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Adversarial Reasoning at Jailbreaking Time

Created by
  • Haebom

Author

Mahdi Sabbaghi, Paul Kassianik, George Pappas, Yaron Singer, Amin Karbasi, Hamed Hassani

Outline

In this paper, we focus on the problem of “model jailbreaking”, which induces malicious responses, as the importance of studying failure cases of large-scale language models (LLMs) increases. Based on recent advances in standardization, measurement, and test-time computation extension, we present a model optimization methodology to achieve high performance on difficult tasks. We perform automated model jailbreaking by exploiting loss signals that drive test-time computations via an adversarial inference approach, achieving state-of-the-art attack success rates on multiple aligned LLMs. This also applies to models that trade inference-time computation for adversarial robustness. In conclusion, we present a new paradigm for LLM vulnerabilities, and lay the foundation for developing more robust and reliable AI systems.

Takeaways, Limitations

Takeaways:
Presenting a new understanding and analysis method for LLM vulnerabilities
Development of an automated model-breaking technique based on adversarial inference and achieving state-of-the-art performance
A new direction for improving LLM robustness using inference-time operations
Laying the foundation for developing safer and more reliable AI systems
Limitations:
Further verification of the generalizability of the proposed method and its applicability to various LLMs is required.
Ethical considerations are needed regarding the potential use for malicious purposes.
Development and research of defense techniques for the proposed method are needed.
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