CVE-2020-26266 Uninitialized memory access in Eigen types in TensorFlow
Attackers can trigger use of uninitialized memory in TensorFlow via saved models, potentially leading to code execution or crashes.
- 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-2020-26266?
CVE-2020-26266 is a medium-severity memory corruption vulnerability in Google Tensorflow, published on 10 Dec 2020. It can only be exploited from the same machine or local network, with no action from the victim.
Official description
In affected versions of TensorFlow under certain cases a saved model can trigger use of uninitialized values during code execution. This is caused by having tensor buffers be filled with the default value of the type but forgetting to default initialize the quantized floating point types in Eigen. This is fixed in versions 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2, and 2.4.0.
Weakness: CWE-908
Which versions are affected by CVE-2020-26266?
34 open source packages ship an affected version, including tensorflow, tensorflow-cpu and tensorflow-gpu. It affects 1 product.
| Package | Affected | Fixed in |
|---|---|---|
tensorflow Bitnami | All versions | 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2 |
tensorflow PyPI | All versions | 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2 |
tensorflow PyPI | All versions | 1.15.5 |
tensorflow PyPI | All versions | 1.15.5 |
tensorflow PyPI | From 2.0.0 | 2.0.4 |
tensorflow PyPI | From 2.0.0 | 2.0.4 |
28 more packages
tensorflow PyPI | From 2.1.0 | 2.1.3 |
tensorflow PyPI | From 2.1.0 | 2.1.3 |
tensorflow PyPI | From 2.2.0 | 2.2.2 |
tensorflow PyPI | From 2.2.0 | 2.2.2 |
tensorflow PyPI | From 2.3.0 | 2.3.2 |
tensorflow PyPI | From 2.3.0 | 2.3.2 |
tensorflow-cpu PyPI | All versions | 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2 |
tensorflow-cpu PyPI | All versions | 1.15.5 |
tensorflow-cpu PyPI | All versions | 1.15.5 |
tensorflow-cpu PyPI | From 2.0.0 | 2.0.4 |
tensorflow-cpu PyPI | From 2.0.0 | 2.0.4 |
tensorflow-cpu PyPI | From 2.1.0 | 2.1.3 |
tensorflow-cpu PyPI | From 2.1.0 | 2.1.3 |
tensorflow-cpu PyPI | From 2.2.0 | 2.2.2 |
tensorflow-cpu PyPI | From 2.2.0 | 2.2.2 |
tensorflow-cpu PyPI | From 2.3.0 | 2.3.2 |
tensorflow-cpu PyPI | From 2.3.0 | 2.3.2 |
tensorflow-gpu PyPI | All versions | 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2 |
tensorflow-gpu PyPI | All versions | 1.15.5 |
tensorflow-gpu PyPI | All versions | 1.15.5 |
tensorflow-gpu PyPI | From 2.0.0 | 2.0.4 |
tensorflow-gpu PyPI | From 2.0.0 | 2.0.4 |
tensorflow-gpu PyPI | From 2.1.0 | 2.1.3 |
tensorflow-gpu PyPI | From 2.1.0 | 2.1.3 |
tensorflow-gpu PyPI | From 2.2.0 | 2.2.2 |
tensorflow-gpu PyPI | From 2.2.0 | 2.2.2 |
tensorflow-gpu PyPI | From 2.3.0 | 2.3.2 |
tensorflow-gpu PyPI | From 2.3.0 | 2.3.2 |
Products include Google Tensorflow.
How do I fix CVE-2020-26266?
Upgrade tensorflow to a fixed version for your release line: 1.15.5, 2.0.4, 2.1.3 or 2.2.2.
Upgrade to the fixed version. Memory-safe languages, compiler hardening and fuzzing prevent most new cases. More about Memory corruption.
Is CVE-2020-26266 being exploited?
There is no known exploitation of CVE-2020-26266 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-2020-26266, GHSA-qhxx-j73r-qpm2, PYSEC-2020-254, PYSEC-2020-297, PYSEC-2020-332.