Self-Mined Hardness for Safety Fine-Tuning

Prakhar Gupta, Garv Shah, Donghua Zhang

Abstract

Safety fine-tuning of language models typically requires a curated adversarial dataset. We take a different approach: score each candidate prompt's difficulty by how often the target model's own rollouts are judged harmful, then fine-tune on the hardest prompts paired with the model's own non-jailbroken rollouts. On Llama-3-8B-Instruct and Llama-3.2-3B-Instruct, this approach cuts the WildJailbreak attack success rate from 11.5% and 20.1% down to 1-3%, but pushes refusal on jailbreak-shaped beni

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Cite This Resource

@article{llmsec202600123,
  title = {Self-Mined Hardness for Safety Fine-Tuning},
  author = {Prakhar Gupta and Garv Shah and Donghua Zhang},
  year = {2026},
  url = {https://arxiv.org/abs/2605.03226},
}