CVE-2020-15198Medium· 5.4▾ SunlitHeap buffer overflow in Tensorflow
▾ Sunlit zone — Low / medium · no exploitation signal
impact 29.7 · likelihood 0.1 · exploitation 0
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Exploit-prediction probability, daily snapshots since Sep 12.
Disclosure to exploitation, from the record and what we observed since indexing it.
Disclosed via OSV
Last analysed / modified upstream
0.5%
The SparseCountSparseOutput implementation does not validate that the input arguments form a valid sparse tensor. In particular, there is no validation that the indices tensor has the same shape as the values one. The values in these tensors are always accessed in parallel:
https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/core/kernels/count_ops.cc#L193-L195
Thus, a shape mismatch can result in accesses outside the bounds of heap allocated buffers.
We have patched the issue in 3cbb917b4714766030b28eba9fb41bb97ce9ee02 and will release a patch release.
We recommend users to upgrade to TensorFlow 2.3.1.
Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.
This vulnerability is a variant of GHSA-p5f8-gfw5-33w4
tensorflow >= 2.3.0, < 2.3.1tensorflow-cpu >= 2.3.0, < 2.3.1tensorflow-gpu >= 2.3.0, < 2.3.1Upgrade to a patched release:
tensorflow 2.3.1tensorflow-cpu 2.3.1tensorflow-gpu 2.3.1Connected by shared product, vendor, weakness, or advisory.
CVE-2020-15200Medium· 5.9Segfault in Tensorflow
CVE-2020-15199Medium· 5.9Denial of Service in Tensorflow
CVE-2020-15197Medium· 6.3Denial of Service in Tensorflow
CVE-2020-15196High· 8.5Heap buffer overflow in Tensorflow
CVE-2020-15201Medium· 4.8Heap buffer overflow in Tensorflow
CVE-2020-15207High· 8.7Segfault and data corruption in tensorflow-lite