← Back to Library

LM-Fix: Lightweight Bit-Flip Detection and Rapid Recovery Framework for Language Models

Authors: Ahmad Tahmasivand, Noureldin Zahran, Saba Al-Sayouri, Mohammed Fouda, Khaled N. Khasawneh

Published: 2025-11-03

arXiv ID: 2511.02866v1

Added to Library: 2025-11-06 05:01 UTC

📄 Abstract

This paper presents LM-Fix, a lightweight detection and rapid recovery framework for faults in large language models (LLMs). Existing integrity approaches are often heavy or slow for modern LLMs. LM-Fix runs a short test-vector pass and uses hash-guided checks to detect bit-flip faults, then repairs them locally without a full reload. Across multiple models, it detects over 94% of single-bit flips at TVL=200 and nearly 100% of multi-bit flips with approximately 1% to 7.7% runtime overhead; recovery is more than 100x faster than reloading. These results show a practical, low-overhead solution to keep LLMs reliable in production

🔍 Key Points

  • The study reveals widespread vulnerabilities in the safety and security of open-weight LLMs, especially in multi-turn prompt interactions, with success rates on multi-turn attacks being 2x to 10x higher than single-turn attacks.
  • A comparative analysis of eight open-weight models showed how alignment strategies influence security gaps: capability-first models like Llama and Qwen demonstrated greater vulnerabilities compared to safety-oriented designs like Google Gemma.
  • The research utilized automated adversarial testing to evaluate models against various attack techniques, emphasizing the need for robust measures to address identified weaknesses and enhance model resilience.
  • Findings emphasize the importance of adopting a security-first design philosophy, recommending layered protections and advanced AI security solutions to mitigate risks associated with deploying open-weight models.
  • The study highlights the need for continuous monitoring and regular red-teaming exercises to ensure that models can withstand evolving threats and maintain operational integrity.

💡 Why This Paper Matters

This paper is critically important as it highlights the significant vulnerabilities inherent in open-weight large language models and the increased exposure to adversarial attacks, particularly in multi-turn scenarios. Its findings serve as a wake-up call for developers and organizations deploying these models, emphasizing the necessity for robust security measures to prevent misuse and protect sensitive information.

🎯 Why It's Interesting for AI Security Researchers

This paper would be of particular interest to AI security researchers due to its comprehensive analysis of adversarial vulnerabilities within prominent open-weight models. It provides valuable insights into the relationship between model design, alignment strategies, and their implications for security. The findings can directly inform the development of improved defenses and contribute to the ongoing discussions about responsible AI deployment and safe practices within the research community.

📚 Read the Full Paper

📄 View on arXiv 📎 Download PDF