https://cve.radiocsirt.org/rss/recent/all/10 Most recent entries from all 2026-10-23T22:35:56.644469+00:00 Vulnerability-Lookup csirt@opendfir.org python-feedgen Contains only the most 10 recent entries. https://cve.radiocsirt.org/vuln/bit-tensorflow-2022-29192 BIT-tensorflow-2022-29192 — Missing validation crashes `QuantizeAndDequantizeV4Grad` in TensorFlow 2026-10-23T22:35:56.769456+00:00

Affected: Bitnami: tensorflow

TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, the implementation of `tf.raw_ops.QuantizeAndDequantizeV4Grad` does not fully validate the input arguments. This results in a `CHECK`-failure which can be used to trigger a denial of service attack. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.

 https://cve.radiocsirt.org/vuln/cnvd-2022-44161 cnvd-2022-44161 2026-10-23T22:35:56.769518+00:00 cnvd-2022-44161 https://cve.radiocsirt.org/vuln/euvd-2026-232733 EUVD-2026-232733 2026-10-23T22:35:56.769537+00:00 EUVD-2026-232733 https://cve.radiocsirt.org/vuln/fkie_cve-2022-29192 fkie_cve-2022-29192 2026-10-23T22:35:56.769548+00:00

TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, the implementation of `tf.raw_ops.QuantizeAndDequantizeV4Grad` does not fully validate the input arguments. This results in a `CHECK`-failure which can be used to trigger a denial of service attack. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.

 https://cve.radiocsirt.org/vuln/ghsa-h2wq-prv9-2f56 GHSA-h2wq-prv9-2f56 — Missing validation crashes `QuantizeAndDequantizeV4Grad` 2026-10-23T22:35:56.769572+00:00

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

### Impact The implementation of [`tf.raw_ops.QuantizeAndDequantizeV4Grad`](https://github.com/tensorflow/tensorflow/blob/f3b9bf4c3c0597563b289c0512e98d4ce81f886e/tensorflow/core/kernels/quantize_and_dequantize_op.cc#L148-L226) does not fully validate the input arguments. This results in a `CHECK`-failure which can be used to trigger a denial of service attack:

```python import tensorflow as tf

tf.raw_ops.QuantizeAndDequantizeV4Grad( gradients=tf.constant(1, shape=[2,2], dtype=tf.float64), input=tf.constant(1, shape=[2,2], dtype=tf.float64), input_min=tf.constant([], shape=[0], dtype=tf.float64), input_max=tf.constant(-10, shape=[], dtype=tf.float64), axis=-1) ```

The code assumes `input_min` and `input_max` are scalars but there is no validation for this.

### Patches We have patched the issue in GitHub commit [098e7762d909bac47ce1dbabe6dfd06294cb9d58](https://github.com/tensorflow/tensorflow/commit/098e7762d909bac47ce1dbabe6dfd06294cb9d58). The fix will be included in TensorFlow 2.9.0. We will also cherrypick this commit on TensorFlow 2.8.1, TensorFlow 2.7.2, and TensorFlow 2.6.4, 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 Neophytos Christou from Secure Systems L…

 https://cve.radiocsirt.org/vuln/gsd-2022-29192 gsd-2022-29192 2026-10-23T22:35:56.769620+00:00 gsd-2022-29192 https://cve.radiocsirt.org/vuln/pysec-2026-1002 PYSEC-2026-1002 — Missing validation crashes `QuantizeAndDequantizeV4Grad` 2026-10-23T22:35:56.769633+00:00

Affected: PyPI: tensorflow-gpu

### Impact The implementation of [`tf.raw_ops.QuantizeAndDequantizeV4Grad`](https://github.com/tensorflow/tensorflow/blob/f3b9bf4c3c0597563b289c0512e98d4ce81f886e/tensorflow/core/kernels/quantize_and_dequantize_op.cc#L148-L226) does not fully validate the input arguments. This results in a `CHECK`-failure which can be used to trigger a denial of service attack:

```python import tensorflow as tf

tf.raw_ops.QuantizeAndDequantizeV4Grad( gradients=tf.constant(1, shape=[2,2], dtype=tf.float64), input=tf.constant(1, shape=[2,2], dtype=tf.float64), input_min=tf.constant([], shape=[0], dtype=tf.float64), input_max=tf.constant(-10, shape=[], dtype=tf.float64), axis=-1) ```

The code assumes `input_min` and `input_max` are scalars but there is no validation for this.

### Patches We have patched the issue in GitHub commit [098e7762d909bac47ce1dbabe6dfd06294cb9d58](https://github.com/tensorflow/tensorflow/commit/098e7762d909bac47ce1dbabe6dfd06294cb9d58). The fix will be included in TensorFlow 2.9.0. We will also cherrypick this commit on TensorFlow 2.8.1, TensorFlow 2.7.2, and TensorFlow 2.6.4, 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 Neophytos Christou from Secure Systems L…