TensorFlow is an end-to-end open source platform for machine learning. In affected versions due to incomplete validation in `tf.raw_ops.QuantizeV2`, an attacker can trigger undefined behavior via binding a reference to a null pointer or can access data outside the bounds of heap allocated arrays. The [implementation](https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/quantize_op.cc#L59) has some validation but does not check that `min_range` and `max_range` both have the same non-zero number of elements. If `axis` is provided (i.e., not `-1`), then validation should check that it is a value in range for the rank of `input` tensor and then the lengths of `min_range` and `max_range` inputs match the `axis` dimension of the `input` tensor. We have patched the issue in GitHub commit 6da6620efad397c85493b8f8667b821403516708. 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 due to
    incomplete validation in `tf.raw_ops.QuantizeV2`, an attacker can trigger undefined behavior via binding a
    reference to a null pointer or can access data outside the bounds of heap allocated arrays. The [implement
    ation](https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/c
    ore/kernels/quantize_op.cc#L59) has some validation but does not check that `min_range` and `max_range`
    both have the same non-zero number of elements. If `axis` is provided (i.e., not `-1`), then validation
    should check that it is a value in range for the rank of `input` tensor and then the lengths of
    `min_range` and `max_range` inputs match the `axis` dimension of the `input` tensor. We have patched the
    issue in GitHub commit 6da6620efad397c85493b8f8667b821403516708. 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-2021-37663)

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	Type	Severity
414782	SCA: security update for tensorflow, tensorflow-cpu, tensorflow-gpu (GHSA-g25h-jr74-qp5j)	Tenable Cloud Security	SCA Checks	Local	high
414782	SCA: security update for tensorflow, tensorflow-cpu, tensorflow-gpu (GHSA-g25h-jr74-qp5j)	Tenable Self-Hosted Container Security	SCA Checks	Local	high
162400	openSUSE 15 Security Update : tensorflow2 (openSUSE-SU-2022:10014-1)	Nessus	SuSE Local Security Checks	Local	high

Detections

Analytics

CVE-2021-37663

high

Tenable Plugins

high

high

high