15 citations · 50 across the 24 of their papers we have counts for
6 papers · 1 filter
Text-to-Image Models Leave Identifiable Signatures: Implications for Leaderboard Security
Ali Naseh, Anshuman Suri, Yuefeng Peng +3
Generative AI leaderboards are central to evaluating model capabilities, but remain vulnerable to manipulation. Among key adversarial objectives is rank manipulation, where an atta…
Exploiting Leaderboards for Large-Scale Distribution of Malicious Models
Anshuman Suri, Harsh Chaudhari, Yuefeng Peng +3
While poisoning attacks on machine learning models have been extensively studied, the mechanisms by which adversaries can distribute poisoned models at scale remain largely unexplo…
Toward Principled LLM Safety Testing: Solving the Jailbreak Oracle Problem
Shuyi Lin, Anshuman Suri, Alina Oprea +1
As large language models (LLMs) become increasingly deployed in safety-critical applications, the lack of systematic methods to assess their vulnerability to jailbreak attacks pres…
SAGA: A Security Architecture for Governing AI Agentic Systems
Georgios Syros, Anshuman Suri, Jacob Ginesin +2
Large Language Model (LLM)-based agents increasingly interact, collaborate, and delegate tasks to one another autonomously with minimal human interaction. Industry guidelines for a…
DROP: Poison Dilution via Knowledge Distillation for Federated Learning
Georgios Syros, Anshuman Suri, Farinaz Koushanfar +2
Federated Learning is vulnerable to adversarial manipulation, where malicious clients can inject poisoned updates to influence the global model's behavior. While existing defense m…
Riddle Me This! Stealthy Membership Inference for Retrieval-Augmented Generation
Ali Naseh, Yuefeng Peng, Anshuman Suri +3
Retrieval-Augmented Generation (RAG) enables Large Language Models (LLMs) to generate grounded responses by leveraging external knowledge databases without altering model parameter…