Third-party cyber evaluations involving OpenAI models
openai.com·14h ago
AI Research
arXiv CS.AI·May 26·10 min read
Security in the Fine-Tuning Lifecycle of Large Language Models: Threats, Defenses,Evaluation, and Future Directions
TL;DR
Fine-tuning large language models (LLMs) is vulnerable to various security threats, including data poisoning and weight tampering. A lifecycle-based framework was developed to systematically evaluate these threats and defenses across three phases: pre-tuning, during-tuning, and post-tuning.
✦ Why It Matters
Engineers can enhance LLM security by understanding phase-specific vulnerabilities and developing robust, cross-phase defense mechanisms.
Key Takeaways
- Implement phase-specific security measures during LLM fine-tuning.
- Evaluate attack effectiveness based on model architecture and scale.
- Explore cross-phase defense strategies for improved security.
- Address configuration-robust defense challenges in future research.
Full Summary
Fine-tuning is essential for adapting pre-trained large language models (LLMs) to specific tasks, but it introduces security vulnerabilities due to reliance on training data and model parameters. This study created a lifecycle-based framework to analyze and compare security threats and defenses during the fine-tuning process, categorizing them into three phases: pre-tuning, during-tuning, and post-tuning.
The researchers evaluated various attack methods, such as weight-editing and cross-lingual backdoor transfer, and found that their effectiveness is highly model-dependent and diminishes with larger models. For instance, weight-editing attacks that worked on earlier models were less effective on modern open-source LLMs.
Additionally, defenses were found to be phase-specific, with few generalizing across different phases. Key open problems were identified, including the need for configuration-robust defenses and cross-phase defense strategies, guiding future research directions in LLM security.