Toward Robust Defenses Against LLM Weight Tampering Attacks

ICLR 2025 Conference Submission8414 Authors

Keywords: ai safety, large language models, tamper-resistance, unlearning, meta-learning

TL;DR: We introduce the first safeguards for LLMs that defend against a significant number of fine-tuning attacks.

Abstract: Rapid advances in the capabilities of large language models (LLMs) have raised widespread concerns regarding their potential for malicious use. Open-weight LLMs present unique challenges, as existing safeguards lack robustness to tampering attacks that modify model weights. For example, recent works have demonstrated that refusal and unlearning safeguards can be trivially removed with a few steps of fine-tuning. These vulnerabilities necessitate new approaches for enabling the safe release of open-weight LLMs. We develop a method, called TAR, for building tamper-resistant safeguards into open-weight LLMs such that adversaries cannot remove the safeguards even after thousands of steps of fine-tuning. In extensive evaluations and red teaming analyses, we find that our method greatly improves tamper-resistance while preserving benign capabilities. Our results demonstrate that progress on tamper-resistance is possible, opening up a promising new avenue to improve the safety and security of open-weight LLMs.

Primary Area: alignment, fairness, safety, privacy, and societal considerations

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Submission Number: 8414