CVE-2020-26271: Heap out of bounds access in MakeEdge in TensorFlow
First published: Thu Dec 10 2020(Updated: )
### Impact Under certain cases, loading a saved model can result in accessing uninitialized memory while building the computation graph. The [`MakeEdge` function](https://github.com/tensorflow/tensorflow/blob/3616708cb866365301d8e67b43b32b46d94b08a0/tensorflow/core/common_runtime/graph_constructor.cc#L1426-L1438) creates an edge between one output tensor of the `src` node (given by `output_index`) and the input slot of the `dst` node (given by `input_index`). This is only possible if the types of the tensors on both sides coincide, so the function begins by obtaining the corresponding `DataType` values and comparing these for equality: ```cc DataType src_out = src->output_type(output_index); DataType dst_in = dst->input_type(input_index); //... ``` However, there is no check that the indices point to inside of the arrays they index into. Thus, this can result in accessing data out of bounds of the corresponding heap allocated arrays. In most scenarios, this can manifest as unitialized data access, but if the index points far away from the boundaries of the arrays this can be used to leak addresses from the library. ### Patches We have patched the issue in GitHub commit [0cc38aaa4064fd9e79101994ce9872c6d91f816b](https://github.com/tensorflow/tensorflow/commit/0cc38aaa4064fd9e79101994ce9872c6d91f816b) and will release TensorFlow 2.4.0 containing the patch. TensorFlow nightly packages after this commit will also have the issue resolved. Since this issue also impacts TF versions before 2.4, we will patch all releases between 1.15 and 2.3 inclusive. ### For more information Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.
Credit: security-advisories@github.com security-advisories@github.com
| Affected Software | Affected Version | How to fix |
|---|---|---|
| pip/tensorflow-gpu | >=2.3.0<2.3.2 | 2.3.2 |
| pip/tensorflow-gpu | >=2.2.0<2.2.2 | 2.2.2 |
| pip/tensorflow-gpu | >=2.1.0<2.1.3 | 2.1.3 |
| pip/tensorflow-gpu | >=2.0.0<2.0.4 | 2.0.4 |
| pip/tensorflow-gpu | <1.15.5 | 1.15.5 |
| pip/tensorflow-cpu | >=2.3.0<2.3.2 | 2.3.2 |
| pip/tensorflow-cpu | >=2.2.0<2.2.2 | 2.2.2 |
| pip/tensorflow-cpu | >=2.1.0<2.1.3 | 2.1.3 |
| pip/tensorflow-cpu | >=2.0.0<2.0.4 | 2.0.4 |
| pip/tensorflow-cpu | <1.15.5 | 1.15.5 |
| pip/tensorflow | >=2.3.0<2.3.2 | 2.3.2 |
| pip/tensorflow | >=2.2.0<2.2.2 | 2.2.2 |
| pip/tensorflow | >=2.1.0<2.1.3 | 2.1.3 |
| pip/tensorflow | >=2.0.0<2.0.4 | 2.0.4 |
| pip/tensorflow | <1.15.5 | 1.15.5 |
| TensorFlow Keras | <1.15.5 | |
| TensorFlow Keras | >=2.0.0<2.0.4 | |
| TensorFlow Keras | >=2.1.0<2.1.3 | |
| TensorFlow Keras | >=2.2.0<2.2.2 | |
| TensorFlow Keras | >=2.3.0<2.3.2 |
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Reference Links
- https://github.com/tensorflow/tensorflow/commit/0cc38aaa4064fd9e79101994ce9872c6d91f816b
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-q263-fvxm-m5mw
- https://nvd.nist.gov/vuln/detail/CVE-2020-26271
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2020-302.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2020-337.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2020-257.yaml
- https://github.com/advisories/GHSA-q263-fvxm-m5mw (Advisory)
Frequently Asked Questions
What is the severity of CVE-2020-26271?
CVE-2020-26271 has a severity level that can lead to accessing uninitialized memory, which might result in system instability or data exposure.
How do I fix CVE-2020-26271?
To remediate CVE-2020-26271, upgrade TensorFlow to version 2.3.2 or later, or to version 1.15.5 if using an older version.
Which versions of TensorFlow are affected by CVE-2020-26271?
CVE-2020-26271 affects TensorFlow versions up to 1.15.5 and versions from 2.0.0 to 2.3.1.
Is CVE-2020-26271 related to TensorFlow GPU or CPU installations?
Yes, CVE-2020-26271 affects both TensorFlow GPU and CPU installations across the specified vulnerable versions.
What kind of impact does CVE-2020-26271 have on applications?
The impact of CVE-2020-26271 may lead to unexpected behavior or crashes in applications using affected versions of TensorFlow.
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