https://cve.radiocsirt.org/rss/recent/all/10 Most recent entries from all 2026-10-09T07:27:40.133222+00:00 Vulnerability-Lookup csirt@opendfir.org python-feedgen Contains only the most 10 recent entries. https://cve.radiocsirt.org/vuln/bit-tensorflow-2021-29526 BIT-tensorflow-2021-29526 — Division by 0 in `Conv2D` 2026-10-09T07:27:40.204082+00:00

Affected: Bitnami: tensorflow

TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a division by 0 in `tf.raw_ops.Conv2D`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/988087bd83f144af14087fe4fecee2d250d93737/tensorflow/core/kernels/conv_ops.cc#L261-L263) does a division by a quantity that is controlled by the caller. 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.

 https://cve.radiocsirt.org/vuln/cnvd-2021-36566 cnvd-2021-36566 2026-10-09T07:27:40.204145+00:00 cnvd-2021-36566 https://cve.radiocsirt.org/vuln/euvd-2026-26451 EUVD-2026-26451 2026-10-09T07:27:40.204162+00:00 EUVD-2026-26451 https://cve.radiocsirt.org/vuln/fkie_cve-2021-29526 fkie_cve-2021-29526 2026-10-09T07:27:40.204175+00:00

TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a division by 0 in `tf.raw_ops.Conv2D`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/988087bd83f144af14087fe4fecee2d250d93737/tensorflow/core/kernels/conv_ops.cc#L261-L263) does a division by a quantity that is controlled by the caller. 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.

 https://cve.radiocsirt.org/vuln/ghsa-4vf2-4xcg-65cx GHSA-4vf2-4xcg-65cx — Division by 0 in `Conv2D` 2026-10-09T07:27:40.204209+00:00

Affected: PyPI: tensorflow, PyPI: tensorflow-cpu, PyPI: tensorflow-gpu

### Impact An attacker can trigger a division by 0 in `tf.raw_ops.Conv2D`:

```python import tensorflow as tf

input = tf.constant([], shape=[0, 0, 0, 0], dtype=tf.float32) filter = tf.constant([], shape=[0, 0, 0, 0], dtype=tf.float32)

strides = [1, 1, 1, 1] padding = "SAME" tf.raw_ops.Conv2D(input=input, filter=filter, strides=strides, padding=padding) ``` This is because the [implementation](https://github.com/tensorflow/tensorflow/blob/988087bd83f144af14087fe4fecee2d250d93737/tensorflow/core/kernels/conv_ops.cc#L261-L263) does a division by a quantity that is controlled by the caller: ```cc const int64 patch_depth = filter.dim_size(2); if (in_depth % patch_depth != 0) { ... } ``` ### Patches We have patched the issue in GitHub commit [b12aa1d44352de21d1a6faaf04172d8c2508b42b](https://github.com/tensorflow/tensorflow/commit/b12aa1d44352de21d1a6faaf04172d8c2508b42b). 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 Ying Wang and…

 https://cve.radiocsirt.org/vuln/gsd-2021-29526 gsd-2021-29526 2026-10-09T07:27:40.204265+00:00 gsd-2021-29526 https://cve.radiocsirt.org/vuln/pysec-2021-163 PYSEC-2021-163 2026-10-09T07:27:40.204278+00:00

Affected: PyPI: tensorflow

TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a division by 0 in `tf.raw_ops.Conv2D`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/988087bd83f144af14087fe4fecee2d250d93737/tensorflow/core/kernels/conv_ops.cc#L261-L263) does a division by a quantity that is controlled by the caller. 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.