evaluating LLMs in Offensive Security

3 citations · 3 across the 10 of their papers we have counts for

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7 papers · 1 filter

cs.CR2026

Which Image Property Carries the Jailbreak? A Controlled Dissection of Image-to-Text Jailbreaks

Boyuan Chen, Yehia Dawoud, Hailemariam Mersha +4

Image-to-text jailbreaks place harmful intent in text, image content, or the relationship between them. We examine image-side factors across four published attack families on a 313…

cs.SD2026

Where Does the Audio Jailbreak Live? A Controlled Frequency-Depth Audit of AdvWave-P on Qwen2-Audio

Boyuan Chen, Minseok Kim, Sohaila Abdulsattar +4

We audit frequency and decoder-depth claims for AdvWave-P, an additive audio jailbreak, on Qwen2-Audio. The protocol masks frequency components of the perturbation in the short-tim…

cs.CR2026

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…

cs.AR2026

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…

cs.SE2026

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…

cs.AR2026

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…

cs.CV2026

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…