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Quantization-Triggered Backdoors in Language Models: Cross-Quantizer Transferability and the Validation--Deployment Gap
Not provided in the abstract
quantizationbackdoor attackslanguage modelsbehavioral equivalence
2608.27512
Builder Relevance
2h ago80%
Abstract
The paper discusses the risks of quantization in language models, highlighting a validation-deployment gap that can lead to backdoor attacks.
Reality Card
Core Claim
The study proves that quantization can trigger backdoor attacks in language models, demonstrating that source-precision certification does not ensure behavioral equivalence in deployed configurations.
Method / Result
Backdoored translation models showed up to 85.02% inversion after quantization.
Limitations
The study's findings may vary across different quantization schemes and model architectures, complicating reproducibility.
Paper to code
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