Vulnerability-Lookup

GSD-2019-7167

Vulnerability from gsd - Updated: 2023-12-13 01:23

Details

Zcash, before the Sapling network upgrade (2018-10-28), had a counterfeiting vulnerability. A key-generation process, during evaluation of polynomials related to a to-be-proven statement, produced certain bypass elements. Availability of these elements allowed a cheating prover to bypass a consistency check, and consequently transform the proof of one statement into an ostensibly valid proof of a different statement, thereby breaking the soundness of the proof system. This misled the original Sprout zk-SNARK verifier into accepting the correctness of a transaction.

Aliases

CVE-2019-7167

Aliases

CVE-2019-7167

JSON

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    "description": "Zcash, before the Sapling network upgrade (2018-10-28), had a counterfeiting vulnerability. A key-generation process, during evaluation of polynomials related to a to-be-proven statement, produced certain bypass elements. Availability of these elements allowed a cheating prover to bypass a consistency check, and consequently transform the proof of one statement into an ostensibly valid proof of a different statement, thereby breaking the soundness of the proof system. This misled the original Sprout zk-SNARK verifier into accepting the correctness of a transaction.",
    "id": "GSD-2019-7167"
  },
  "gsd": {
    "metadata": {
      "exploitCode": "unknown",
      "remediation": "unknown",
      "reportConfidence": "confirmed",
      "type": "vulnerability"
    },
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      "id": "GSD-2019-7167",
      "modified": "2023-12-13T01:23:46.594377Z",
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            "accessVector": "NETWORK",
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            "availabilityImpact": "NONE",
            "baseScore": 5.0,
            "confidentialityImpact": "NONE",
            "integrityImpact": "PARTIAL",
            "vectorString": "AV:N/AC:L/Au:N/C:N/I:P/A:N",
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          },
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          "impactScore": 2.9,
          "obtainAllPrivilege": false,
          "obtainOtherPrivilege": false,
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            "scope": "UNCHANGED",
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      "lastModifiedDate": "2020-08-24T17:37Z",
      "publishedDate": "2019-03-27T02:29Z"
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}

CVE-2019-7167 (GCVE-0-2019-7167)

Vulnerability from cvelistv5 – Published: 2019-03-27 01:37 – Updated: 2024-08-04 20:38

Summary

Severity

No CVSS data available.

CWE

Assigner

mitre mitre root

References

3 references

URL	Tags
http://fortune.com/2019/02/05/zcash-vulnerability…	x_refsource_MISC
https://z.cash/blog/zcash-counterfeiting-vulnerab…	x_refsource_MISC
https://github.com/JinBean/CVE-Extension	x_refsource_MISC

Show details on NVD website

JSON

To clipboard

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Sightings

Author	Source	Type	Date	Other

Nomenclature

Seen: The vulnerability was mentioned, discussed, or observed by the user.
Confirmed: The vulnerability has been validated from an analyst's perspective.
Published Proof of Concept: A public proof of concept is available for this vulnerability.
Exploited: The vulnerability was observed as exploited by the user who reported the sighting.
Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
Not exploited: The vulnerability was not observed as exploited by the user who reported the sighting.
Not confirmed: The user expressed doubt about the validity of the vulnerability.
Not patched: The vulnerability was not observed as successfully patched by the user who reported the sighting.

Detection rules are retrieved from Rulezet.

The MITRE ATT&CK techniques below are AI-generated suggestions, inferred from the description of the vulnerability by the CIRCL/vulnerability-attack-technique-classification-roberta-base model, served locally by ML-Gateway. They have not been verified by an analyst and are provided for guidance only.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.

Related by attack behaviour

Vulnerabilities whose description is nearest to this one in the vector space of the CIRCL/vulnerability-attack-technique-biencoder model. This is a similarity search over the bi-encoder space (plain cosine), not a classification, and it has no measured accuracy.