BIT-tensorflow-2022-35973

    Dashboard / Vulnerabilities / BIT-tensorflow-2022-35973

    BIT-tensorflow-2022-35973

    Published: 6 Mar 2024Last Modified: 8 Sept 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/tensorflow/tensorflow/commit/aca766ac7693bf29ed0df55ad6bfcc78f35e7f48, https://github.com/tensorflow/tensorflow/security/advisories/GHSA-689c-r7h2-fv9v, https://nvd.nist.gov/vuln/detail/CVE-2022-35973

    Affected packages

    Package

    Name: tensorflow

    Purl: pkg:bitnami/tensorflow

    Affected ranges

    Type: SEMVER

    Events:

    Introduced- 2.7.0
    Fixed -2.7.2

    Affected versions

    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
    BIT-tensorflow-2022-35973 | CVE-DB