arXiv:2606.04929v1 Announce Type: new Abstract: LLM post-training proceeds through multiple stages, e.g., supervised fine-tuning (SFT) followed by reinforcement learning from human feedback (RLHF) or direct preference optimization (DPO), where each stage draws data from different, potentially untrusted sources. Existing literature assumes data poisoning attacks may occur at each training stage, but neglects the possibility of multiple attackers. To study the trustworthiness of the entire post-training pipeline, we propose the threat model of sequential data poisoning, where multiple adversarie
As LLMs become more integrated into critical systems, the focus is shifting from basic model development to securing the entire training and deployment pipeline against sophisticated attacks.
Sophisticated data poisoning attacks can compromise the integrity, safety, and reliability of LLMs, undermining trust and potentially leading to catastrophic failures in downstream applications.
The threat model for LLM security now explicitly includes sequential, multi-stage attacks by multiple adversaries, requiring a more holistic and robust defense strategy across the entire post-training process.
- · AI security researchers
- · Cybersecurity firms specializing in AI
- · Developers of robust LLM evaluation frameworks
- · Organizations relying on untrusted data sources
- · LLM providers with weak security protocols
- · Users of compromised AI systems
Increased investment in LLM security and data provenance tools will occur.
New standards and regulations for LLM supply chain security will emerge to address these advanced threats.
The development and deployment of LLMs in highly sensitive areas (e.g., defense, finance) will be significantly delayed until certified secure pipelines are established.
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Read at arXiv cs.LG