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GHSA-977j-xj7q-2jr9
No affected components available
Impact
Converting a string (from Python) to a tf.float16 value results in a segmentation fault in eager mode as the format checks for this use case are only in the graph mode.
This issue can lead to denial of service in inference/training where a malicious attacker can send a data point which contains a string instead of a tf.float16 value.
Similar effects can be obtained by manipulating saved models and checkpoints whereby replacing a scalar tf.float16 value with a scalar string will trigger this issue due to automatic conversions.
This can be easily reproduced by tf.constant("hello", tf.float16), if eager execution is enabled.
Patches
We have patched the vulnerability in GitHub commit 5ac1b9.
We are additionally releasing TensorFlow 1.15.1 and 2.0.1 with this vulnerability patched.
TensorFlow 2.1.0 was released after we fixed the issue, thus it is not affected.
We encourage users to switch to TensorFlow 1.15.1, 2.0.1 or 2.1.0.
For more information
Please consult SECURITY.md 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 difficult for an attacker to exploit this vulnerability and may require special conditions. An attacker needs basic access or low-level privileges. The attacker needs the user to perform some action, like clicking a link. The vulnerability can affect other systems as well, not just the initial system. There is a low impact on the confidentiality of the information. There is a low impact on the integrity of the data. There is a low impact on the availability of the system.
Exploitation attempts have been detected. Elevated vigilance and prompt remediation are advised.
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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