CVE-2021-37689 Null pointer dereference in TensorFlow Lite MLIR optimizations
An attacker can cause a denial of service by crafting a malicious TFLite model that triggers a null pointer dereference in MLIR optimizations.
- Exploited in the wild
- Not known
- Login needed
- Any signed-in user
- Attackable
- Local or same network only
- Victim action needed
- Not needed
- Fix
- Available
What is CVE-2021-37689?
CVE-2021-37689 is a high-severity vulnerability in Google Tensorflow, published on 12 Aug 2021. It can only be exploited from the same machine or local network, with no action from the victim.
Official description
TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can craft a TFLite model that would trigger a null pointer dereference, which would result in a crash and denial of service. This is caused by the MLIR optimization of `L2NormalizeReduceAxis` operator. The [implementation](https://github.com/tensorflow/tensorflow/blob/149562d49faa709ea80df1d99fc41d005b81082a/tensorflow/compiler/mlir/lite/transforms/optimize.cc#L67-L70) unconditionally dereferences a pointer to an iterator to a vector without checking that the vector has elements. We have patched the issue in GitHub commit d6b57f461b39fd1aa8c1b870f1b974aac3554955. 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.
Weakness: CWE-476
Which versions are affected by CVE-2021-37689?
22 open source packages ship an affected version, including tensorflow, tensorflow-cpu and tensorflow-gpu. It affects 1 product.
| Package | Affected | Fixed in |
|---|---|---|
tensorflow Bitnami | From 2.3.0 | 2.3.4, 2.4.3, 2.5.1 |
tensorflow PyPI | All versions | 2.3.4 |
tensorflow PyPI | All versions | 2.3.4 |
tensorflow PyPI | From 2.3.0 | 2.3.4, 2.4.3 |
tensorflow PyPI | From 2.4.0 | 2.4.3 |
tensorflow PyPI | From 2.4.0 | 2.4.3 |
16 more packages
tensorflow PyPI | From 2.5.0 | 2.5.1 |
tensorflow PyPI | From 2.5.0 | 2.5.1 |
tensorflow-cpu PyPI | All versions | 2.3.4 |
tensorflow-cpu PyPI | All versions | 2.3.4 |
tensorflow-cpu PyPI | From 2.3.0 | 2.3.4, 2.4.3 |
tensorflow-cpu PyPI | From 2.4.0 | 2.4.3 |
tensorflow-cpu PyPI | From 2.4.0 | 2.4.3 |
tensorflow-cpu PyPI | From 2.5.0 | 2.5.1 |
tensorflow-cpu PyPI | From 2.5.0 | 2.5.1 |
tensorflow-gpu PyPI | All versions | 2.3.4 |
tensorflow-gpu PyPI | All versions | 2.3.4 |
tensorflow-gpu PyPI | From 2.3.0 | 2.3.4, 2.4.3 |
tensorflow-gpu PyPI | From 2.4.0 | 2.4.3 |
tensorflow-gpu PyPI | From 2.4.0 | 2.4.3 |
tensorflow-gpu PyPI | From 2.5.0 | 2.5.1 |
tensorflow-gpu PyPI | From 2.5.0 | 2.5.1 |
Products include Google Tensorflow.
How do I fix CVE-2021-37689?
Upgrade tensorflow to a fixed version for your release line: 2.3.4, 2.4.3 or 2.5.1.
Is CVE-2021-37689 being exploited?
There is no known exploitation of CVE-2021-37689 in the wild and no public exploit code.
Other vulnerabilities in Google Tensorflow
- CVE-2023-25668 TensorFlow vulnerable to heap out-of-buffer read in the QuantizeAndDequantize operation
- CVE-2018-7575 Integer Overflow or Wraparound in Google TensorFlow
- CVE-2021-37678 Arbitrary code execution due to YAML deserialization
- CVE-2021-35958 Path traversal in n/a
- CVE-2020-15202 Integer truncation in Shard API usage
Sources
Also known as BIT-tensorflow-2021-37689, GHSA-wf5p-c75w-w3wh, PYSEC-2021-311, PYSEC-2021-602, PYSEC-2021-800.