GHSA-qg48-85hg-mqc5

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GHSA-qg48-85hg-mqc5

Summary: Division by 0 in `DenseCountSparseOutput`

Details: ### Impact An attacker can cause a denial of service via a FPE runtime error in `tf.raw_ops.DenseCountSparseOutput`: ```python import tensorflow as tf values = tf.constant([], shape=[0, 0], dtype=tf.int64) weights = tf.constant([]) tf.raw_ops.DenseCountSparseOutput( values=values, weights=weights, minlength=-1, maxlength=58, binary_output=True) ``` This is because the [implementation](https://github.com/tensorflow/tensorflow/blob/efff014f3b2d8ef6141da30c806faf141297eca1/tensorflow/core/kernels/count_ops.cc#L123-L127) computes a divisor value from user data but does not check that the result is 0 before doing the division: ```cc int num_batch_elements = 1; for (int i = 0; i < num_batch_dimensions; ++i) { num_batch_elements *= data.shape().dim_size(i); } int num_value_elements = data.shape().num_elements() / num_batch_elements; ``` Since `data` is given by the `values` argument, `num_batch_elements` is 0. ### Patches We have patched the issue in GitHub commit [da5ff2daf618591f64b2b62d9d9803951b945e9f](https://github.com/tensorflow/tensorflow/commit/da5ff2daf618591f64b2b62d9d9803951b945e9f). The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, and TensorFlow 2.3.3, as these are also affected. ### 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.

References: https://github.com/tensorflow/tensorflow/security/advisories/GHSA-qg48-85hg-mqc5, https://nvd.nist.gov/vuln/detail/CVE-2021-29554, https://github.com/tensorflow/tensorflow/commit/da5ff2daf618591f64b2b62d9d9803951b945e9f, https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-482.yaml, https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-680.yaml, https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-191.yaml, https://github.com/tensorflow/tensorflow