3 citations · 4 across the 8 of their papers we have counts for
7 papers · 1 filter
Watermarking for Byzantine Robust LLM Training
Leixu Huang, Zedian Shao, Teodora Baluta
LLM watermarking has emerged as a promising approach for detecting downstream model misuse in centralized settings, but extending it to federated learning (FL) presents unique chal…
Cordyceps: Covert Control Attacks on LLMs via Data Poisoning
Zedian Shao, Charles Fleming, Teodora Baluta
Large language models (LLMs) are often fine-tuned on uncurated text datasets that adversaries can poison. Existing poisoning attacks primarily rely on fixed trigger phrases that de…
Watermark Robustness and Radioactivity May Be at Odds in Federated Learning
Leixu Huang, Zedian Shao, Teodora Baluta
Federated learning (FL) enables fine-tuning large language models (LLMs) across distributed data sources. As these sources increasingly include LLM-generated text, provenance track…
PromptLocate: Localizing Prompt Injection Attacks
Yuqi Jia, Yupei Liu, Zedian Shao +2
Prompt injection attacks deceive a large language model into completing an attacker-specified task instead of its intended task by contaminating its input data with an injected pro…
A Critical Evaluation of Defenses against Prompt Injection Attacks
Yuqi Jia, Zedian Shao, Yupei Liu +3
Large Language Models (LLMs) are vulnerable to prompt injection attacks, and several defenses have recently been proposed, often claiming to mitigate these attacks successfully. Ho…
Enhancing Prompt Injection Attacks to LLMs via Poisoning Alignment
Zedian Shao, Hongbin Liu, Jaden Mu +1
Prompt injection attack, where an attacker injects a prompt into the original one, aiming to make an Large Language Model (LLM) follow the injected prompt to perform an attacker-ch…
Refusing Safe Prompts for Multi-modal Large Language Models
Zedian Shao, Hongbin Liu, Yuepeng Hu +1
Multimodal large language models (MLLMs) have become the cornerstone of today's generative AI ecosystem, sparking intense competition among tech giants and startups. In particular,…