8 citations · 14 across the 9 of their papers we have counts for
5 papers · 1 filter
Stealix: Model Stealing via Prompt Evolution
Zhixiong Zhuang, Hui-Po Wang, Maria-Irina Nicolae +1
Model stealing poses a significant security risk in machine learning by enabling attackers to replicate a black-box model without access to its training data, thus jeopardizing int…
ProxyPrompt: Securing System Prompts against Prompt Extraction Attacks
Zhixiong Zhuang, Maria-Irina Nicolae, Hui-Po Wang +1
The integration of large language models (LLMs) into a wide range of applications has highlighted the critical role of well-crafted system prompts, which require extensive testing…
Medical Multimodal Model Stealing Attacks via Adversarial Domain Alignment
Yaling Shen, Zhixiong Zhuang, Kun Yuan +4
Medical multimodal large language models (MLLMs) are becoming an instrumental part of healthcare systems, assisting medical personnel with decision making and results analysis. Mod…
Fundamental Risks in the Current Deployment of General-Purpose AI Models: What Have We (Not) Learnt From Cybersecurity?
Mario Fritz
General Purpose AI - such as Large Language Models (LLMs) - have seen rapid deployment in a wide range of use cases. Most surprisingly, they have have made their way from plain lan…
Stealthy Imitation: Reward-guided Environment-free Policy Stealing
Zhixiong Zhuang, Maria-Irina Nicolae, Mario Fritz
Deep reinforcement learning policies, which are integral to modern control systems, represent valuable intellectual property. The development of these policies demands considerable…