Xie, Z., Gao, J., Li, L., Li, Z., Liu, Q., & Kong, L. (2024). Jailbreaking as a Reward Misspecification Problem. arXiv preprint arXiv:2406.14393v3.
This paper investigates the vulnerability of safety-aligned large language models (LLMs) to jailbreaking, proposing that reward misspecification during the alignment process is the root cause. The authors aim to develop a new metric to quantify this misspecification and leverage it to create a system for automated red teaming.
The researchers introduce ReGap, a metric that quantifies the extent of reward misspecification by measuring the difference in implicit rewards assigned to harmless and harmful responses. They then develop ReMiss, an automated red teaming system that utilizes ReGap to identify and exploit vulnerabilities in aligned LLMs by generating adversarial prompts. The effectiveness of ReMiss is evaluated on the AdvBench benchmark and compared to existing jailbreaking methods.
The study demonstrates that reward misspecification is a critical vulnerability in aligned LLMs, enabling successful jailbreaking attacks. ReGap and ReMiss provide valuable tools for understanding and mitigating these vulnerabilities, contributing to the development of safer and more reliable LLMs.
This research highlights a crucial security concern in the development and deployment of LLMs. By framing jailbreaking as a reward misspecification problem, the study offers a novel perspective and practical tools for improving the safety and robustness of aligned LLMs.
The study primarily focuses on a specific type of jailbreaking attack using appended suffixes. Future research could explore other attack vectors and develop more sophisticated defense mechanisms against reward misspecification. Additionally, investigating the generalization of ReMiss to other domains and tasks would be beneficial.
To Another Language
from source content
arxiv.org
Key Insights Distilled From
by Zhihui Xie, ... at arxiv.org 10-07-2024
https://arxiv.org/pdf/2406.14393.pdfDeeper Inquiries