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Security Risks from Activation Approximations in Large Language Models

Large language models (LLMs) have demonstrated remarkable capabilities in various domains. However, with the growing capabilities and expanding deployment scenarios of these models, the challenges of their implementation also increase, particularly due to their enormous size and the advanced, yet complex, activation designs prevalent in popular model series like Llama, Gemma, and Mistral. These challenges are especially apparent in resource-constrained deployment scenarios, where mitigating inference efficiency bottlenecks is crucial.

Among various recent efforts, activation approximation has emerged as a promising approach for boosting inference efficiency and is sometimes considered indispensable for applications like private inference. While significant speed gains with minimal impact on usability have been achieved, and the method appears suitable for practical deployment, the implications of activation approximations on safety remain unclear.

A recent study addresses this critical gap in the field of LLM safety, conducting the first systematic security evaluation of activation approximations. The security audit encompasses seven state-of-the-art techniques across three common categories and reveals a consistent degradation in safety across ten safety-aligned LLMs.

Potential Dangers of Activation Approximations

The study highlights that activation approximations, despite their efficiency benefits, can introduce security vulnerabilities in LLMs, even when these models have been previously aligned for safety. This is a significant finding, as the efficiency gains from approximations are often considered necessary to deploy LLMs in resource-constrained environments or for specific applications like private inference.

Extensive Investigation and Results

The investigation encompassed a variety of activation approximation techniques and different LLMs. The results showed a consistent degradation of safety across various models and techniques. This suggests that the issue is not limited to specific models or approximation methods but represents a systemic problem.

Outlook and Significance for the Future

The findings of this study underscore the need for further research in the area of safe and efficient LLM deployment. It is crucial to find the balance between performance and safety and to develop strategies that leverage the benefits of activation approximations without compromising the security of the models. The development of more robust approximation methods or alternative strategies for efficiency gains could be a vital step in this direction. The insights from this study are relevant for LLM developers, security researchers, and anyone involved in the practical deployment of LLMs.

Bibliography: Zhang, J., Chen, K., He, L., Lou, J., Li, D., Feng, Z., Song, M., Liu, J., Ren, K., & Yang, X. (2025). Activation Approximations Can Incur Safety Vulnerabilities Even in Aligned LLMs: Comprehensive Analysis and Defense. arXiv preprint arXiv:2502.00840. LLM Safety Challenges. Retrieved from https://llm-safety-challenges.github.io/challenges_llms.pdf Su, C., Wang, X., & Zhang, T. (2024). Zero-Shot Hallucination Detection via Measuring the Evidence Strength of Generated Texts. In Findings of the Association for Computational Linguistics: ACL 2024 (pp. 1181-1194). USENIX Security '24. Retrieved from https://www.usenix.org/conference/usenixsecurity24/technical-sessions Wang, X., Su, C., & Zhang, T. (2024). Towards Comprehensive Factuality Evaluation of Large Language Models: Unifying Existing Benchmarks and Metrics. In OpenReview. Chawins, LLM-SP. Retrieved from https://github.com/chawins/llm-sp ```