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GHSA-wf5p-c75w-w3wh
No affected components available
Impact
An attacker can craft a TFLite model that would trigger a null pointer dereference, which would result in a crash and denial of service:
This is caused by the MLIR optimization of L2NormalizeReduceAxis operator. The implementation unconditionally dereferences a pointer to an iterator to a vector without checking that the vector has elements:
bool L2NormalizeReduceAxis(Value sq_op, DenseElementsAttr axis) {
if (sq_op.getType().cast<ShapedType>().getRank() - 1 ==
*axis.getValues<int>().begin() ||
*axis.getValues<int>().begin() == -1) {
// ...
}
// ...
}
Patches
We have patched the issue in GitHub commit d6b57f461b39fd1aa8c1b870f1b974aac3554955.
The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.
For more information
Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.
Attribution
This vulnerability has been reported by Yakun Zhang of Baidu Security.
The vulnerability requires local access to the device to be exploited. It is easy for an attacker to exploit this vulnerability. An attacker needs basic access or low-level privileges. No user interaction is needed for the attacker to exploit this vulnerability. The impact is confined to the system where the vulnerability exists. There is a high impact on the confidentiality of the information. There is a high impact on the integrity of the data. There is a high impact on the availability of the system.
Exploitation activity has been observed. Apply available patches or mitigations urgently.
The exploit probability is very low. The vulnerability is unlikely to be exploited in the next 30 days.
We did not find any exploit available. Neither in GitHub repositories nor in the Exploit-Database.
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