According to @AnthropicAI, new research shows that inserting just a few malicious documents into training or fine-tuning data can introduce exploitable vulnerabilities in an AI model regardless of model size or dataset scale, making data-poisoning attacks more practical than previously believed. Source: @AnthropicAI on X, Oct 9, 2025. For traders, this finding elevates model-risk considerations for AI-driven strategies and AI-integrated crypto protocols where outputs depend on potentially poisoned models, underscoring the need for provenance-verified data, robust evaluation, and continuous monitoring when relying on LLM outputs. Source: @AnthropicAI on X, Oct 9, 2025. Based on this update, monitor security disclosures from major AI providers and dataset hygiene policies that could affect service reliability and valuations across AI-related equities and AI-crypto narratives. Source: @AnthropicAI on X, Oct 9, 2025.