35 citations · 73 across the 11 of their papers we have counts for
7 papers · 1 filter
Effective and Efficient Jailbreaks of Black-Box LLMs with Cross-Behavior Attacks
Vasudev Gohil
Despite recent advancements in Large Language Models (LLMs) and their alignment, they can still be jailbroken, i.e., harmful and toxic content can be elicited from them. While exis…
LLMPirate: LLMs for Black-box Hardware IP Piracy
Vasudev Gohil, Matthew DeLorenzo, Veera Vishwa Achuta Sai Venkat Nallam +2
The rapid advancement of large language models (LLMs) has enabled the ability to effectively analyze and generate code nearly instantaneously, resulting in their widespread adoptio…
MABFuzz: Multi-Armed Bandit Algorithms for Fuzzing Processors
Vasudev Gohil, Rahul Kande, Chen Chen +2
As the complexities of processors keep increasing, the task of effectively verifying their integrity and security becomes ever more daunting. The intricate web of instructions, mic…
PSOFuzz: Fuzzing Processors with Particle Swarm Optimization
Chen Chen, Vasudev Gohil, Rahul Kande +2
Hardware security vulnerabilities in computing systems compromise the security defenses of not only the hardware but also the software running on it. Recent research has shown that…
Reinforcement Learning for Hardware Security: Opportunities, Developments, and Challenges
Satwik Patnaik, Vasudev Gohil, Hao Guo +2
Reinforcement learning (RL) is a machine learning paradigm where an autonomous agent learns to make an optimal sequence of decisions by interacting with the underlying environment.…
ATTRITION: Attacking Static Hardware Trojan Detection Techniques Using Reinforcement Learning
Vasudev Gohil, Hao Guo, Satwik Patnaik +2
Stealthy hardware Trojans (HTs) inserted during the fabrication of integrated circuits can bypass the security of critical infrastructures. Although researchers have proposed many…