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This study reveals a critical vulnerability in AI coding assistants, which fail to verify software origins despite the availability of trust signals like digital signatures and software bills of materials. The finding that these tools utilize such security data in less than one percent of trials highlights a significant gap between creating provenance information and its actual implementation in automated workflows. For the research community, this underscores the urgent need for developers to either harden AI verification logic or manually audit installations when integrating untrusted code into high-performance or quantum computing environments.
Assistants Rarely Install Software after Checking Trust Signals during Less Than One Percent of Trials

Across nearly two thousand installation attempts by AI coding assistants, none checked where software originated before installing it. Despite increasing availability of machine-checkable trust signals, such as lists of components or digital signatures, these were opened in only nine trials and never used for verification. This demonstrates a critical gap between creating security information and actually utilising it within automated workflows.
AI coding assistants have selected, installed and configured software without routinely verifying its origins despite the availability of trust signals such as software bills of materials or signatures. A thorough study involving 1,920 registered trials revealed that these assistants rarely check where software comes from before installation. Artificial intelligence coding assistants infrequently verify the origin of software during installation despite security features designed to provide this information. Publishing details about software origins is therefore insufficient to secure systems because these tools…
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