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Learn about LLM poisoning, a critical security vulnerability where as few as 250 malicious documents can create backdoors in large language models regardless of their size or training data volume. Explore groundbreaking research from Anthropic, conducted jointly with the UK AI Security Institute and the Alan Turing Institute, demonstrating how both 13B parameter models and smaller 600M models can be compromised by the same minimal number of poisoned documents despite the larger model being trained on over 20 times more data. Understand the implications of this security breakthrough for AI safety and the surprising finding that model scale doesn't provide protection against this type of attack.
