2 citations · 3 across the 6 of their papers we have counts for
6 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…
LLM Whisperer: An Inconspicuous Attack to Bias LLM Responses
Weiran Lin, Anna Gerchanovsky, Omer Akgul +3
Writing effective prompts for large language models (LLM) can be unintuitive and burdensome. In response, services that optimize or suggest prompts have emerged. While such service…
VeriSplit: Secure and Practical Offloading of Machine Learning Inferences across IoT Devices
Han Zhang, Zifan Wang, Mihir Dhamankar +2
Many Internet-of-Things (IoT) devices rely on cloud computation resources to perform machine learning inferences. This is expensive and may raise privacy concerns for users. Consum…
Transfer Attacks and Defenses for Large Language Models on Coding Tasks
Chi Zhang, Zifan Wang, Ravi Mangal +3
Modern large language models (LLMs), such as ChatGPT, have demonstrated impressive capabilities for coding tasks including writing and reasoning about code. They improve upon previ…
Is Certifying Robustness Still Worthwhile?
Ravi Mangal, Klas Leino, Zifan Wang +5
Over the years, researchers have developed myriad attacks that exploit the ubiquity of adversarial examples, as well as defenses that aim to guard against the security vulnerabilit…
A Recipe for Improved Certifiable Robustness
Kai Hu, Klas Leino, Zifan Wang +1
Recent studies have highlighted the potential of Lipschitz-based methods for training certifiably robust neural networks against adversarial attacks. A key challenge, supported bot…