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University of Adelaide
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Cloned outputs can be detected with over 87% accuracy using behavioral signatures, revealing significant vulnerabilities in LLM deployment security.
Targeted bit-flip attacks can cripple Vision-Language-Action models, reducing their success rates to zero with just a few flips in key layers.
Corrupting the imagination in VLA policies is 60 times more effective than random perturbations, exposing a critical vulnerability in safety mechanisms.
