The paper demonstrated 90% success against knowledge bases containing millions of documents, using gradient-optimized payloads. What I tested is a vocabulary-engineering approach — no optimization against the embedding model — against a 5-document corpus. The corpus is obviously smaller than what the paper evaluated, so the success rate isn’t directly comparable. The value of a small local lab is reproducibility and clarity of mechanism, not scale. In a real production knowledge base with hundreds of documents on the same topic, the attacker needs more poisoned documents to reliably dominate the top-k — but the attack remains viable. The PoisonedRAG authors showed that even at millions-of-documents scale, five crafted documents are sufficient when using their optimization approach.
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out = weights @ V # dispatch 5。手游对此有专业解读
For me 的空气动力学设计中，最引人注目的是一套可升降激光雷达。，更多细节参见heLLoword翻译
Python 中验证 mmcv-full GPU 扩展是否可用：