Dataset and Lessons Learned from the 2024 SaTML LLM Capture-the-Flag Competition
<|reference_start|>Dataset and Lessons Learned from the 2024 SaTML LLM Capture-the-Flag Competition: 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 this problem, we organized a capture-the-flag competition at IEEE SaTML 2024, where the flag is a secret string in the LLM system prompt. The competition was organized in two phases. In the first phase, teams developed defenses to prevent the model from leaking the secret. During the second phase, teams were challenged to extract the secrets hidden for defenses proposed by the other teams. This report summarizes the main insights from the competition. Notably, we found that all defenses were bypassed at least once, highlighting the difficulty of designing a successful defense and the necessity for additional research to protect LLM systems. To foster future research in this direction, we compiled a dataset with over 137k multi-turn attack chats and open-sourced the platform.<|reference_end|>
@article{debenedetti2024dataset,
    title={Dataset and Lessons Learned from the 2024 SaTML LLM Capture-the-Flag
  Competition},
    author={Edoardo Debenedetti, Javier Rando, Daniel Paleka, Silaghi Fineas
  Florin, Dragos Albastroiu, Niv Cohen, Yuval Lemberg, Reshmi Ghosh, Rui Wen,
  Ahmed Salem, Giovanni Cherubin, Santiago Zanella-Beguelin, Robin Schmid,
  Victor Klemm, Takahiro Miki, Chenhao Li, Stefan Kraft, Mario Fritz, Florian
  Tram`er, Sahar Abdelnabi, Lea Sch"onherr},
    journal={arXiv preprint arXiv:2406.07954},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.07954},
    primaryClass={cs.CR cs.AI}
}