CVE-2021-37669: Crash in NMS ops caused by integer conversion to unsigned in TensorFlow
Impact An attacker can cause denial of service in applications serving models using tf.rawops.NonMaxSuppressionV5 by triggering a division by 0:
python import tensorflow as tf
tf.rawops.NonMaxSuppressionV5( boxes=[[0.1,0.1,0.1,0.1],[0.2,0.2,0.2,0.2],[0.3,0.3,0.3,0.3]], scores=[1.0,2.0,3.0], maxoutputsize=-1, iouthreshold=0.5, scorethreshold=0.5, softnmssigma=1.0, padtomaxoutputsize=True) The implementation uses a user controlled argument to resize a std::vector:
cc const int outputsize = maxoutputsize.scalar<int>()(); // ... std::vector<int> selected; // ... if (padtomaxoutputsize) { selected.resize(outputsize, 0); // ... } However, as std::vector::resize takes the size argument as a sizet and outputsize is an int, there is an implicit conversion to usigned. If the attacker supplies a negative value, this conversion results in a crash.
A similar issue occurs in CombinedNonMaxSuppression:
python import tensorflow as tf
tf.rawops.NonMaxSuppressionV5( boxes=[[[[0.1,0.1,0.1,0.1],[0.2,0.2,0.2,0.2],[0.3,0.3,0.3,0.3]],[[0.1,0.1,0.1,0.1],[0.2,0.2,0.2,0.2],[0.3,0.3,0.3,0.3]],[[0.1,0.1,0.1,0.1],[0.2,0.2,0.2,0.2],[0.3,0.3,0.3,0.3]]]], scores=[[[1.0,2.0,3.0],[1.0,2.0,3.0],[1.0,2.0,3.0]]], maxoutputsizeperclass=-1, maxtotalsize=10, iouthreshold=scorethreshold=0.5, padperclass=True, clipboxes=True) Patches 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.
For more information Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.
Attribution This vulnerability has been reported by members of the Aivul Team from Qihoo 360.
Other sources
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.rawops.NonMaxSuppressionV5 by triggering a division by 0. The implementation uses a user controlled argument to resize a std::vector. However, as std::vector::resize takes the size argument as a sizet and outputsize 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.
Affected Software
Remediation
Recommended actions to resolve this vulnerability, in priority order.
- Upgrade
Upgrade
pip/tensorflow-gputo a version that resolves this vulnerability.Fixed in 2.5.1 - Upgrade
Upgrade
pip/tensorflow-gputo a version that resolves this vulnerability.Fixed in 2.4.3 - Upgrade
Upgrade
pip/tensorflow-gputo a version that resolves this vulnerability.Fixed in 2.3.4 - Upgrade
Upgrade
pip/tensorflow-cputo a version that resolves this vulnerability.Fixed in 2.5.1 - Upgrade
Upgrade
pip/tensorflow-cputo a version that resolves this vulnerability.Fixed in 2.4.3 - Upgrade
Upgrade
pip/tensorflow-cputo a version that resolves this vulnerability.Fixed in 2.3.4 - Upgrade
Upgrade
pip/tensorflowto a version that resolves this vulnerability.Fixed in 2.5.1 - Upgrade
Upgrade
pip/tensorflowto a version that resolves this vulnerability.Fixed in 2.4.3 - Upgrade
Upgrade
pip/tensorflowto a version that resolves this vulnerability.Fixed in 2.3.4 - Upgrade
Upgrade
tensorflowto a version that resolves this vulnerability.Patch 3a7362750d5c372420aa8f0caf7bf5b5c3d0f52d - Upgrade
Upgrade
tensorflowto a version that resolves this vulnerability.Patch b5cdbf12ffcaaffecf98f22a6be5a64bb96e4f58 - Upgrade
Upgrade
tensorflowto a version that resolves this vulnerability.Fixed in 2.6.0 - Upgrade
Upgrade
tensorflowto a version that resolves this vulnerability.Fixed in 2.5.1 - Upgrade
Upgrade
tensorflowto a version that resolves this vulnerability.Fixed in 2.4.3 - Upgrade
Upgrade
tensorflowto a version that resolves this vulnerability.Fixed in 2.3.4 - Compensating control
For applications serving models using tf.raw_ops.NonMaxSuppressionV5, do not allow user-controlled max_output_size/max_output_size_per_class values to be negative (negative values can trigger the crash due to implicit conversion when resizing a std::vector).
Event History
Frequently Asked Questions
What is the severity of CVE-2021-37669?
CVE-2021-37669 has a high severity rating due to its potential to cause denial of service.
How do I fix CVE-2021-37669?
To fix CVE-2021-37669, upgrade to TensorFlow version 2.5.1 or later.
Which versions of TensorFlow are affected by CVE-2021-37669?
CVE-2021-37669 affects TensorFlow versions from 2.3.0 to 2.6.0-rc2.
What causes the denial of service in CVE-2021-37669?
The denial of service in CVE-2021-37669 is caused by a division by zero triggered in certain TensorFlow operations.
Is CVE-2021-37669 related to any specific TensorFlow operations?
CVE-2021-37669 is specifically related to the `tf.raw_ops.NonMaxSuppressionV5` operation in TensorFlow.