Competition Report: Finding Universal Jailbreak Backdoors in Aligned LLMs

Authors: Javier Rando, Francesco Croce, Kryštof Mitka, Stepan Shabalin, Maksym Andriushchenko, Nicolas Flammarion, Florian Tramèr
Published: 2024-04-22 | Updated: 2024-06-06

Source: https://arxiv.org/abs/2404.14461

PDF: https://arxiv.org/pdf/2404.14461

Labels Predicted by AI

プロンプトインジェクション バックドア攻撃 LLMセキュリティ

Please note that these labels were automatically added by AI. Therefore, they may not be entirely accurate.
For more details, please see the About the Literature Database page.

Abstract

Large language models are aligned to be safe, preventing users from generating harmful content like misinformation or instructions for illegal activities. However, previous work has shown that the alignment process is vulnerable to poisoning attacks. Adversaries can manipulate the safety training data to inject backdoors that act like a universal sudo command: adding the backdoor string to any prompt enables harmful responses from models that, otherwise, behave safely. Our competition, co-located at IEEE SaTML 2024, challenged participants to find universal backdoors in several large language models. This report summarizes the key findings and promising ideas for future research.