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CVE-2021-37677

TensorFlow is an end-to-end open source platform for machine learning. In affected versions the shape inference code for `tf.raw_ops.Dequantize` has a vulnerability that could trigger a denial of service via a segfault if an attacker provides invalid arguments. The shape inference [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/ops/array_ops.cc#L2999-L3014) uses `axis` to select between two different values for `minmax_rank` which is then used to retrieve tensor dimensions. However, code assumes that `axis` can be either `-1` or a value greater than `-1`, with no validation for the other values. We have patched the issue in GitHub commit da857cfa0fde8f79ad0afdbc94e88b5d4bbec764. 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.

Published

2021-08-12T23:15:08.090

Last Modified

2024-11-21T06:15:40.763

Status

Modified

Source

[email protected]

Severity

CVSSv3.1: 5.5 (MEDIUM)

CVSSv2 Vector

AV:L/AC:L/Au:N/C:N/I:N/A:P

  • Access Vector: LOCAL
  • Access Complexity: LOW
  • Authentication: NONE
  • Confidentiality Impact: NONE
  • Integrity Impact: NONE
  • Availability Impact: PARTIAL
Exploitability Score

3.9

Impact Score

2.9

Weaknesses
  • Type: Secondary
    CWE-20
  • Type: Primary
    CWE-1284
Affected Vendors & Products
Type Vendor Product Version/Range Vulnerable?
Application google tensorflow < 2.3.4 Yes
Application google tensorflow < 2.4.3 Yes
Application google tensorflow 2.5.0 Yes
Application google tensorflow 2.6.0 Yes
Application google tensorflow 2.6.0 Yes
Application google tensorflow 2.6.0 Yes
References