3 citations · 8 across the 4 of their papers we have counts for
4 papers
How Vulnerable Are AI Agents to Indirect Prompt Injections? Insights from a Large-Scale Public Competition
Mateusz Dziemian, Maxwell Lin, Xiaohan Fu +28
LLM based agents are increasingly deployed in high stakes settings where they process external data sources such as emails, documents, and code repositories. This creates exposure…
AI Risk Management Should Incorporate Both Safety and Security
Xiangyu Qi, Yangsibo Huang, Yi Zeng +22
The exposure of security vulnerabilities in safety-aligned language models, e.g., susceptibility to adversarial attacks, has shed light on the intricate interplay between AI safety…
Defensive Prompt Patch: A Robust and Interpretable Defense of LLMs against Jailbreak Attacks
Chen Xiong, Xiangyu Qi, Pin-Yu Chen +1
Safety, security, and compliance are essential requirements when aligning large language models (LLMs). However, many seemingly aligned LLMs are soon shown to be susceptible to jai…
Mitigating Fine-tuning based Jailbreak Attack with Backdoor Enhanced Safety Alignment
Jiongxiao Wang, Jiazhao Li, Yiquan Li +7
Despite the general capabilities of Large Language Models (LLM), these models still request fine-tuning or adaptation with customized data when meeting specific business demands. H…