163 citations · 1.4k across the 139 of their papers we have counts for
5 papers · 1 filter
Attacker's Noise Can Manipulate Your Audio-based LLM in the Real World
Vinu Sankar Sadasivan, Soheil Feizi, Rajiv Mathews +1
This paper investigates the real-world vulnerabilities of audio-based large language models (ALLMs), such as Qwen2-Audio. We first demonstrate that an adversary can craft stealthy…
DREW : Towards Robust Data Provenance by Leveraging Error-Controlled Watermarking
Mehrdad Saberi, Vinu Sankar Sadasivan, Arman Zarei +2
Identifying the origin of data is crucial for data provenance, with applications including data ownership protection, media forensics, and detecting AI-generated content. A standar…
Fast Adversarial Attacks on Language Models In One GPU Minute
Vinu Sankar Sadasivan, Shoumik Saha, Gaurang Sriramanan +3
In this paper, we introduce a novel class of fast, beam search-based adversarial attack (BEAST) for Language Models (LMs). BEAST employs interpretable parameters, enabling attacker…
DRSM: De-Randomized Smoothing on Malware Classifier Providing Certified Robustness
Shoumik Saha, Wenxiao Wang, Yigitcan Kaya +2
Machine Learning (ML) models have been utilized for malware detection for over two decades. Consequently, this ignited an ongoing arms race between malware authors and antivirus sy…
Sample Efficient Detection and Classification of Adversarial Attacks via Self-Supervised Embeddings
Mazda Moayeri, Soheil Feizi
Adversarial robustness of deep models is pivotal in ensuring safe deployment in real world settings, but most modern defenses have narrow scope and expensive costs. In this paper,…