84 citations · 99 across the 34 of their papers we have counts for
14 papers · 1 filter
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…
How Do LLM Agents Actually Get the Flag? Trace-Level Provenance for Agentic Offensive Security Evaluation
Kimberly Milner, Minghao Shao, Nanda Rani +8
Capture-the-Flag (CTF) benchmarks are widely used to assess the offensive security capabilities of autonomous language-model agents. Evaluations rely on shallow binary judgments or…
HarmChip: Evaluating Hardware Security Centric LLM Safety via Jailbreak Benchmarking
Zeng Wang, Minghao Shao, Weimin Fu +6
The integration of large language models (LLMs) into electronic design automation (EDA) workflows has introduced powerful capabilities for RTL generation, verification, and design…
RAVEN: Retrieval-Augmented Vulnerability Exploration Network for Memory Corruption Analysis in User Code and Binary Programs
Parteek Jamwal, Minghao Shao, Boyuan Chen +15
Large Language Models (LLMs) have demonstrated remarkable capabilities across various cybersecurity tasks, including vulnerability classification, detection, and patching. However,…
CTFExplorer: Evaluating LLM Offensive Agents Through Multi-Target Web CTF Benchmarking
Nanda Rani, Kimberly Milner, Minghao Shao +9
Existing benchmarks for LLM-based offensive security agents use isolated, single-target setups with a known vulnerable service and fixed objective. They measure exploitation effect…
TrojanGYM: A Detector-in-the-Loop LLM for Adaptive RTL Hardware Trojan Insertion
Saideep Sreekumar, Zeng Wang, Akashdeep Saha +6
Hardware Trojans (HTs) remain a critical threat because learning-based detectors often overfit to narrow trigger/payload patterns and small, stylized benchmarks. We introduce Troja…
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…
NetDeTox: Adversarial and Efficient Evasion of Hardware-Security GNNs via RL-LLM Orchestration
Zeng Wang, Minghao Shao, Akashdeep Saha +4
Graph neural networks (GNNs) have shown promise in hardware security by learning structural motifs from netlist graphs. However, this reliance on motifs makes GNNs vulnerable to ad…
Towards Effective Offensive Security LLM Agents: Hyperparameter Tuning, LLM as a Judge, and a Lightweight CTF Benchmark
Minghao Shao, Nanda Rani, Kimberly Milner +9
Recent advances in LLM agentic systems have improved the automation of offensive security tasks, particularly for Capture the Flag (CTF) challenges. We systematically investigate t…
MetaCipher: A Time-Persistent and Universal Multi-Agent Framework for Cipher-Based Jailbreak Attacks for LLMs
Boyuan Chen, Minghao Shao, Abdul Basit +2
As large language models (LLMs) grow more capable, they face growing vulnerability to sophisticated jailbreak attacks. While developers invest heavily in alignment finetuning and s…
CRAKEN: Cybersecurity LLM Agent with Knowledge-Based Execution
Minghao Shao, Haoran Xi, Nanda Rani +9
Large Language Model (LLM) agents can automate cybersecurity tasks and can adapt to the evolving cybersecurity landscape without re-engineering. While LLM agents have demonstrated…
VeriLeaky: Navigating IP Protection vs Utility in Fine-Tuning for LLM-Driven Verilog Coding
Zeng Wang, Minghao Shao, Mohammed Nabeel +7
Large language models (LLMs) offer significant potential for coding, yet fine-tuning (FT) with curated data is essential for niche languages like Verilog. Using proprietary intelle…
NYU CTF Bench: A Scalable Open-Source Benchmark Dataset for Evaluating LLMs in Offensive Security
Minghao Shao, Sofija Jancheska, Meet Udeshi +10
Large Language Models (LLMs) are being deployed across various domains today. However, their capacity to solve Capture the Flag (CTF) challenges in cybersecurity has not been thoro…
An Empirical Evaluation of LLMs for Solving Offensive Security Challenges
Minghao Shao, Boyuan Chen, Sofija Jancheska +4
Capture The Flag (CTF) challenges are puzzles related to computer security scenarios. With the advent of large language models (LLMs), more and more CTF participants are using LLMs…