CHECK-fail in `QuantizeAndDequantizeV4Grad`

### Impact An attacker can trigger a denial of service via a `CHECK`-fail in `tf.raw_ops.QuantizeAndDequantizeV4Grad`: ```python import tensorflow as tf gradient_tensor = tf.constant([0.0], shape=[1]) input_tensor = tf.constant([0.0], shape=[1]) input_min = tf.constant([[0.0]], shape=[1, 1]) input_max = tf.constant([[0.0]], shape=[1, 1]) tf.raw_ops.QuantizeAndDequantizeV4Grad( gradients=gradient_tensor, input=input_tensor, input_min=input_min, input_max=input_max, axis=0) ``` This is because the [implementation](https://github.com/tensorflow/tensorflow/blob/95078c145b5a7a43ee046144005f733092756ab5/tensorflow/core/kernels/quantize_and_dequantize_op.cc#L162-L163) does not validate the rank of the `input_*` tensors. In turn, this results in the tensors being passes as they are to [`QuantizeAndDequantizePerChannelGradientImpl`](https://github.com/tensorflow/tensorflow/blob/95078c145b5a7a43ee046144005f733092756ab5/tensorflow/core/kernels/quantize_and_dequantize_op.h#L295-L306): ```cc template <typename Device, typename T> struct QuantizeAndDequantizePerChannelGradientImpl { static void Compute(const Device& d, typename TTypes<T, 3>::ConstTensor gradient, typename TTypes<T, 3>::ConstTensor input, const Tensor* input_min_tensor, const Tensor* input_max_tensor, typename TTypes<T, 3>::Tensor input_backprop, typename TTypes<T>::Flat input_min_backprop, typename TTypes<T>::Flat input_max_backprop) { ... auto input_min = input_min_tensor->vec<T>(); auto input_max = input_max_tensor->vec<T>(); ... } ``` However, the `vec<T>` method, requires the rank to 1 and triggers a `CHECK` failure otherwise. ### Patches We have patched the issue in GitHub commit [20431e9044cf2ad3c0323c34888b192f3289af6b](https://github.com/tensorflow/tensorflow/commit/20431e9044cf2ad3c0323c34888b192f3289af6b). The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2 as this is the only other affected version. ### 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 Yakun Zhang and Ying Wang of Baidu X-Team.

CVSS 3.1

CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L

Severity from

GitHub (reviewed advisory)

Weakness

CWE-754

Also known as

BIT-tensorflow-2021-29544, CVE-2021-29544, PYSEC-2021-181, PYSEC-2021-472, PYSEC-2021-670

• github.com/tensorflow/tensorflow/security/advisories/GHSA-6g85-3hm8-83f9
• nvd.nist.gov/vuln/detail/CVE-2021-29544
• github.com/tensorflow/tensorflow/commit/20431e9044cf2ad3c0323c34888b192f3289af6b
• github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-472.yaml
• github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-670.yaml
• github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-181.yaml
• github.com/tensorflow/tensorflow
• github.com/tensorflow/tensorflow/blob/95078c145b5a7a43ee046144005f733092756ab5/tensorflow/core/kernels/quantize_and_dequantize_op.cc#L162-L163
• github.com/tensorflow/tensorflow/blob/95078c145b5a7a43ee046144005f733092756ab5/tensorflow/core/kernels/quantize_and_dequantize_op.h#L295-L306