CVE-2022-36003 (published 2022-09-16T23:15Z)

TensorFlow is an open source platform for machine learning. When `RandomPoissonV2` receives large input shape and rates, it gives a `CHECK` fail that can trigger a denial of service attack. We have patched the issue in GitHub commit 552bfced6ce4809db5f3ca305f60ff80dd40c5a3. 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 for 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: Antonio Sanchez
author_date: 2022-07-23 22:03:07-07:00
msg: Fix size check for large input shape and rates.

To address a check failure for exceedingly large output shapes, we need to
`AppendShapeWithStatus`.

PiperOrigin-RevId: 462885894
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: random_op.cc
change_type: ModificationType.MODIFY
diff: @@ -166,7 +166,7 @@ class RandomGammaOp : public OpKernel {
     }
     const int64_t samples_per_alpha = samples_shape.num_elements();
 
-    samples_shape.AppendShape(alpha_t.shape());
+    OP_REQUIRES_OK(ctx, samples_shape.AppendShapeWithStatus(alpha_t.shape()));
     // Allocate output samples.
     Tensor* samples_t = nullptr;
     OP_REQUIRES_OK(ctx, ctx->allocate_output(0, samples_shape, &samples_t));

programming_language: C++
filename: random_poisson_op.cc
change_type: ModificationType.MODIFY
diff: @@ -296,8 +296,8 @@ class RandomPoissonOp : public OpKernel {
     TensorShape samples_shape;
     OP_REQUIRES_OK(ctx, tensor::MakeShape(shape_t, &samples_shape));
     const int64_t num_samples = samples_shape.num_elements();
+    OP_REQUIRES_OK(ctx, samples_shape.AppendShapeWithStatus(rate_t.shape()));
 
-    samples_shape.AppendShape(rate_t.shape());
     // Allocate output samples.
     Tensor* samples_t = nullptr;
     OP_REQUIRES_OK(ctx, ctx->allocate_output(0, samples_shape, &samples_t));

programming_language: C++
filename: random_gamma_test.py
change_type: ModificationType.MODIFY
diff: @@ -16,7 +16,10 @@
 
 import numpy as np
 
+from tensorflow.python.eager import context
+from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import errors
 from tensorflow.python.framework import ops
 from tensorflow.python.framework import random_seed
 from tensorflow.python.framework import test_util
@@ -216,6 +219,16 @@ class RandomGammaTest(test.TestCase):
         self.assertEqual(0, math_ops.reduce_sum(math_ops.cast(
             math_ops.less_equal(x, 0.), dtype=dtypes.int64)).eval())
 
+  def testSizeTooLarge(self):
+    # Grappler asserts on size overflow, so this error is only caught when
+    # running eagerly.
+    if context.executing_eagerly():
+      with self.assertRaisesRegex((ValueError, errors.InvalidArgumentError),
+                                  "overflow"):
+        rate = constant_op.constant(1.0, shape=(4, 4, 4, 4, 4))
+        self.evaluate(
+            random_ops.random_gamma(
+                shape=[46902, 51188, 34063, 59195], alpha=rate))
 
 if __name__ == "__main__":
   test.main()

programming_language: Python
filename: random_poisson_test.py
change_type: ModificationType.MODIFY
diff: @@ -17,6 +17,7 @@ import numpy as np
 
 from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import errors
 from tensorflow.python.framework import ops
 from tensorflow.python.framework import test_util
 from tensorflow.python.kernel_tests.random import util
@@ -171,6 +172,14 @@ class RandomPoissonTest(test.TestCase):
     sample = random_ops.random_poisson(shape=[2], lam=np.inf)
     self.assertAllEqual([np.inf, np.inf], self.evaluate(sample))
 
+  def testSizeTooLarge(self):
+    with self.assertRaisesRegex((ValueError, errors.InvalidArgumentError),
+                                "overflow"):
+      rate = constant_op.constant(1.0, shape=(4, 4, 4, 4, 4))
+      self.evaluate(
+          random_ops.random_poisson(
+              shape=[46902, 51188, 34063, 59195], lam=rate))
+
 
 if __name__ == "__main__":
   test.main()

programming_language: Python