Описание
Memory corruption in DrawBoundingBoxesV2
Impact
The implementation of tf.raw_ops.MaxPoolGradWithArgmax can cause reads outside of bounds of heap allocated data if attacker supplies specially crafted inputs:
The implementation assumes that the last element of boxes input is 4, as required by the op. Since this is not checked attackers passing values less than 4 can write outside of bounds of heap allocated objects and cause memory corruption:
If the last dimension in boxes is less than 4, accesses similar to tboxes(b, bb, 3) will access data outside of bounds. Further during code execution there are also writes to these indices.
Patches
We have patched the issue in GitHub commit 79865b542f9ffdc9caeb255631f7c56f1d4b6517.
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 for more information regarding the security model and how to contact us with issues and questions.
Attribution
This vulnerability has been reported by Yakun Zhang and Ying Wang of Baidu X-Team.
Ссылки
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-whr9-vfh2-7hm6
- https://nvd.nist.gov/vuln/detail/CVE-2021-29571
- https://github.com/tensorflow/tensorflow/commit/79865b542f9ffdc9caeb255631f7c56f1d4b6517
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-499.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-697.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-208.yaml
Пакеты
tensorflow
< 2.1.4
2.1.4
tensorflow
>= 2.2.0, < 2.2.3
2.2.3
tensorflow
>= 2.3.0, < 2.3.3
2.3.3
tensorflow
>= 2.4.0, < 2.4.2
2.4.2
tensorflow-cpu
< 2.1.4
2.1.4
tensorflow-cpu
>= 2.2.0, < 2.2.3
2.2.3
tensorflow-cpu
>= 2.3.0, < 2.3.3
2.3.3
tensorflow-cpu
>= 2.4.0, < 2.4.2
2.4.2
tensorflow-gpu
< 2.1.4
2.1.4
tensorflow-gpu
>= 2.2.0, < 2.2.3
2.2.3
tensorflow-gpu
>= 2.3.0, < 2.3.3
2.3.3
tensorflow-gpu
>= 2.4.0, < 2.4.2
2.4.2
Связанные уязвимости
TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.MaxPoolGradWithArgmax` can cause reads outside of bounds of heap allocated data if attacker supplies specially crafted inputs. The implementation(https://github.com/tensorflow/tensorflow/blob/31bd5026304677faa8a0b77602c6154171b9aec1/tensorflow/core/kernels/image/draw_bounding_box_op.cc#L116-L130) assumes that the last element of `boxes` input is 4, as required by [the op](https://www.tensorflow.org/api_docs/python/tf/raw_ops/DrawBoundingBoxesV2). Since this is not checked attackers passing values less than 4 can write outside of bounds of heap allocated objects and cause memory corruption. If the last dimension in `boxes` is less than 4, accesses similar to `tboxes(b, bb, 3)` will access data outside of bounds. Further during code execution there are also writes to these indices. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, T
TensorFlow is an end-to-end open source platform for machine learning. ...