想知道怎么不跑图就能筛出有害LoRA？这篇论文用权重向量做指纹，跨模型都管用，比看元数据靠谱多了。
arXiv论文提出通过LoRA权重左上角奇异向量(u1)生成无推理指纹，检测用于生成儿童性虐待材料的LoRA。研究以人类年龄作为CSAM的良性代理，发现u1能识别LoRA训练内容，跨基模型泛化，并对无关良性内容弃权。该信号对权重噪声、重缩放和精度降低具有鲁棒性。结果表明可从权重直接筛查有害LoRA，无需依赖元数据或生成有害输出。
Detecting CSAM Text-to-Image LoRAs From Weights
Low-rank adaptation (LoRA) fine-tuning has made it cheap and easy to customize open-weight image generation models for specific tasks, including the production of child sexual abuse material (CSAM). Existing moderation relies on metadata or generated outputs, but metadata can be deceptive and generating outputs may itself be unacceptable or illegal. We show that a safer signal lives in the weights. The top-left singular vectors of a LoRA's updates form a compact, inference-free fingerprint ($u_1$) of its strongest learned change. Using human-subject age as a benign proxy for CSAM, we find that $u_1$ identifies what a LoRA was trained on, generalizes across base models, and abstains on unrelated benign content. The signal is robust to additive weight noise, rescaling, and precision reduction. These results indicate that harmful LoRAs could be screened directly from their weights without relying on metadata or generating harmful outputs.