4 citations · 5 across the 7 of their papers we have counts for
9 papers
Toward Reliable, Safe, and Secure LLMs for Scientific Applications
Saket Sanjeev Chaturvedi, Joshua Bergerson, Tanwi Mallick
As large language models (LLMs) evolve into autonomous "AI scientists," they promise transformative advances but introduce novel vulnerabilities, from potential "biosafety risks" t…
MOBA: A Material-Oriented Backdoor Attack against LiDAR-based 3D Object Detection Systems
Saket S. Chaturvedi, Gaurav Bagwe, Lan Zhang +2
LiDAR-based 3D object detection is widely used in safety-critical systems. However, these systems remain vulnerable to backdoor attacks that embed hidden malicious behaviors during…
Your RAG is Unfair: Exposing Fairness Vulnerabilities in Retrieval-Augmented Generation via Backdoor Attacks
Gaurav Bagwe, Saket S. Chaturvedi, Xiaolong Ma +3
Retrieval-augmented generation (RAG) enhances factual grounding by integrating retrieval mechanisms with generative models but introduces new attack surfaces, particularly through…
AIP: Subverting Retrieval-Augmented Generation via Adversarial Instructional Prompt
Saket S. Chaturvedi, Gaurav Bagwe, Lan Zhang +1
Retrieval-Augmented Generation (RAG) enhances large language models (LLMs) by retrieving relevant documents from external sources to improve factual accuracy and verifiability. How…
BadFusion: 2D-Oriented Backdoor Attacks against 3D Object Detection
Saket S. Chaturvedi, Lan Zhang, Wenbin Zhang +2
3D object detection plays an important role in autonomous driving; however, its vulnerability to backdoor attacks has become evident. By injecting ''triggers'' to poison the traini…
Pay "Attention" to Adverse Weather: Weather-aware Attention-based Object Detection
Saket S. Chaturvedi, Lan Zhang, Xiaoyong Yuan
Despite the recent advances of deep neural networks, object detection for adverse weather remains challenging due to the poor perception of some sensors in adverse weather. Instead…