GHSA-jjr8-m8g8-p6wv
Null pointer dereference in TFLite's `Reshape` operator
Packages
| Ecosystem | Package | Affected | Fixed in |
|---|---|---|---|
| PyPI | tensorflow | < 2.1.4 | 2.1.4 |
| PyPI | tensorflow | >= 2.2.0, < 2.2.3 | 2.2.3 |
| PyPI | tensorflow | >= 2.3.0, < 2.3.3 | 2.3.3 |
| PyPI | tensorflow | >= 2.4.0, < 2.4.2 | 2.4.2 |
| PyPI | tensorflow-cpu | < 2.1.4 | 2.1.4 |
| PyPI | tensorflow-cpu | >= 2.2.0, < 2.2.3 | 2.2.3 |
| PyPI | tensorflow-cpu | >= 2.3.0, < 2.3.3 | 2.3.3 |
| PyPI | tensorflow-cpu | >= 2.4.0, < 2.4.2 | 2.4.2 |
| PyPI | tensorflow-gpu | < 2.1.4 | 2.1.4 |
| PyPI | tensorflow-gpu | >= 2.2.0, < 2.2.3 | 2.2.3 |
| PyPI | tensorflow-gpu | >= 2.3.0, < 2.3.3 | 2.3.3 |
| PyPI | tensorflow-gpu | >= 2.4.0, < 2.4.2 | 2.4.2 |
Details
As written in the advisory (Markdown source, shown as plain text).
### 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.
References
External links from the advisory. They lead to third-party sites.
- 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/commit/f8378920345f4f4604202d4ab15ef64b2aceaa16
- 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
- https://github.com/tensorflow/tensorflow
- https://github.com/tensorflow/tensorflow/blob/9c1dc920d8ffb4893d6c9d27d1f039607b326743/tensorflow/lite/core/subgraph.cc#L1062-L1074