CVE-2021-29522 (published 2021-05-14T20:15Z)

TensorFlow is an end-to-end open source platform for machine learning. The `tf.raw_ops.Conv3DBackprop*` operations fail to validate that the input tensors are not empty. In turn, this would result in a division by 0. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/a91bb59769f19146d5a0c20060244378e878f140/tensorflow/core/kernels/conv_grad_ops_3d.cc#L430-L450) does not check that the divisor used in computing the shard size is not zero. Thus, if attacker controls the input sizes, they can trigger a denial of service via a division by zero error. 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-369
cwe_name: Divide By Zero
description: The product divides a value by zero.
extended_description: This weakness typically occurs when an unexpected value is provided to the product, or if an error occurs that is not properly detected. It frequently occurs in calculations involving physical dimensions such as size, length, width, and height.
         

Fix information:
repo_url: https://github.com/tensorflow/tensorflow
commit_details: repo_url: https://github.com/tensorflow/tensorflow
author: Mihai Maruseac
author_date: 2021-04-19 16:00:40-07:00
msg: Eliminate a division by 0 in 3D convolutions.

Also prevent a CHECK failed introduced in the most recent change.

PiperOrigin-RevId: 369322073
Change-Id: I4f609c028f89565fb2b49c3fdd20b63496582bae
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: conv_grad_ops_3d.cc
change_type: ModificationType.MODIFY
diff: @@ -239,6 +239,14 @@ class Conv3DBackpropInputOp : public OpKernel {
       input_shape = context->input(0).shape();
     }
 
+    OP_REQUIRES(context, input_shape.dims() == 5,
+                errors::InvalidArgument("input tensor must have 5 dimensions"));
+    OP_REQUIRES(
+        context, filter_shape.dims() == 5,
+        errors::InvalidArgument("filter_sizes tensor must have 5 dimensions"));
+    OP_REQUIRES(
+        context, out_backprop_shape.dims() == 5,
+        errors::InvalidArgument("out_backprop tensor must have 5 dimensions"));
     OP_REQUIRES(
         context, input_shape.dim_size(4) == filter_shape.dim_size(3),
         errors::InvalidArgument("input and filter_sizes must have the same "
@@ -360,6 +368,14 @@ class Conv3DCustomBackpropInputOp : public OpKernel {
       input_shape = context->input(0).shape();
     }
 
+    OP_REQUIRES(context, input_shape.dims() == 5,
+                errors::InvalidArgument("input tensor must have 5 dimensions"));
+    OP_REQUIRES(
+        context, filter_shape.dims() == 5,
+        errors::InvalidArgument("filter_sizes tensor must have 5 dimensions"));
+    OP_REQUIRES(
+        context, out_backprop_shape.dims() == 5,
+        errors::InvalidArgument("out_backprop tensor must have 5 dimensions"));
     OP_REQUIRES(
         context, input_shape.dim_size(4) == filter_shape.dim_size(3),
         errors::InvalidArgument("input and filter_sizes must have the same "
@@ -444,6 +460,11 @@ class Conv3DCustomBackpropInputOp : public OpKernel {
     // contraction compared to sharding and matmuls.
     const bool use_parallel_contraction = dims.batch_size == 1;
 
+    OP_REQUIRES(
+        context, work_unit_size > 0,
+        errors::InvalidArgument("input, filter_sizes and out_backprop tensors "
+                                "must all have at least 1 element"));
+
     const size_t shard_size =
         use_parallel_contraction
             ? 1
@@ -724,6 +745,14 @@ class Conv3DBackpropFilterOp : public OpKernel {
       filter_shape = context->input(1).shape();
     }
 
+    OP_REQUIRES(context, input_shape.dims() == 5,
+                errors::InvalidArgument("input tensor must have 5 dimensions"));
+    OP_REQUIRES(
+        context, filter_shape.dims() == 5,
+        errors::InvalidArgument("filter_sizes tensor must have 5 dimensions"));
+    OP_REQUIRES(
+        context, out_backprop_shape.dims() == 5,
+        errors::InvalidArgument("out_backprop tensor must have 5 dimensions"));
     OP_REQUIRES(
         context, input_shape.dim_size(4) == filter_shape.dim_size(3),
         errors::InvalidArgument("input and filter_sizes must have the same "
@@ -850,6 +879,14 @@ class Conv3DCustomBackpropFilterOp : public OpKernel {
       filter_shape = context->input(1).shape();
     }
 
+    OP_REQUIRES(context, input_shape.dims() == 5,
+                errors::InvalidArgument("input tensor must have 5 dimensions"));
+    OP_REQUIRES(
+        context, filter_shape.dims() == 5,
+        errors::InvalidArgument("filter_sizes tensor must have 5 dimensions"));
+    OP_REQUIRES(
+        context, out_backprop_shape.dims() == 5,
+        errors::InvalidArgument("out_backprop tensor must have 5 dimensions"));
     OP_REQUIRES(
         context, input_shape.dim_size(4) == filter_shape.dim_size(3),
         errors::InvalidArgument("input and filter_sizes must have the same "
@@ -936,6 +973,11 @@ class Conv3DCustomBackpropFilterOp : public OpKernel {
 
     const int64 work_unit_size = size_A + size_B + size_C;
 
+    OP_REQUIRES(
+        context, work_unit_size > 0,
+        errors::InvalidArgument("input, filter_sizes and out_backprop tensors "
+                                "must all have at least 1 element"));
+
     const size_t shard_size =
         (target_working_set_size + work_unit_size - 1) / work_unit_size;
 

programming_language: C++