Deep Backdoors in Deep Reinforcement Learning Agents
Black Hat
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Deep Backdoors in Deep Reinforcement Learning Agents exposes how attackers can stealthily manipulate AI agents through poisoned training or malicious architectures, posing serious risks in high-stakes systems like nuclear fusion reactors, and explores early defenses like neural firewalls.
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The architecture of the neural net itself is malicious; it doesn't matter what you do with the training; the malicious functionality stays in there. This is very scary.
—Black Hat
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