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GHSA-3ff2-r28g-w7h9
No affected components available
Impact
The shape inference function for Transpose is vulnerable to a heap buffer overflow:
import tensorflow as tf
@tf.function
def test():
y = tf.raw_ops.Transpose(x=[1,2,3,4],perm=[-10])
return y
test()
This occurs whenever perm contains negative elements. The shape inference function does not validate that the indices in perm are all valid:
for (int32_t i = 0; i < rank; ++i) {
int64_t in_idx = data[i];
if (in_idx >= rank) {
return errors::InvalidArgument("perm dim ", in_idx,
" is out of range of input rank ", rank);
}
dims[i] = c->Dim(input, in_idx);
}
where Dim(tensor, index) accepts either a positive index less than the rank of the tensor or the special value -1 for unknown dimensions.
Patches
We have patched the issue in GitHub commit c79ba87153ee343401dbe9d1954d7f79e521eb14.
The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit 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.
Attribution
This vulnerability has been reported by members of the Aivul Team from Qihoo 360.
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 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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