46 citations · 47 across the 4 of their papers we have counts for
4 papers
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…
From Bad to Worse: Using Private Data to Propagate Disinformation on Online Platforms with a Greater Efficiency
Protik Bose Pranto, Waqar Hassan Khan, Sahar Abdelnabi +3
We outline a planned experiment to investigate if personal data (e.g., demographics and behavioral patterns) can be used to selectively expose individuals to disinformation such th…
Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection
Kai Greshake, Sahar Abdelnabi, Shailesh Mishra +3
Large Language Models (LLMs) are increasingly being integrated into various applications. The functionalities of recent LLMs can be flexibly modulated via natural language prompts.…
Open-Domain, Content-based, Multi-modal Fact-checking of Out-of-Context Images via Online Resources
Sahar Abdelnabi, Rakibul Hasan, Mario Fritz
Misinformation is now a major problem due to its potential high risks to our core democratic and societal values and orders. Out-of-context misinformation is one of the easiest and…