This research reveals critical security vulnerabilities in DeepSeek's Chain-of-Thought (CoT) reasoning model, demonstrating how fine-tuning attacks can manipulate the model to generate harmful content.

• Identifies how pre-training data containing harmful information can be exploited through adversarial fine-tuning
• Demonstrates specific attack vectors that bypass safety alignment in CoT-enabled models
• Shows that advanced reasoning capabilities in LLMs may create unique security vulnerabilities
• Highlights urgent implications for secure LLM deployment in production environments

For security professionals and AI developers, this research underscores the need for robust defense mechanisms against increasingly sophisticated attacks on reasoning-enhanced language models.

The dark deep side of DeepSeek: Fine-tuning attacks against the safety alignment of CoT-enabled models