2021-37650 | Google TensorFlow tf.raw_ops.DatasetToTFRecord buffer overflow


CVSS Meta Temp Score Current Exploit Price (≈) CTI Interest Score
6.0 $5k-$25k 0.53

A vulnerability was found in Google TensorFlow up to 2.3.3/2.4.2/2.5.0 (Artificial Intelligence Software) and classified as critical. This issue affects the function tf.raw_ops.ExperimentalDatasetToTFRecord/tf.raw_ops.DatasetToTFRecord. The manipulation with an unknown input leads to a memory corruption vulnerability. Using CWE to declare the problem leads to CWE-120. Impacted is confidentiality, integrity, and availability. The summary by CVE is:

TensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementation for `tf.raw_ops.ExperimentalDatasetToTFRecord` and `tf.raw_ops.DatasetToTFRecord` can trigger heap buffer overflow and segmentation fault. The [implementation](https://github.com/tensorflow/tensorflow/blob/f24faa153ad31a4b51578f8181d3aaab77a1ddeb/tensorflow/core/kernels/data/experimental/to_tf_record_op.cc#L93-L102) assumes that all records in the dataset are of string type. However, there is no check for that, and the example given above uses numeric types. We have patched the issue in GitHub commit e0b6e58c328059829c3eb968136f17aa72b6c876. 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.

The weakness was released 08/13/2021. The advisory is shared at github.com. The identification of this vulnerability is CVE-2021-37650 since 07/29/2021. The exploitation is known to be easy. The attack may be initiated remotely. No form of authentication is needed for a successful exploitation. It demands that the victim is doing some kind of user interaction. Technical details are known, but no exploit is available. The price for an exploit might be around USD $5k-$25k at the moment (estimation calculated on 08/17/2021).

Upgrading to version 2.3.4, 2.4.3, 2.5.1 or 2.6.0 eliminates this vulnerability. Applying a patch is able to eliminate this problem. The bugfix is ready for download at github.com. The best possible mitigation is suggested to be upgrading to the latest version.

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Vendor

Name

VulDB Meta Base Score: 6.3
VulDB Meta Temp Score: 6.0

VulDB Base Score: 6.3
VulDB Temp Score: 6.0
VulDB Vector: 🔒
VulDB Reliability: 🔍

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VulDB Base Score: 🔒
VulDB Temp Score: 🔒
VulDB Reliability: 🔍
Class: Memory corruption
CWE: CWE-120
ATT&CK: Unknown

Local: No
Remote: Yes

Availability: 🔒
Status: Not defined

Price Prediction: 🔍
Current Price Estimation: 🔒


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Threat Intelligenceinfoedit

Interest: 🔍
Active Actors: 🔍
Active APT Groups: 🔍Recommended: Upgrade
Status: 🔍

0-Day Time: 🔒

Upgrade: TensorFlow 2.3.4/2.4.3/2.5.1/2.6.0
Patch: github.com

07/29/2021 CVE assigned
08/13/2021 +15 days Advisory disclosed
08/13/2021 +0 days VulDB entry created
08/17/2021 +4 days VulDB last updateVendor: https://www.google.com/

Advisory: github.com
Status: Confirmed
Confirmation: 🔒

CVE: CVE-2021-37650 (🔒)

Created: 08/13/2021 07:16
Updated: 08/17/2021 17:31
Changes: (1) source_cve_cna
Complete: 🔍

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