CVE-2020-15208High· 7.4▾ TwilightData corruption in tensorflow-lite
▾ Twilight zone — High severity, or a signal on a lesser flaw
impact 40.7 · likelihood 0.2 · exploitation 0
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Exploit-prediction probability, daily snapshots since Jul 8.
Disclosure to exploitation, from the record and what we observed since indexing it.
Disclosed via OSV
Last analysed / modified upstream
0.9%
0.9% → 0.9%
When determining the common dimension size of two tensors, TFLite uses a DCHECK which is no-op outside of debug compilation modes:
https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/lite/kernels/internal/types.h#L437-L442
Since the function always returns the dimension of the first tensor, malicious attackers can craft cases where this is larger than that of the second tensor. In turn, this would result in reads/writes outside of bounds since the interpreter will wrongly assume that there is enough data in both tensors.
We have patched the issue in 8ee24e7949a20 and will release patch releases for all versions between 1.15 and 2.3.
We recommend users to upgrade to TensorFlow 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 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 has been reported by members of the Aivul Team from Qihoo 360.
tensorflow < 1.15.4tensorflow >= 2.0.0, < 2.0.3tensorflow >= 2.1.0, < 2.1.2tensorflow >= 2.2.0, < 2.2.1tensorflow >= 2.3.0, < 2.3.1tensorflow-cpu < 1.15.4tensorflow-cpu >= 2.0.0, < 2.0.3tensorflow-cpu >= 2.1.0, < 2.1.2tensorflow-cpu >= 2.2.0, < 2.2.1tensorflow-cpu >= 2.3.0, < 2.3.1tensorflow-gpu < 1.15.4tensorflow-gpu >= 2.0.0, < 2.0.3tensorflow-gpu >= 2.1.0, < 2.1.2tensorflow-gpu >= 2.2.0, < 2.2.1tensorflow-gpu >= 2.3.0, < 2.3.1Upgrade to a patched release:
tensorflow 1.15.4tensorflow 2.0.3tensorflow 2.1.2tensorflow 2.2.1tensorflow 2.3.1tensorflow-cpu 1.15.4tensorflow-cpu 2.0.3tensorflow-cpu 2.1.2tensorflow-cpu 2.2.1tensorflow-cpu 2.3.1tensorflow-gpu 1.15.4tensorflow-gpu 2.0.3tensorflow-gpu 2.1.2tensorflow-gpu 2.2.1tensorflow-gpu 2.3.1Connected by shared product, vendor, weakness, or advisory.
CVE-2020-15207High· 8.7Segfault and data corruption in tensorflow-lite
CVE-2020-15203High· 7.5Denial of Service in Tensorflow
CVE-2020-15210Medium· 6.5Segmentation fault in tensorflow-lite
CVE-2020-15206Critical· 9.0Denial of Service in Tensorflow
CVE-2020-15193High· 7.1Memory corruption in Tensorflow
CVE-2020-15209Medium· 5.9Null pointer dereference in tensorflow-lite