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Fragmented defenses against LLM backdoor attacks stem from fundamental differences in how clean and dirty-label attacks manipulate model features.
LLMs can be backdoored to "think well but answer wrong," even while generating seemingly correct reasoning traces, making attacks far harder to detect.
