TensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementation of `tf.raw_ops.StringNGrams` is vulnerable to an integer overflow issue caused by converting a signed integer value to an unsigned one and then allocating memory based on this value. The [implementation](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/string_ngrams_op.cc#L184) calls `reserve` on a `tstring` with a value that sometimes can be negative if user supplies negative `ngram_widths`. The `reserve` method calls `TF_TString_Reserve` which has an `unsigned long` argument for the size of the buffer. Hence, the implicit conversion transforms the negative value to a large integer. We have patched the issue in GitHub commit c283e542a3f422420cfdb332414543b62fc4e4a5. 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 the
    implementation of `tf.raw_ops.StringNGrams` is vulnerable to an integer overflow issue caused by
    converting a signed integer value to an unsigned one and then allocating memory based on this value. The [
    implementation](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/ten
    sorflow/core/kernels/string_ngrams_op.cc#L184) calls `reserve` on a `tstring` with a value that sometimes
    can be negative if user supplies negative `ngram_widths`. The `reserve` method calls `TF_TString_Reserve`
    which has an `unsigned long` argument for the size of the buffer. Hence, the implicit conversion
    transforms the negative value to a large integer. We have patched the issue in GitHub commit
    c283e542a3f422420cfdb332414543b62fc4e4a5. 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-2021-37646)

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
415526	SCA: security update for tensorflow, tensorflow-cpu, tensorflow-gpu (GHSA-h6jh-7gv5-28vg)	Tenable Cloud Security	SCA Checks	Local	medium
415526	SCA: security update for tensorflow, tensorflow-cpu, tensorflow-gpu (GHSA-h6jh-7gv5-28vg)	Tenable Self-Hosted Container Security	SCA Checks	Local	medium
162400	openSUSE 15 Security Update : tensorflow2 (openSUSE-SU-2022:10014-1)	Nessus	SuSE Local Security Checks	Local	high

Detections

Analytics

CVE-2021-37646

medium

Tenable Plugins

medium

medium

high