35 citations · 75 across the 12 of their papers we have counts for
12 papers
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…
CreativEval: Evaluating Creativity of LLM-Based Hardware Code Generation
Matthew DeLorenzo, Vasudev Gohil, Jeyavijayan Rajendran
Large Language Models (LLMs) have proved effective and efficient in generating code, leading to their utilization within the hardware design process. Prior works evaluating LLMs' a…
AttackGNN: Red-Teaming GNNs in Hardware Security Using Reinforcement Learning
Vasudev Gohil, Satwik Patnaik, Dileep Kalathil +1
Machine learning has shown great promise in addressing several critical hardware security problems. In particular, researchers have developed novel graph neural network (GNN)-based…
Make Every Move Count: LLM-based High-Quality RTL Code Generation Using MCTS
Matthew DeLorenzo, Animesh Basak Chowdhury, Vasudev Gohil +4
Existing large language models (LLMs) for register transfer level code generation face challenges like compilation failures and suboptimal power, performance, and area (PPA) effici…
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…
DETERRENT: Detecting Trojans using Reinforcement Learning
Vasudev Gohil, Satwik Patnaik, Hao Guo +3
Insertion of hardware Trojans (HTs) in integrated circuits is a pernicious threat. Since HTs are activated under rare trigger conditions, detecting them using random logic simulati…
Security of Cloud FPGAs: A Survey
Chenglu Jin, Vasudev Gohil, Ramesh Karri +1
Integrating Field Programmable Gate Arrays (FPGAs) with cloud computing instances is a rapidly emerging trend on commercial cloud computing platforms such as Amazon Web Services (A…
FlowBazaar: A Market-Mediated Software Defined Communications Ecosystem at the Wireless Edge
Rajarshi Bhattacharyya, Bainan Xia, Desik Rengarajan +2
The predominant use of wireless access networks is for media streaming applications, which are only gaining popularity as ever more devices become available for this purpose. Howev…