5 citations · 12 across the 6 of their papers we have counts for
6 papers
Vera Verto: Multimodal Hijacking Attack
Minxing Zhang, Ahmed Salem, Michael Backes +1
The increasing cost of training machine learning (ML) models has led to the inclusion of new parties to the training pipeline, such as users who contribute training data and compan…
Breaking Agents: Compromising Autonomous LLM Agents Through Malfunction Amplification
Boyang Zhang, Yicong Tan, Yun Shen +4
Recently, autonomous agents built on large language models (LLMs) have experienced significant development and are being deployed in real-world applications. These agents can exten…
SOS! Soft Prompt Attack Against Open-Source Large Language Models
Ziqing Yang, Michael Backes, Yang Zhang +1
Open-source large language models (LLMs) have become increasingly popular among both the general public and industry, as they can be customized, fine-tuned, and freely used. Howeve…
Dataset and Lessons Learned from the 2024 SaTML LLM Capture-the-Flag Competition
Edoardo Debenedetti, Javier Rando, Daniel Paleka +18
Large language model systems face important security risks from maliciously crafted messages that aim to overwrite the system's original instructions or leak private data. To study…
Comprehensive Assessment of Toxicity in ChatGPT
Boyang Zhang, Xinyue Shen, Wai Man Si +6
Moderating offensive, hateful, and toxic language has always been an important but challenging topic in the domain of safe use in NLP. The emerging large language models (LLMs), su…
Last One Standing: A Comparative Analysis of Security and Privacy of Soft Prompt Tuning, LoRA, and In-Context Learning
Rui Wen, Tianhao Wang, Michael Backes +2
Large Language Models (LLMs) are powerful tools for natural language processing, enabling novel applications and user experiences. However, to achieve optimal performance, LLMs oft…