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TrojanPraise: Jailbreak LLMs via Benign Fine-Tuning

Authors: Zhixin Xie, Xurui Song, Jun Luo

Published: 2026-01-18

arXiv ID: 2601.12460v1

Added to Library: 2026-01-21 03:00 UTC

Red Teaming

📄 Abstract

The demand of customized large language models (LLMs) has led to commercial LLMs offering black-box fine-tuning APIs, yet this convenience introduces a critical security loophole: attackers could jailbreak the LLMs by fine-tuning them with malicious data. Though this security issue has recently been exposed, the feasibility of such attacks is questionable as malicious training dataset is believed to be detectable by moderation models such as Llama-Guard-3. In this paper, we propose TrojanPraise, a novel finetuning-based attack exploiting benign and thus filter-approved data. Basically, TrojanPraise fine-tunes the model to associate a crafted word (e.g., "bruaf") with harmless connotations, then uses this word to praise harmful concepts, subtly shifting the LLM from refusal to compliance. To explain the attack, we decouple the LLM's internal representation of a query into two dimensions of knowledge and attitude. We demonstrate that successful jailbreak requires shifting the attitude while avoiding knowledge shift, a distortion in the model's understanding of the concept. To validate this attack, we conduct experiments on five opensource LLMs and two commercial LLMs under strict black-box settings. Results show that TrojanPraise achieves a maximum attack success rate of 95.88% while evading moderation.

🔍 Key Points

  • Introduction of TrojanPraise, a novel attack method that uses benign fine-tuning to exploit vulnerabilities in LLMs.
  • Decoupling of knowledge and attitude dimensions to provide a theoreticalfoundation for understanding the attack mechanism.
  • Experimental validation demonstrating a high attack success rate (up to 95.88%) across multiple models while evading moderation systems.
  • Highlighting the inadequacy of existing moderation models in detecting benign fine-tuning attacks, emphasizing the need for more robust defense mechanisms.
  • Discussion of the ethical implications and limitations of the attack, suggesting areas for future research in AI safety.

💡 Why This Paper Matters

The paper is relevant as it uncovers critical security vulnerabilities inherent in LLM fine-tuning services. By demonstrating how benign data can be weaponized to bypass existing moderation and safety mechanisms, it calls attention to the urgent need for implementing stronger safeguards to protect against such novel attack methods. The findings emphasize that traditional security measures may not be sufficient and that AI systems must evolve to counter new types of risks.

🎯 Why It's Interesting for AI Security Researchers

This research is of paramount relevance to AI security researchers as it reveals a significant gap in the safety and compliance mechanisms of large language models. Understanding the TrojanPraise attack not only helps in recognizing current vulnerabilities but also propels the development of better defenses against adversarial tactics targeting AI systems. The insights from this paper can guide future research into creating more secure AI models, ultimately contributing to safer AI deployment in real-world applications.

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