TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can cause denial of service in applications serving models using `tf.raw_ops.NonMaxSuppressionV5` by triggering a division by 0. The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/image/non_max_suppression_op.cc#L170-L271) uses a user controlled argument to resize a `std::vector`. However, as `std::vector::resize` takes the size argument as a `size_t` and `output_size` is an `int`, there is an implicit conversion to unsigned. If the attacker supplies a negative value, this conversion results in a crash. A similar issue occurs in `CombinedNonMaxSuppression`. We have patched the issue in GitHub commit 3a7362750d5c372420aa8f0caf7bf5b5c3d0f52d and commit [b5cdbf12ffcaaffecf98f22a6be5a64bb96e4f58. 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 an attacker
    can cause denial of service in applications serving models using `tf.raw_ops.NonMaxSuppressionV5` by
    triggering a division by 0. The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a
    83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/image/non_max_suppression_op.cc#L170-L271) uses a
    user controlled argument to resize a `std::vector`. However, as `std::vector::resize` takes the size
    argument as a `size_t` and `output_size` is an `int`, there is an implicit conversion to unsigned. If the
    attacker supplies a negative value, this conversion results in a crash. A similar issue occurs in
    `CombinedNonMaxSuppression`. We have patched the issue in GitHub commit
    3a7362750d5c372420aa8f0caf7bf5b5c3d0f52d and commit [b5cdbf12ffcaaffecf98f22a6be5a64bb96e4f58. 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-2021-37669)

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
419423	SCA: security update for tensorflow, tensorflow-cpu, tensorflow-gpu (GHSA-vmjw-c2vp-p33c)	Tenable Cloud Security	SCA Checks	Local	medium
419423	SCA: security update for tensorflow, tensorflow-cpu, tensorflow-gpu (GHSA-vmjw-c2vp-p33c)	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-2021-37669

medium

Tenable Plugins

medium

medium

high