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GHSA-7pxj-m4jf-r6h2
No affected components available
Impact
An attacker can trigger undefined behavior, integer overflows, segfaults and CHECK-fail crashes if they can change saved checkpoints from outside of TensorFlow.
This is because the checkpoints loading infrastructure is missing validation for invalid file formats.
Patches
We have patched the issue in GitHub commits b619c6f865715ca3b15ef1842b5b95edbaa710ad, e8dc63704c88007ee4713076605c90188d66f3d2, 368af875869a204b4ac552b9ddda59f6a46a56ec, and abcced051cb1bd8fb05046ac3b6023a7ebcc4578.
These fixes will be included in TensorFlow 2.7.0. We will also cherrypick these commits on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, 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.
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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