GHSA-9crf-c6qr-r273

Dashboard / Vulnerabilities / GHSA-9crf-c6qr-r273

GHSA-9crf-c6qr-r273

Summary: Integer division by 0 in `tf.raw_ops.AllToAll`

Details: ### Impact The [shape inference code for `AllToAll`](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/ops/tpu_cross_replica_ops.cc#L25-L74) can be made to execute a division by 0: ```python import tensorflow as tf @tf.function def func(): return tf.raw_ops.AllToAll( input=[0.0, 0.1652, 0.6543], group_assignment=[1, -1], concat_dimension=0, split_dimension=0, split_count=0) func() ``` This occurs whenever the `split_count` argument is 0: ```cc TF_RETURN_IF_ERROR(c->GetAttr("split_count", &split_count)); ... for (int32_t i = 0; i < rank; ++i) { ... dims[i] = c->MakeDim(c->Value(dims[i]) / split_count); ... } ``` ### Patches We have patched the issue in GitHub commit [a8ad3e5e79c75f36edb81e0ba3f3c0c5442aeddc](https://github.com/tensorflow/tensorflow/commit/a8ad3e5e79c75f36edb81e0ba3f3c0c5442aeddc). The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, 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 members of the Aivul Team from Qihoo 360.

References: https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9crf-c6qr-r273, https://nvd.nist.gov/vuln/detail/CVE-2021-41218, https://github.com/tensorflow/tensorflow/commit/a8ad3e5e79c75f36edb81e0ba3f3c0c5442aeddc, https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-627.yaml, https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-825.yaml, https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-410.yaml, https://github.com/tensorflow/tensorflow