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The article argues that cosine similarity in vector databases is a mathematical property that does not ensure factual accuracy, rendering retrieval systems susceptible to poisoning attacks where malicious documents can surpass legitimate ones in ranking.
The paper introduces a 'wrong-physics backdoor' poisoning attack on neural PDE operators, where triggered inputs cause models to output valid solutions for alternate physical parameters, highlighting a critical security vulnerability in reusable solver archives.