CVE-2021-29592: Null pointer dereference in TFLite's `Reshape` operator
First published: Fri May 14 2021(Updated: )
### Impact The fix for [CVE-2020-15209](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15209) missed the case when the target shape of `Reshape` operator is given by the elements of a 1-D tensor. As such, the [fix for the vulnerability](https://github.com/tensorflow/tensorflow/blob/9c1dc920d8ffb4893d6c9d27d1f039607b326743/tensorflow/lite/core/subgraph.cc#L1062-L1074) allowed passing a null-buffer-backed tensor with a 1D shape: ```cc if (tensor->data.raw == nullptr && tensor->bytes > 0) { if (registration.builtin_code == kTfLiteBuiltinReshape && i == 1) { // In general, having a tensor here with no buffer will be an error. // However, for the reshape operator, the second input tensor is only // used for the shape, not for the data. Thus, null buffer is ok. continue; } else { // In all other cases, we need to return an error as otherwise we will // trigger a null pointer dereference (likely). ReportError("Input tensor %d lacks data", tensor_index); return kTfLiteError; } } ``` ### Patches We have patched the issue in GitHub commit [f8378920345f4f4604202d4ab15ef64b2aceaa16](https://github.com/tensorflow/tensorflow/commit/f8378920345f4f4604202d4ab15ef64b2aceaa16). The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. ### 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. ### Attribution This vulnerability has been reported by members of the Aivul Team from Qihoo 360.
Credit: security-advisories@github.com security-advisories@github.com
| Affected Software | Affected Version | How to fix |
|---|---|---|
| pip/tensorflow-gpu | >=2.4.0<2.4.2 | 2.4.2 |
| pip/tensorflow-gpu | >=2.3.0<2.3.3 | 2.3.3 |
| pip/tensorflow-gpu | >=2.2.0<2.2.3 | 2.2.3 |
| pip/tensorflow-gpu | <2.1.4 | 2.1.4 |
| pip/tensorflow-cpu | >=2.4.0<2.4.2 | 2.4.2 |
| pip/tensorflow-cpu | >=2.3.0<2.3.3 | 2.3.3 |
| pip/tensorflow-cpu | >=2.2.0<2.2.3 | 2.2.3 |
| pip/tensorflow-cpu | <2.1.4 | 2.1.4 |
| pip/tensorflow | >=2.4.0<2.4.2 | 2.4.2 |
| pip/tensorflow | >=2.3.0<2.3.3 | 2.3.3 |
| pip/tensorflow | >=2.2.0<2.2.3 | 2.2.3 |
| pip/tensorflow | <2.1.4 | 2.1.4 |
| TensorFlow Keras | <2.1.4 | |
| TensorFlow Keras | >=2.2.0<2.2.3 | |
| TensorFlow Keras | >=2.3.0<2.3.3 | |
| TensorFlow Keras | >=2.4.0<2.4.2 |
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Reference Links
- https://github.com/tensorflow/tensorflow/commit/f8378920345f4f4604202d4ab15ef64b2aceaa16
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-jjr8-m8g8-p6wv
- https://nvd.nist.gov/vuln/detail/CVE-2021-29592
- https://github.com/tensorflow/tensorflow/blob/9c1dc920d8ffb4893d6c9d27d1f039607b326743/tensorflow/lite/core/subgraph.cc#L1062-L1074
- https://github.com/advisories/GHSA-jjr8-m8g8-p6wv (Advisory)
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-520.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-718.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-229.yaml
Frequently Asked Questions
What is the severity of CVE-2021-29592?
CVE-2021-29592 has a medium severity level as it can potentially cause unintended behavior in the TensorFlow library.
How do I fix CVE-2021-29592?
To fix CVE-2021-29592, upgrade TensorFlow to version 2.4.2, 2.3.3, 2.2.3, or 2.1.4.
Which versions of TensorFlow are affected by CVE-2021-29592?
CVE-2021-29592 affects TensorFlow versions prior to 2.1.4, between 2.2.0 and 2.2.3, between 2.3.0 and 2.3.3, and between 2.4.0 and 2.4.2.
What components are affected by CVE-2021-29592?
CVE-2021-29592 affects the Reshape operator in the TensorFlow library's API.
Is CVE-2021-29592 a local or remote vulnerability?
CVE-2021-29592 is primarily considered a local vulnerability as it affects the functioning of the TensorFlow API when used incorrectly.
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