Backdoor attacks compromise pretrained world models used for robot control
Researchers have identified a critical supply-chain vulnerability in pretrained world models, the learned simulators increasingly repurposed as off-the-shelf dynamics backbones for control tasks. An adversary with access to a released checkpoint can inject a backdoor that hijacks downstream controllers even when victims train and evaluate on clean data, without ever encountering an explicit trigger. The attack exploits the latent space itself, routing poisoned observations into specific regions and warping local dynamics so that standard optimization procedures (actor training or planning algorithms) inadvertently execute the attacker's objectives. This finding exposes a structural risk in the emerging paradigm of foundation model reuse, analogous to supply-chain threats in language models but operating at the dynamics level.68
