PoisonForge: Task-Level Targeted Poisoning Benchmark for Instruction-Tuned LLMs
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 (from 2B to 32B parameters) across five families under a primarily 1% poison budget. With only 10 poisoned examples among 1,000 fine-tuning examples, 1...
| Authors | Luze Sun, Anshuman Suri, Harsh Chaudhari et al. |
|---|---|
| Primary category | cs.CR |
| Published | 22 May 2026 |
| Last updated at source | 22 May 2026 |
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