CVE-2022-35941 (published 2022-09-16T20:15Z)

TensorFlow is an open source platform for machine learning. The `AvgPoolOp` function takes an argument `ksize` that must be positive but is not checked. A negative `ksize` can trigger a `CHECK` failure and crash the program. We have patched the issue in GitHub commit 3a6ac52664c6c095aa2b114e742b0aa17fdce78f. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds to this issue.

CVSS v3 base score: 7.5, HIGH (CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H)

Weakness information:
cwe_id: CWE-617
cwe_name: Reachable Assertion
description: The product contains an assert() or similar statement that can be triggered by an attacker, which leads to an application exit or other behavior that is more severe than necessary.
extended_description: 
            While assertion is good for catching logic errors and reducing the chances of reaching more serious vulnerability conditions, it can still lead to a denial of service.
            For example, if a server handles multiple simultaneous connections, and an assert() occurs in one single connection that causes all other connections to be dropped, this is a reachable assertion that leads to a denial of service.
         
         

Fix information:
repo_url: https://github.com/tensorflow/tensorflow
commit_details: repo_url: https://github.com/tensorflow/tensorflow
author: A. Unique TensorFlower
author_date: 2022-07-18 15:04:51-07:00
msg: Fix security vulnerability with AvgPoolGrad

PiperOrigin-RevId: 461724457
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: avgpooling_op.cc
change_type: ModificationType.MODIFY
diff: @@ -298,7 +298,7 @@ class AvgPoolingGradOp : public OpKernel {
     TensorShape output_shape;
     auto shape_vec = tensor_in_shape.vec<int32>();
     for (int64_t i = 0; i < tensor_in_shape.NumElements(); ++i) {
-      output_shape.AddDim(shape_vec(i));
+      OP_REQUIRES_OK(context, output_shape.AddDimWithStatus(shape_vec(i)));
     }
     const int64_t in_rows = output_shape.dim_size(1);
     const int64_t in_cols = output_shape.dim_size(2);
@@ -457,7 +457,7 @@ class AvgPoolingGradOp<GPUDevice, T> : public OpKernel {
     TensorShape output_shape;
     auto shape_vec = tensor_in_shape.vec<int32>();
     for (int64_t i = 0; i < tensor_in_shape.NumElements(); ++i) {
-      output_shape.AddDim(shape_vec(i));
+      OP_REQUIRES_OK(context, output_shape.AddDimWithStatus(shape_vec(i)));
     }
 
     if (output_shape.num_elements() == 0) {
@@ -543,7 +543,7 @@ class AvgPoolingGradOpCustomGPUKernel : public OpKernel {
     TensorShape output_shape;
     auto shape_vec = tensor_in_shape.vec<int32>();
     for (int64_t i = 0; i < tensor_in_shape.NumElements(); ++i) {
-      output_shape.AddDim(shape_vec(i));
+      OP_REQUIRES_OK(context, output_shape.AddDimWithStatus(shape_vec(i)));
     }
     if (output_shape.num_elements() == 0) {
       Tensor* output = nullptr;

programming_language: C++
filename: pooling_ops_test.py
change_type: ModificationType.MODIFY
diff: @@ -2470,6 +2470,22 @@ class PoolingTest(test.TestCase, parameterized.TestCase):
               inp, grad, argmax, ksize=[1, 1, 1, 1], strides=[1, 1, 1, 1],
               padding="VALID")
 
+  def testAvgPoolGradInvalidInputShapeRaiseError(self):
+    with self.assertRaises((ValueError, errors_impl.InvalidArgumentError)):
+      with self.cached_session():
+        orig_input_shape = constant_op.constant(
+            -536870912, shape=[4], dtype=dtypes.int32)
+        grad = constant_op.constant(
+            .0890338004362538, shape=[1, 5, 7, 1], dtype=dtypes.float64)
+        t = gen_nn_ops.AvgPoolGrad(
+            orig_input_shape=orig_input_shape,
+            grad=grad,
+            ksize=[1, 2, 2, 1],
+            strides=[1, 2, 2, 1],
+            padding="VALID",
+            data_format="NHWC")
+        self.evaluate(t)
+
 
 def GetMaxPoolFwdTest(input_size, filter_size, strides, padding):
 

programming_language: Python