CVE-2021-29580 (published 2021-05-14T20:15Z)

TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.FractionalMaxPoolGrad` triggers an undefined behavior if one of the input tensors is empty. The code is also vulnerable to a denial of service attack as a `CHECK` condition becomes false and aborts the process. The implementation(https://github.com/tensorflow/tensorflow/blob/169054888d50ce488dfde9ca55d91d6325efbd5b/tensorflow/core/kernels/fractional_max_pool_op.cc#L215) fails to validate that input and output tensors are not empty and are of the same rank. Each of these unchecked assumptions is responsible for the above issues. 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.

Severity: LOW
CVSS v2 base score: 2.1 (AV:L/AC:L/Au:N/C:N/I:N/A:P)
CVSS v3 base score: 5.5, MEDIUM (CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H)

Weakness information:
cwe_id: CWE-908
cwe_name: Use of Uninitialized Resource
description: The product uses or accesses a resource that has not been initialized.
extended_description: When a resource has not been properly initialized, the product may behave unexpectedly. This may lead to a crash or invalid memory access, but the consequences vary depending on the type of resource and how it is used within the product.
         

Fix information:
repo_url: https://github.com/tensorflow/tensorflow
commit_details: repo_url: https://github.com/tensorflow/tensorflow
author: Mihai Maruseac
author_date: 2021-05-05 22:39:29-07:00
msg: Validate arguments of `FractionalMaxPoolGrad`

PiperOrigin-RevId: 372274982
Change-Id: If46b0c442efa4eaef635ce6a476717060420122c
repository: repo_url: https://github.com/tensorflow/tensorflow
repo_name: tensorflow/tensorflow
description: An Open Source Machine Learning Framework for Everyone
repo_language: C++
owner: tensorflow
file_changes: filename: fractional_max_pool_op.cc
change_type: ModificationType.MODIFY
diff: @@ -235,6 +235,20 @@ class FractionalMaxPoolGradOp : public OpKernel {
 
     // Just to make it similar to FractionalMaxPoolOp.
     constexpr int tensor_in_and_out_dims = 4;
+    OP_REQUIRES(
+        context, tensor_in.dims() == tensor_in_and_out_dims,
+        errors::InvalidArgument("orig_input should be a tensor of rank 4, got ",
+                                tensor_in.DebugString()));
+    OP_REQUIRES(context, tensor_in.NumElements() > 0,
+                errors::InvalidArgument("orig_input must not be empty, got ",
+                                        tensor_in.DebugString()));
+    OP_REQUIRES(context, tensor_out.dims() == tensor_in_and_out_dims,
+                errors::InvalidArgument(
+                    "orig_output should be a tensor of rank 4, got ",
+                    tensor_out.DebugString()));
+    OP_REQUIRES(context, tensor_out.NumElements() > 0,
+                errors::InvalidArgument("orig_output must not be empty, got ",
+                                        tensor_out.DebugString()));
     std::vector<int64> input_size(tensor_in_and_out_dims);
     std::vector<int64> output_size(tensor_in_and_out_dims);
     for (int i = 0; i < tensor_in_and_out_dims; ++i) {

programming_language: C++