ZK-Hammer: Leaking Secrets from Zero-Knowledge Proofs via Rowhammer

Zero-knowledge succinct non-interactive arguments of knowledge (zk-SNARK) schemes have been a promising technique in verified computation. Zk-SNARK schemes were designed to be mathematically secure against cryptographic attacks and it remains unclear whether they are vulnerable to fault injection attacks. In this work, we provide a positive answer by presenting ZK-Hammer, which leaks secrets from zk-SNARK schemes via Rowhammer. We incur faults in the exponentiate variables in the Quadratic Arithmetic Program (QAP) problem. Then we analyze the faulty proof using the bilinear pairing technique and manage to recover the secret. We employ a Rowhammer fault evaluation in libsnark and identify 3 CVEs.

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Published
2025-06-22
DOI
https://doi.org/10.1109/dac63849.2025.11133021
Primary Topic
Benford’s Law and Fraud Detection
Type
article
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ZK-Hammer: Leaking Secrets from Zero-Knowledge Proofs via Rowhammer

Qingni Shen, Zhonghai Wu, Yuejian Fang, Xin Zhang et al.
Benford’s Law and Fraud Detection
article

ZK-Hammer: Leaking Secrets from Zero-Knowledge Proofs via Rowhammer

Qingni Shen, Zhonghai Wu, Yuejian Fang, Xin Zhang, Di Hu, Junkai Liang
article en

Abstract

Zero-knowledge succinct non-interactive arguments of knowledge (zk-SNARK) schemes have been a promising technique in verified computation. Zk-SNARK schemes were designed to be mathematically secure against cryptographic attacks and it remains unclear whether they are vulnerable to fault injection attacks. In this work, we provide a positive answer by presenting ZK-Hammer, which leaks secrets from zk-SNARK schemes via Rowhammer. We incur faults in the exponentiate variables in the Quadratic Arithmetic Program (QAP) problem. Then we analyze the faulty proof using the bilinear pairing technique and manage to recover the secret. We employ a Rowhammer fault evaluation in libsnark and identify 3 CVEs.

Software Engineering Institute (US), Peking University (CN)
Openalex Percentile: Top 76%
Benford’s Law and Fraud Detection
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