Quantization-Triggered Backdoors in Language Models: Cross-Quantizer Transferability and the Validation--Deployment Gap
Post-training quantization can activate dormant backdoors in LLMs that pass full-precision safety checks—a validation-deployment gap with direct supply-chain implications for edge AI.
Summary written by editorial AI · Source link below
arXiv:2608.27512v1 Announce Type: cross Abstract: Post-training quantization is often treated as a semantically neutral optimization for edge deployment of Large Language Models. When a full-precision source checkpoint is evaluated and quantization is applied downstream without equivalent re-evaluation, this workflow creates a structural validation--deployment gap: because quantization is a many-to-one mapping over parameter space, source-precision certification does not guarantee behavioral eq
Editorial Analysis
Enterprises deploying quantized LLMs at the edge may unknowingly activate backdoors that were invisible during standard validation, creating a critical blind spot in the model supply chain.
Mandate post-quantization security and behavioural testing for all LLMs before edge deployment, especially when sourcing models from third-party providers.
Compressing AI models for efficient deployment can silently activate hidden backdoors—current safety testing misses this gap.
Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.
External link — opens at arXiv Crypto & Security in a new tab.
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