TensorFlow is an end-to-end open source platform for machine learning. In affected versions it is possible to nest a `tf.map_fn` within another `tf.map_fn` call. However, if the input tensor is a `RaggedTensor` and there is no function signature provided, code assumes the output is a fully specified tensor and fills output buffer with uninitialized contents from the heap. The `t` and `z` outputs should be identical, however this is not the case. The last row of `t` contains data from the heap which can be used to leak other memory information. The bug lies in the conversion from a `Variant` tensor to a `RaggedTensor`. The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/ragged_tensor_from_variant_op.cc#L177-L190) does not check that all inner shapes match and this results in the additional dimensions. The same implementation can result in data loss, if input tensor is tweaked. We have patched the issue in GitHub commit 4e2565483d0ffcadc719bd44893fb7f609bb5f12. 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 it is possible
    to nest a `tf.map_fn` within another `tf.map_fn` call. However, if the input tensor is a `RaggedTensor`
    and there is no function signature provided, code assumes the output is a fully specified tensor and fills
    output buffer with uninitialized contents from the heap. The `t` and `z` outputs should be identical,
    however this is not the case. The last row of `t` contains data from the heap which can be used to leak
    other memory information. The bug lies in the conversion from a `Variant` tensor to a `RaggedTensor`. The
    [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/te
    nsorflow/core/kernels/ragged_tensor_from_variant_op.cc#L177-L190) does not check that all inner shapes
    match and this results in the additional dimensions. The same implementation can result in data loss, if
    input tensor is tweaked. We have patched the issue in GitHub commit
    4e2565483d0ffcadc719bd44893fb7f609bb5f12. 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-2021-37679)

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
415006	SCA: security update for tensorflow, tensorflow-cpu, tensorflow-gpu (GHSA-g8wg-cjwc-xhhp)	Tenable Cloud Security	SCA Checks	Local	high
415006	SCA: security update for tensorflow, tensorflow-cpu, tensorflow-gpu (GHSA-g8wg-cjwc-xhhp)	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-2021-37679

high

Tenable Plugins

high

high

high