most citedUsing Retriever Augmented Large Language Models for Attack Graph Generation

4 citations · 9 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CR20244 cited

Using Retriever Augmented Large Language Models for Attack Graph Generation

Renascence Tarafder Prapty, Ashish Kundu, Arun Iyengar

As the complexity of modern systems increases, so does the importance of assessing their security posture through effective vulnerability management and threat modeling techniques.…

cs.AI20241 cited

Code Hallucination

Mirza Masfiqur Rahman, Ashish Kundu

Generative models such as large language models are extensively used as code copilots and for whole program generation. However, the programs they generate often have questionable…

cs.CV20241 cited

RAW: A Robust and Agile Plug-and-Play Watermark Framework for AI-Generated Images with Provable Guarantees

Xun Xian, Ganghua Wang, Xuan Bi +4

Safeguarding intellectual property and preventing potential misuse of AI-generated images are of paramount importance. This paper introduces a robust and agile plug-and-play waterm…

cs.CR20241 cited

Transfer Learning for Security: Challenges and Future Directions

Adrian Shuai Li, Arun Iyengar, Ashish Kundu +1

Many machine learning and data mining algorithms rely on the assumption that the training and testing data share the same feature space and distribution. However, this assumption m…

cs.CR20231 cited

Demystifying Poisoning Backdoor Attacks from a Statistical Perspective

Ganghua Wang, Xun Xian, Jayanth Srinivasa +4

The growing dependence on machine learning in real-world applications emphasizes the importance of understanding and ensuring its safety. Backdoor attacks pose a significant securi…

cs.HC20231 cited

Evaluating Chatbots to Promote Users' Trust -- Practices and Open Problems

Biplav Srivastava, Kausik Lakkaraju, Tarmo Koppel +3

Chatbots, the common moniker for collaborative assistants, are Artificial Intelligence (AI) software that enables people to naturally interact with them to get tasks done. Although…