Be Kind, Rewrite: Benign Projections via Rewriting Defend Against LLM Data Poisoning Attacks

Abstract

Large language models (LLMs) are highly susceptible to backdoor attacks (BAs), wherein training samples are poisoned using trigger-based harmful content. Furthermore, existing defenses have proven ineffective when extensively tested across BA patterns. To better combat BAs, we explore the use of LLM rewriting as a proactive defense against data poisoning. First, we theoretically show that when LLM rewriting utilizes open-book benign samples--termed open-book benign rewriting (OBBR)--the probabil

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@misc{halloran2026be,
  title = {{Be Kind, Rewrite: Benign Projections via Rewriting Defend Against LLM Data Poisoning Attacks}},
  author = {John T. Halloran and Noopur S. Bhatt},
  year = {2026},
  month = may,
  eprint = {2605.19147},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2605.19147}
}