8 citations · 14 across the 9 of their papers we have counts for
11 papers
Tetrahedral linkage as an intrinsic measure of glycan antifreeze behavior
Aakash Kumar, Shoumik Saha, Dilip Gersappe
Antifreeze materials prevent ice-formation by disrupting the ice-formation by binding to certain ice-planes. Cellulose, the most abundant biopolymer, has shown the ability to bind…
Same Question, Different Answers: Evaluating LLM Reliability Beyond Accuracy
Kazem Faghih, Yize Cheng, Shoumik Saha +3
Large language models (LLMs) often achieve strong accuracy on benchmarks, yet it remains unclear how reliably they apply this knowledge when the same question is phrased in differe…
Under the Hood of SKILL.md: Semantic Supply-chain Attacks on AI Agent Skill Registry
Shoumik Saha, Kazem Faghih, Soheil Feizi
Autonomous AI agents increasingly extend their capabilities through Agent Skills: modular filesystem packages whose SKILL.md files describe when and how agents should use them. Whi…
Breaking the Code: Security Assessment of AI Code Agents Through Systematic Jailbreaking Attacks
Shoumik Saha, Jifan Chen, Sam Mayers +3
Code-capable large language model (LLM) agents are embedded in software engineering workflows where they can read, write, and execute code, raising "jailbreak" stakes beyond text-o…
Adversarial Paraphrasing: A Universal Attack for Humanizing AI-Generated Text
Yize Cheng, Vinu Sankar Sadasivan, Mehrdad Saberi +2
The increasing capabilities of Large Language Models (LLMs) have raised concerns about their misuse in AI-generated plagiarism and social engineering. While various AI-generated te…
Almost AI, Almost Human: The Challenge of Detecting AI-Polished Writing
Shoumik Saha, Soheil Feizi
The growing use of large language models (LLMs) for text generation has led to widespread concerns about AI-generated content detection. However, an overlooked challenge is AI-poli…
ML-Based Behavioral Malware Detection Is Far From a Solved Problem
Yigitcan Kaya, Yizheng Chen, Marcus Botacin +5
Malware detection is a ubiquitous application of Machine Learning (ML) in security. In behavioral malware analysis, the detector relies on features extracted from program execution…
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…
Contrastive Self-Supervised Learning Based Approach for Patient Similarity: A Case Study on Atrial Fibrillation Detection from PPG Signal
Subangkar Karmaker Shanto, Shoumik Saha, Atif Hasan Rahman +2
In this paper, we propose a novel contrastive learning based deep learning framework for patient similarity search using physiological signals. We use a contrastive learning based…
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…
MALIGN: Explainable Static Raw-byte Based Malware Family Classification using Sequence Alignment
Shoumik Saha, Sadia Afroz, Atif Rahman
For a long time, malware classification and analysis have been an arms-race between antivirus systems and malware authors. Though static analysis is vulnerable to evasion technique…