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GHSA-vq36-27g6-p492
No affected components available
Impact
TensorFlow's type inference can cause a heap OOB read as the bounds checking is done in a DCHECK (which is a no-op during production):
if (node_t.type_id() != TFT_UNSET) {
int ix = input_idx[i];
DCHECK(ix < node_t.args_size())
<< "input " << i << " should have an output " << ix
<< " but instead only has " << node_t.args_size()
<< " outputs: " << node_t.DebugString();
input_types.emplace_back(node_t.args(ix));
// ...
}
An attacker can control input_idx such that ix would be larger than the number of values in node_t.args.
Patches
We have patched the issue in GitHub commit c99d98cd189839dcf51aee94e7437b54b31f8abd.
The fix will be included in TensorFlow 2.8.0. This is the only affected version.
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 can be exploited over the network without needing physical access. 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 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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