2021-37677 | Google TensorFlow tf.raw_ops.Dequantize denial of service
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A vulnerability has been found in Google TensorFlow up to 2.3.3/2.4.2/2.5.0 (Artificial Intelligence Software) and classified as problematic. Affected by this vulnerability is the function
tf.raw_ops.Dequantize. The manipulation with an unknown input leads to a denial of service vulnerability. The CWE definition for the vulnerability is CWE-404. As an impact it is known to affect availability. The summary by CVE is:
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.
The weakness was disclosed 08/13/2021. It is possible to read the advisory at github.com. This vulnerability is known as CVE-2021-37677 since 07/29/2021. The exploitation appears to be easy. The attack can be launched remotely. The exploitation doesn’t need any form of authentication. It demands that the victim is doing some kind of user interaction. Technical details of the vulnerability are known, but there is no available exploit. The pricing for an exploit might be around USD $0-$5k at the moment (estimation calculated on 08/18/2021). The attack technique deployed by this issue is T1499 according to MITRE ATT&CK.
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.
VulDB Meta Base Score: 4.3
VulDB Meta Temp Score: 4.1
Status: Not defined
0-Day Time: 🔒
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