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CVE-2022-36005: `CHECK` fail in `FakeQuantWithMinMaxVarsGradient`

TensorFlow is an open source platform for machine learning. When tf.quantization.fake_quant_with_min_max_vars_gradient receives input min or max that is nonscalar, it gives a CHECK fail that can trigger a denial of service attack. We have patched the issue in GitHub commit f3cf67ac5705f4f04721d15e485e192bb319feed. 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.

CVE
#vulnerability#mac#dos#git

Package

pip tensorflow, tensorflow-cpu, tensorflow-gpu (pip)

Affected versions

< 2.10.0

Patched versions

2.7.2, 2.8.1, 2.9.1, 2.10.0

Impact

When tf.quantization.fake_quant_with_min_max_vars_gradient receives input min or max that is nonscalar, it gives a CHECK fail that can trigger a denial of service attack.

import tensorflow as tf import numpy as np arg_0=tf.constant(value=np.random.random(size=(2, 2)), shape=(2, 2), dtype=tf.float32) arg_1=tf.constant(value=np.random.random(size=(2, 2)), shape=(2, 2), dtype=tf.float32) arg_2=tf.constant(value=np.random.random(size=(2, 2)), shape=(2, 2), dtype=tf.float32) arg_3=tf.constant(value=np.random.random(size=(2, 2)), shape=(2, 2), dtype=tf.float32) arg_4=8 arg_5=False arg_6=’’ tf.quantization.fake_quant_with_min_max_vars_gradient(gradients=arg_0, inputs=arg_1, min=arg_2, max=arg_3, num_bits=arg_4, narrow_range=arg_5, name=arg_6)

Patches

We have patched the issue in GitHub commit f3cf67ac5705f4f04721d15e485e192bb319feed.

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.

For more information

Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.

Attribution

This vulnerability has been reported by

  • 刘力源, Information System & Security and Countermeasures Experiments Center, Beijing Institute of Technology
  • Neophytos Christou, Secure Systems Labs, Brown University

Related news

GHSA-r26c-679w-mrjm: TensorFlow vulnerable to `CHECK` fail in `FakeQuantWithMinMaxVarsGradient`

### Impact When `tf.quantization.fake_quant_with_min_max_vars_gradient` receives input `min` or `max` that is nonscalar, it gives a `CHECK` fail that can trigger a denial of service attack. ```python import tensorflow as tf import numpy as np arg_0=tf.constant(value=np.random.random(size=(2, 2)), shape=(2, 2), dtype=tf.float32) arg_1=tf.constant(value=np.random.random(size=(2, 2)), shape=(2, 2), dtype=tf.float32) arg_2=tf.constant(value=np.random.random(size=(2, 2)), shape=(2, 2), dtype=tf.float32) arg_3=tf.constant(value=np.random.random(size=(2, 2)), shape=(2, 2), dtype=tf.float32) arg_4=8 arg_5=False arg_6='' tf.quantization.fake_quant_with_min_max_vars_gradient(gradients=arg_0, inputs=arg_1, min=arg_2, max=arg_3, num_bits=arg_4, narrow_range=arg_5, name=arg_6) ``` ### Patches We have patched the issue in GitHub commit [f3cf67ac5705f4f04721d15e485e192bb319feed](https://github.com/tensorflow/tensorflow/commit/f3cf67ac5705f4f04721d15e485e192bb319feed). The fix will be included in T...

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