CVE-2022-35973

    Dashboard / Vulnerabilities / CVE-2022-35973

    CVE-2022-35973

    Published: 16 Sept 2022Last Modified: 12 Aug 2026

    Summary: Segfault in `QuantizedMatMul` in TensorFlow

    Details: TensorFlow is an open source platform for machine learning. If `QuantizedMatMul` is given nonscalar input for: `min_a`, `max_a`, `min_b`, or `max_b` It gives a segfault that can be used to trigger a denial of service attack. We have patched the issue in GitHub commit aca766ac7693bf29ed0df55ad6bfcc78f35e7f48. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue.

    References: https://github.com/CVEProject/cvelistV5/tree/main/cves/2022/35xxx/CVE-2022-35973.json, https://github.com/tensorflow/tensorflow/security/advisories/GHSA-689c-r7h2-fv9v, https://nvd.nist.gov/vuln/detail/CVE-2022-35973, https://github.com/tensorflow/tensorflow/commit/aca766ac7693bf29ed0df55ad6bfcc78f35e7f48

    Affected packages

    Package

    Name:

    Purl:

    Affected ranges

    Type: GIT

    Events:

    Introduced- c256c071bb26e1e13b4666d1b3e229e110bc914a
    Fixed -dd7b8a3c1714d0052ce4b4a2fd8dcef927439a24

    Affected versions

    v2.9.0

    Common Vulnerability Scoring System

    Attack Vector
    Network
    Adjacent
    Local
    Physical
    Privileges Required
    None
    Low
    High
    User Interaction
    None
    Required
    Scope
    Unchanged
    Changed
    Confidentiality
    None
    Low
    High
    Integrity
    None
    Low
    High
    Availability
    None
    Low
    High
    CVE-2022-35973 | CVE-DB