TensorFlow is an end-to-end open source platform for machine learning. In affected versions when running shape functions, some functions (such as `MutableHashTableShape`) produce extra output information in the form of a `ShapeAndType` struct. The shapes embedded in this struct are owned by an inference context that is cleaned up almost immediately; if the upstream code attempts to access this shape information, it can trigger a segfault. `ShapeRefiner` is mitigating this for normal output shapes by cloning them (and thus putting the newly created shape under ownership of an inference context that will not die), but we were not doing the same for shapes and types. This commit fixes that by doing similar logic on output shapes and types. We have patched the issue in GitHub commit ee119d4a498979525046fba1c3dd3f13a039fbb1. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.

There are packages installed that are affected by a vulnerability referenced in the following CVE:

  - TensorFlow is an end-to-end open source platform for machine learning. In affected versions when running
    shape functions, some functions (such as `MutableHashTableShape`) produce extra output information in the
    form of a `ShapeAndType` struct. The shapes embedded in this struct are owned by an inference context that
    is cleaned up almost immediately; if the upstream code attempts to access this shape information, it can
    trigger a segfault. `ShapeRefiner` is mitigating this for normal output shapes by cloning them (and thus
    putting the newly created shape under ownership of an inference context that will not die), but we were
    not doing the same for shapes and types. This commit fixes that by doing similar logic on output shapes
    and types. We have patched the issue in GitHub commit ee119d4a498979525046fba1c3dd3f13a039fbb1. The fix
    will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow
    2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. (CVE-2021-37690)

The remote openSUSE 15 host has packages installed that are affected by multiple vulnerabilities as referenced in the openSUSE-SU-2022:10014-1 advisory.

    Update to TF2 2.6.0 which fixes multiple CVEs (boo#1189423).

    - Introduction of bazel6.3 and basel-skylib1.0.3 as build       dependencies.
      The latter has been adapted to all a version in its package       name (if %set_ver_suffix is set to 1). This allows multiple       versions to exist for one product (not installed).
      NOTE: basel-skylib1.0.3 does not exist in oS:Factory:
      basel-skylib in oS:Factory - the base version - is 1.0.3.

Tenable has extracted the preceding description block directly from the SUSE security advisory.

Note that Nessus has not tested for these issues but has instead relied only on the application's self-reported version number.

ID	Name	Product	Family	Severity
409560	SCA: security update for tensorflow, tensorflow-cpu, tensorflow-gpu (GHSA-3hxh-8cp2-g4hg)	Tenable Cloud Security	SCA Checks	medium
409560	SCA: security update for tensorflow, tensorflow-cpu, tensorflow-gpu (GHSA-3hxh-8cp2-g4hg)	Tenable Self-Hosted Container Security	SCA Checks	medium
162400	openSUSE 15 Security Update : tensorflow2 (openSUSE-SU-2022:10014-1)	Nessus	SuSE Local Security Checks	high

Detections

Analytics

CVE-2021-37690

medium

Tenable Plugins

medium

medium

high