CVE-2021-29583
published 2021-05-14CVE-2021-29583: TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.FusedBatchNorm` is vulnerable to a heap buffer…
PriorityP336high7.8CVSS 3.1
AVLACLPRLUINSUCHIHAH
EPSS
0.21%
11.3th percentile
TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.FusedBatchNorm` is vulnerable to a heap buffer overflow. If the tensors are empty, the same implementation can trigger undefined behavior by dereferencing null pointers. The implementation(https://github.com/tensorflow/tensorflow/blob/57d86e0db5d1365f19adcce848dfc1bf89fdd4c7/tensorflow/core/kernels/fused_batch_norm_op.cc) fails to validate that `scale`, `offset`, `mean` and `variance` (the last two only when required) all have the same number of elements as the number of channels of `x`. This results in heap out of bounds reads when the buffers backing these tensors are indexed past their boundary. If the tensors are empty, the validation mentioned in the above paragraph would also trigger and prevent the undefined behavior. 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.
Affected
18 ranges
| Vendor | Product | Version range | Fixed in |
|---|---|---|---|
| debian | tensorflow | — | — |
| tensorflow | < 2.1.4 | 2.1.4 | |
| tensorflow | >= 2.2.0 < 2.2.3 | 2.2.3 | |
| tensorflow | >= 2.3.0 < 2.3.3 | 2.3.3 | |
| tensorflow | >= 2.4.0 < 2.4.2 | 2.4.2 | |
| intel | optimization_for_tensorflow | >= 0 < 2.1.4 | 2.1.4 |
| intel | optimization_for_tensorflow | >= 0 < 6972f9dfe325636b3db4e0bc517ee22a159365c0 | 6972f9dfe325636b3db4e0bc517ee22a159365c0 |
| intel | optimization_for_tensorflow | >= 0 < 2.2.0rc0 | 2.2.0rc0 |
| intel | optimization_for_tensorflow | >= 2.2.0 < 2.2.3 | 2.2.3 |
| intel | optimization_for_tensorflow | >= 2.2.0 < 2.3.0rc0 | 2.3.0rc0 |
| intel | optimization_for_tensorflow | >= 2.3.0 < 2.3.3 | 2.3.3 |
| intel | optimization_for_tensorflow | >= 2.3.0 < 2.3.4 | 2.3.4 |
| intel | optimization_for_tensorflow | >= 2.4.0 < 2.4.2 | 2.4.2 |
| intel | optimization_for_tensorflow | >= 2.4.0 < 2.4.3 | 2.4.3 |
| tensorflow | tensorflow | < 2.1.4 | 2.1.4 |
| tensorflow | tensorflow | — | — |
| tensorflow | tensorflow | — | — |
| tensorflow | tensorflow | — | — |
CVSS provenance
nvdv3.17.8HIGHCVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H
nvdv2.04.6MEDIUMAV:L/AC:L/Au:N/C:P/I:P/A:P
vendor_debian2.5LOW
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Debian
CVE-2021-29583: tensorflow - TensorFlow is an end-to-end open source platform for machine learning. The imple...
vendor_debian·2021·CVSS 2.5
CVE-2021-29583 [LOW] CVE-2021-29583: tensorflow - TensorFlow is an end-to-end open source platform for machine learning. The imple...
TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.FusedBatchNorm` is vulnerable to a heap buffer overflow. If the tensors are empty, the same implementation can trigger undefined behavior by dereferencing null pointers. The implementation(https://github.com/tensorflow/tensorflow/blob/57d86e0db5d1365f19adcce848dfc1bf89fdd4c7/tensorflow/core/kernels/fused_batch_norm_op.cc) fails to validate that `scale`, `offset`, `mean` and `variance` (the last two only when required) all have the same number of elements as the number of channels of `x`. This results in heap out of bounds reads when the buffers backing these tensors are indexed past their boundary. If the tensors are empty, the validation mentioned in the above paragraph would also trig
OSV
Heap buffer overflow and undefined behavior in `FusedBatchNorm`
osv·2021-05-21
CVE-2021-29583 [LOW] Heap buffer overflow and undefined behavior in `FusedBatchNorm`
Heap buffer overflow and undefined behavior in `FusedBatchNorm`
### Impact
The implementation of `tf.raw_ops.FusedBatchNorm` is vulnerable to a heap buffer overflow:
```python
import tensorflow as tf
x = tf.zeros([10, 10, 10, 6], dtype=tf.float32)
scale = tf.constant([0.0], shape=[1], dtype=tf.float32)
offset = tf.constant([0.0], shape=[1], dtype=tf.float32)
mean = tf.constant([0.0], shape=[1], dtype=tf.float32)
variance = tf.constant([0.0], shape=[1], dtype=tf.float32)
epsilon = 0.0
exponential_avg_factor = 0.0
data_format = "NHWC"
is_training = False
tf.raw_ops.FusedBatchNorm(
x=x, scale=scale, offset=offset, mean=mean, variance=variance,
epsilon=epsilon, exponential_avg_factor=exponential_avg_factor,
data_format=data_format, is_training=is_training)
```
If the tensors are empty, th
GHSA
Heap buffer overflow and undefined behavior in `FusedBatchNorm`
ghsa·2021-05-21
CVE-2021-29583 [LOW] CWE-125 Heap buffer overflow and undefined behavior in `FusedBatchNorm`
Heap buffer overflow and undefined behavior in `FusedBatchNorm`
### Impact
The implementation of `tf.raw_ops.FusedBatchNorm` is vulnerable to a heap buffer overflow:
```python
import tensorflow as tf
x = tf.zeros([10, 10, 10, 6], dtype=tf.float32)
scale = tf.constant([0.0], shape=[1], dtype=tf.float32)
offset = tf.constant([0.0], shape=[1], dtype=tf.float32)
mean = tf.constant([0.0], shape=[1], dtype=tf.float32)
variance = tf.constant([0.0], shape=[1], dtype=tf.float32)
epsilon = 0.0
exponential_avg_factor = 0.0
data_format = "NHWC"
is_training = False
tf.raw_ops.FusedBatchNorm(
x=x, scale=scale, offset=offset, mean=mean, variance=variance,
epsilon=epsilon, exponential_avg_factor=exponential_avg_factor,
data_format=data_format, is_training=is_training)
```
If the tensors are empty, th
OSV
CVE-2021-29583: TensorFlow is an end-to-end open source platform for machine learning
osv·2021-05-14
CVE-2021-29583 CVE-2021-29583: TensorFlow is an end-to-end open source platform for machine learning
TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.FusedBatchNorm` is vulnerable to a heap buffer overflow. If the tensors are empty, the same implementation can trigger undefined behavior by dereferencing null pointers. The implementation(https://github.com/tensorflow/tensorflow/blob/57d86e0db5d1365f19adcce848dfc1bf89fdd4c7/tensorflow/core/kernels/fused_batch_norm_op.cc) fails to validate that `scale`, `offset`, `mean` and `variance` (the last two only when required) all have the same number of elements as the number of channels of `x`. This results in heap out of bounds reads when the buffers backing these tensors are indexed past their boundary. If the tensors are empty, the validation mentioned in the above paragraph would also trig
No detection rules found.
No public exploits indexed.
No writeups or analysis indexed.
https://github.com/tensorflow/tensorflow/commit/6972f9dfe325636b3db4e0bc517ee22a159365c0https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9xh4-23q4-v6wrhttps://github.com/tensorflow/tensorflow/commit/6972f9dfe325636b3db4e0bc517ee22a159365c0https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9xh4-23q4-v6wr
2021-05-14
Published