CVE-2022-41887 (published 2022-11-18T22:15Z)

TensorFlow is an open source platform for machine learning. `tf.keras.losses.poisson` receives a `y_pred` and `y_true` that are passed through `functor::mul` in `BinaryOp`. If the resulting dimensions overflow an `int32`, TensorFlow will crash due to a size mismatch during broadcast assignment. We have patched the issue in GitHub commit c5b30379ba87cbe774b08ac50c1f6d36df4ebb7c. The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1 and 2.9.3, as these are also affected and still in supported range. However, we will not cherrypick this commit into TensorFlow 2.8.x, as it depends on Eigen behavior that changed between 2.8 and 2.9.

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-131
cwe_name: Incorrect Calculation of Buffer Size
description: The product does not correctly calculate the size to be used when allocating a buffer, which could lead to a buffer overflow.
extended_description: 
            
            
             
			 
            
         
         

Fix information:
repo_url: https://github.com/tensorflow/tensorflow
commit_details: repo_url: https://github.com/tensorflow/tensorflow
author: Antonio Sanchez
author_date: 2022-10-06 09:40:55-07:00
msg: Fix cwise dimension overflow issue again.

If resulting dimensions overflow an int32, we were seeing an overflow and
crash due to size mismatch during broadcast assignment.  The cause is a simple
dimension type mismatch.

Note that actual tests for this are currently impractical, since successful
operations require more than 2^32 elements and OOM on most machines.

PiperOrigin-RevId: 479336566
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: cwise_ops_common.h
change_type: ModificationType.MODIFY
diff: @@ -450,13 +450,15 @@ struct BinaryFunctor<CPUDevice, Functor, 2, false> {
     Assign(d, out, in.unaryExpr(Unary(scalar.data())));
   }
 
-  inline Eigen::IndexList<int, Eigen::type2index<1>> NByOne(int n) {
-    Eigen::IndexList<int, Eigen::type2index<1>> ret;
+  inline Eigen::IndexList<Eigen::DenseIndex, Eigen::type2index<1>> NByOne(
+      Eigen::DenseIndex n) {
+    Eigen::IndexList<Eigen::DenseIndex, Eigen::type2index<1>> ret;
     ret.set(0, n);
     return ret;
   }
-  inline Eigen::IndexList<Eigen::type2index<1>, int> OneByM(int m) {
-    Eigen::IndexList<Eigen::type2index<1>, int> ret;
+  inline Eigen::IndexList<Eigen::type2index<1>, Eigen::DenseIndex> OneByM(
+      Eigen::DenseIndex m) {
+    Eigen::IndexList<Eigen::type2index<1>, Eigen::DenseIndex> ret;
     ret.set(1, m);
     return ret;
   }
@@ -487,10 +489,10 @@ struct BinaryFunctor<CPUDevice, Functor, 2, false> {
       // use_broadcast_optimization<T> are compile-time constant, gcc
       // does a decent job avoiding generating code when conditions
       // are not met.
-      const int a = in0.dimension(0);  // in0 is shape [a, b]
-      const int b = in0.dimension(1);
-      const int c = in1.dimension(0);  // in1 is shape [c, d]
-      const int d = in1.dimension(1);
+      const Eigen::DenseIndex a = in0.dimension(0);  // in0 is shape [a, b]
+      const Eigen::DenseIndex b = in0.dimension(1);
+      const Eigen::DenseIndex c = in1.dimension(0);  // in1 is shape [c, d]
+      const Eigen::DenseIndex d = in1.dimension(1);
       if ((a == 1) && (d == 1)) {
         auto lhs = in0.reshape(OneByM(b)).broadcast(NByOne(c));
         auto rhs = in1.reshape(NByOne(c)).broadcast(OneByM(b));

programming_language: C++