19 citations · 25 across the 8 of their papers we have counts for
5 papers · 1 filter
EmbTracker: Traceable Black-box Watermarking for Federated Language Models
Haodong Zhao, Jinming Hu, Yijie Bai +6
Federated Language Model (FedLM) allows a collaborative learning without sharing raw data, yet it introduces a critical vulnerability, as every untrustworthy client may leak the re…
PINA: Prompt Injection Attack against Navigation Agents
Jiani Liu, Yixin He, Lanlan Fan +5
Navigation agents powered by large language models (LLMs) convert natural language instructions into executable plans and actions. Compared to text-based applications, their securi…
Attention is All You Need to Defend Against Indirect Prompt Injection Attacks in LLMs
Yinan Zhong, Qianhao Miao, Yanjiao Chen +3
Large Language Models (LLMs) have been integrated into many applications (e.g., web agents) to perform more sophisticated tasks. However, LLM-empowered applications are vulnerable…
Patronus: Safeguarding Text-to-Image Models against White-Box Adversaries
Xinfeng Li, Shengyuan Pang, Jialin Wu +5
Text-to-image (T2I) models, though exhibiting remarkable creativity in image generation, can be exploited to produce unsafe images. Existing safety measures, e.g., content moderati…
RACONTEUR: A Knowledgeable, Insightful, and Portable LLM-Powered Shell Command Explainer
Jiangyi Deng, Xinfeng Li, Yanjiao Chen +5
Malicious shell commands are linchpins to many cyber-attacks, but may not be easy to understand by security analysts due to complicated and often disguised code structures. Advance…