Security in the Fine-Tuning Lifecycle of Large Language Models: Threats, Defenses,Evaluation, and Future Directions
arXiv:2605.25073v1 Announce Type: cross Abstract: Background: Fine-tuning is central to adapting pre-trained Large Language Models (LLMs) to downstream tasks, but its reliance on training data, parameter updates, and reusable components opens entry points for attackers. Threats have evolved from data poisoning and weight tampering to agent manipulation and interface exploitation, yet existing reviews lack a unified framework spanning the full fine-tuning lifecycle. Objective: This paper presents a systematic survey of LLM fine-tuning security and establishes a lifecycle-based framework for com
As fine-tuning becomes the standard for adapting LLMs to specific tasks and applications, the vulnerabilities introduced by this process are becoming critical concerns for widespread adoption and security.
A systematic understanding of fine-tuning security threats and defenses is crucial for developing robust and trustworthy AI systems, which will accelerate the deployment of LLM-powered applications across sensitive domains.
The focus of AI security expands from pre-training and inference to encompass the entire fine-tuning lifecycle, necessitating new frameworks, tools, and best practices for safeguarding LLMs.
- · AI security researchers
- · Cybersecurity firms
- · Organizations developing secure LLM applications
- · AI red teams
- · Developers neglecting security in fine-tuning
- · Organizations relying on insecure LLM deployments
- · Untrustworthy AI models
Increased investment in specialized AI security talent and solutions for LLM fine-tuning.
New regulatory and compliance standards for AI systems will likely incorporate fine-tuning security requirements.
The emergence of 'secure-by-design' LLM fine-tuning platforms, enhancing trust in agentic AI deployments.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG