CVE-2021-29535Low· 2.5▾ SunlitHeap buffer overflow in `QuantizedMul`
▾ Sunlit zone — Low / medium · no exploitation signal
impact 13.8 · likelihood 0 · exploitation 0
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Exploit-prediction probability, daily snapshots since Sep 12.
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 heap buffer overflow in QuantizedMul by passing in invalid thresholds for the quantization:
import tensorflow as tf
x = tf.constant([256, 328], shape=[1, 2], dtype=tf.quint8)
y = tf.constant([256, 328], shape=[1, 2], dtype=tf.quint8)
min_x = tf.constant([], dtype=tf.float32)
max_x = tf.constant([], dtype=tf.float32)
min_y = tf.constant([], dtype=tf.float32)
max_y = tf.constant([], dtype=tf.float32)
tf.raw_ops.QuantizedMul(x=x, y=y, min_x=min_x, max_x=max_x, min_y=min_y, max_y=max_y)
This is because the implementation assumes that the 4 arguments are always valid scalars and tries to access the numeric value directly:
const float min_x = context->input(2).flat<float>()(0);
const float max_x = context->input(3).flat<float>()(0);
const float min_y = context->input(4).flat<float>()(0);
const float max_y = context->input(5).flat<float>()(0);
However, if any of these tensors is empty, then .flat<T>() is an empty buffer and accessing the element at position 0 results in overflow.
We have patched the issue in GitHub commit efea03b38fb8d3b81762237dc85e579cc5fc6e87.
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 Ying Wang and Yakun Zhang 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-2021-29541Low· 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
CVE-2020-15198Medium· 5.4Heap buffer overflow in Tensorflow