用语义变换检测指令微调中的恶意数据，无需提前知道攻击方式。

Instruction fine-tuning attacks pose a serious threat to large language models (LLMs) by subtly embedding poisoned examples in fine-tuning datasets, leading to harmful or unintended behaviors in downstream applications. Detecting such attacks is challenging because poisoned data is often indistinguishable from clean data, and prior knowledge of triggers or attack strategies is rarely available. We present a detection method that requires no prior knowledge of the attack. Our approach leverages influence functions under semantic transformation by comparing influence distributions before and after semantic inversions to identify critical poisons, defined as examples whose influence is strong and remains unchanged across transformations. We introduce a multi-transform ensemble approach that achieves F1 scores between 79.5 and 95.2 percent with precision between 66 and 100 percent on sentiment classification, significantly improving over single-transform methods. Our method generalizes to unseen transformation types with an F1 score of 86 percent through cross-category validation. We demonstrate effectiveness across multiple models, including T5-small and DeepSeek-Coder-1.3B, and across tasks such as sentiment classification and math reasoning. Removing a small fraction of detected poisons, between 1 and 3 percent of the data, restores model performance to near-clean levels. These results demonstrate the practicality of influence-based diagnostics for defending against instruction fine-tuning attacks in real-world large language model deployment. Artifact available at https://github.com/lijiawei20161002/Poison-Detection. Warning: this paper contains offensive data examples.