Overflow vulnerability found in FHE for private neural network inference

Researchers have identified a critical vulnerability in Fully Homomorphic Encryption (FHE) schemes, specifically the widely used CKKS scheme, which can lead to overflow attacks. These attacks corrupt neural network outputs by causing inputs to exceed the tolerances of FHE circuits. To address this, the paper proposes a formal verification technique that calculates certified bounds for neuron ranges, effectively eliminating overflows and reducing failure rates to zero in experimental benchmarks. This overflow-free solution is compatible with existing CKKS frameworks by allowing the substitution of standard polynomials with rigorously designed ones. AI

IMPACT Addresses a critical security flaw in using FHE for private AI inference, potentially enabling more robust and secure deployment of AI models.

RANK_REASON Academic paper detailing a new vulnerability and a proposed solution for a specific cryptographic scheme used in AI. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• CKKS
• Fully Homomorphic Encryption
• neural networks

• paper
• safety

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