CVE-2024-45040
Published: 06 September 2024
Summary
CVE-2024-45040 is a medium-severity Exposure of Sensitive Information to an Unauthorized Actor (CWE-200) vulnerability in Consensys Gnark-Crypto. Its CVSS base score is 5.9 (Medium).
Operationally, exploitation aligns with the MITRE ATT&CK technique Password Cracking (T1110.002); ranked at the 43.6th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2024-2765
Vulnerability details
gnark is a fast zk-SNARK library that offers a high-level API to design circuits. Prior to version 0.11.0, commitments to private witnesses in Groth16 as implemented break the zero-knowledge property. The vulnerability affects only Groth16 proofs with commitments. Notably, PLONK…
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proofs are not affected. The vulnerability affects the zero-knowledge property of the proofs - in case the witness (secret or internal) values are small, then the attacker may be able to enumerate all possible choices to deduce the actual value. If the possible choices for the variables to be committed is large or there are many values committed, then it would be computationally infeasible to enumerate all valid choices. It doesn't affect the completeness/soundness of the proofs. The vulnerability has been fixed in version 0.11.0. The patch to fix the issue is to add additional randomized value to the list of committed value at proving time to mask the rest of the values which were committed. As a workaround, the user can manually commit to a randomized value.
- CWE(s)
Related Threats
MITRE ATT&CK Enterprise TechniquesAI
Why these techniques?
The vulnerability enables brute-force enumeration (analogous to password cracking) of small private witness values by recomputing commitments from the proving key and matching against the observed commitment in Groth16 proofs, breaking zero-knowledge.
Affected Assets
Mitigating Controls
Likely Mitigating Controls AI
Per-CVE control mapping for this CVE has not run yet; the list below is derived from the weakness types (CWEs) cited in the NVD entry.
Automated marking applies security attributes to system outputs, making it harder for attackers to exploit unmarked sensitive information leading to unauthorized exposure.
Proper attribute retention and permitted-value enforcement limits unauthorized actors from accessing sensitive information lacking correct labels.
Prevents unauthorized exposure of sensitive information by prohibiting untrusted external systems from processing or storing it.
By enforcing authorization matching prior to sharing, the control reduces the risk of exposing sensitive information to unauthorized actors.
Review and removal of nonpublic information from publicly accessible systems directly prevents exposure of sensitive data to unauthorized actors.
Data mining protection mechanisms detect and block unauthorized bulk extraction of sensitive data, directly mitigating exposure to unauthorized actors.
Literacy training teaches users to recognize and avoid actions that result in unauthorized exposure of sensitive information.
Retaining and monitoring training records confirms personnel have completed privacy and security awareness training on handling sensitive data, reducing the chance of unauthorized exposure due to lack of knowledge.