arXiv:2605.23168v1 Announce Type: cross Abstract: When practitioners fine-tune LLMs on unvetted datasets, an adversary can exploit the data supply chain through task-level poisoning: inserting a small number of crafted instruction-response pairs that cause the model to embed attacker-specified entities, such as a country, in outputs for a targeted task family while behaving normally elsewhere. We introduce PoisonForge, a benchmark that parameterizes this threat along four dimensions (bias type, poisoning mode, appearance count, and target output length) and evaluates 12 open-weight models (fro
The proliferation of open-source LLMs and the practice of fine-tuning them on diverse datasets makes the vulnerability to data supply chain attacks an immediate concern.
This benchmark reveals a significant security vulnerability in LLM development, where malicious actors can surreptitiously embed biases or specific outputs into models.
Developers of LLMs must now urgently implement more robust data vetting and supply chain security measures to prevent targeted poisoning attacks.
- · Cybersecurity firms specializing in AI
- · Organizations developing secure training pipeline tools
- · Auditors of AI models
- · Developers fine-tuning LLMs on unvetted datasets
- · Users trusting black-box LLMs implicitly
- · Companies relying on compromised LLMs
Increased focus on data provenance and security in AI model development.
New regulatory requirements or industry standards for LLM fine-tuning and data supply chains might emerge.
The weaponization of LLM poisoning could lead to targeted disinformation campaigns or embedded malicious functionalities in AI systems.
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Read at arXiv cs.LG