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Moderate severityNVD Advisory· Published Sep 16, 2022· Updated Apr 23, 2025

`CHECK` fail in `tf.random.gamma` in TensorFlow

CVE-2022-36004

Description

TensorFlow is an open source platform for machine learning. When tf.random.gamma 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.

Affected packages

Versions sourced from the GitHub Security Advisory.

PackageAffected versionsPatched versions
tensorflowPyPI
< 2.7.22.7.2
tensorflowPyPI
>= 2.8.0, < 2.8.12.8.1
tensorflowPyPI
>= 2.9.0, < 2.9.12.9.1
tensorflow-cpuPyPI
< 2.7.22.7.2
tensorflow-cpuPyPI
>= 2.8.0, < 2.8.12.8.1
tensorflow-cpuPyPI
>= 2.9.0, < 2.9.12.9.1
tensorflow-gpuPyPI
< 2.7.22.7.2
tensorflow-gpuPyPI
>= 2.8.0, < 2.8.12.8.1
tensorflow-gpuPyPI
>= 2.9.0, < 2.9.12.9.1

Affected products

1

Patches

1
552bfced6ce4

Fix size check for large input shape and rates.

https://github.com/tensorflow/tensorflowAntonio SanchezJul 24, 2022via ghsa
commit
4 files changed · +24 −2
  • tensorflow/core/kernels/random_op.cc+1 −1 modified
    @@ -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));
  • tensorflow/core/kernels/random_poisson_op.cc+1 −1 modified
    @@ -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));
  • tensorflow/python/kernel_tests/random/random_gamma_test.py+13 −0 modified
    @@ -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 @@ def testPositive(self):
         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()
  • tensorflow/python/kernel_tests/random/random_poisson_test.py+9 −0 modified
    @@ -17,6 +17,7 @@
 
 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 @@ def testInfRate(self):
     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()

Vulnerability mechanics

Generated by null/stub on May 9, 2026. Inputs: CWE entries + fix-commit diffs from this CVE's patches. Citations validated against bundle.

References

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