TensorFlow vulnerable to OOB read in `Gather_nd` in TF Lite

NGHIÊM TRỌNG 9.1CVE-2022-35937 · BIT-tensorflow-2022-35937 · GHSA-pxrw-j2fv-hx3h · Công bố 16/09/2022 · cập nhật 17/06/2026

Tóm tắt nhanh

CVE-2022-35937 là lỗ hổng mức NGHIÊM TRỌNG (CVSS 9.1) ảnh hưởng tensorflow, tensorflow-cpu, tensorflow-gpu.
Xác suất bị khai thác trong 30 ngày tới (EPSS): 0.57%, cao hơn 47.2% các lỗ hổng khác.
Cách xử lý: cập nhật lên 2.8.1, 2.9.1, 2.7.2 hoặc mới hơn.

Gói / sản phẩm	Phiên bản bị ảnh hưởng	Phiên bản đã vá
tensorflow PyPI	`≥ 2.8.0, < 2.8.1≥ 2.9.0, < 2.9.1< 2.7.2`	`2.8.12.9.12.7.2`
tensorflow-cpu PyPI	`≥ 2.8.0, < 2.8.1≥ 2.9.0, < 2.9.1< 2.7.2`	`2.8.12.9.12.7.2`
tensorflow-gpu PyPI	`≥ 2.8.0, < 2.8.1≥ 2.9.0, < 2.9.1< 2.7.2`	`2.8.12.9.12.7.2`
tensorflow google	`= 2.10≥ 2.7.0, < 2.7.2≥ 2.8.0, < 2.8.1≥ 2.9.0, < 2.9.1`	`2.7.22.8.12.9.1`

Mô tả

Impact

The GatherNd function takes arguments that determine the sizes of inputs and outputs. If the inputs given are greater than or equal to the sizes of the outputs, an out-of-bounds memory read is triggered.

Patches

We have patched the issue in GitHub commit 595a65a3e224a0362d7e68c2213acfc2b499a196.

The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, 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 Hui Peng from Baidu Security.

Mô tả của NVD

TensorFlow is an open source platform for machine learning. The `GatherNd` function takes arguments that determine the sizes of inputs and outputs. If the inputs given are greater than or equal to the sizes of the outputs, an out-of-bounds memory read is triggered. This issue has been patched in GitHub commit 595a65a3e224a0362d7e68c2213acfc2b499a196. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue.