GitHub Advisoryadvisory · PyPI
GHSA-g8wg-cjwc-xhhp Heap OOB in nested `tf.map_fn` with `RaggedTensor`s
HIGH
Official page ↗
- Aliases
- CVE-2021-37679
- Ecosystem
- PyPI
- Packages
- tensorflow, tensorflow-cpu, tensorflow-gpu
- Severity
- High
- CVSS vector
- CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:N
- Weaknesses
- CWE-125, CWE-681
- Reviewed
- 2021-08-24
Affected packages
• PyPI tensorflow: < 2.3.4 → fixed in 2.3.4
• PyPI tensorflow: >= 2.4.0, < 2.4.3 → fixed in 2.4.3
• PyPI tensorflow: >= 2.5.0, < 2.5.1 → fixed in 2.5.1
• PyPI tensorflow-cpu: < 2.3.4 → fixed in 2.3.4
• PyPI tensorflow-cpu: >= 2.4.0, < 2.4.3 → fixed in 2.4.3
• PyPI tensorflow-cpu: >= 2.5.0, < 2.5.1 → fixed in 2.5.1
• PyPI tensorflow-gpu: < 2.3.4 → fixed in 2.3.4
• PyPI tensorflow-gpu: >= 2.4.0, < 2.4.3 → fixed in 2.4.3
• PyPI tensorflow-gpu: >= 2.5.0, < 2.5.1 → fixed in 2.5.1
Description
Impact
It is possible to nest a `tf.map_fn` within another `tf.map_fn` call. However, if the input tensor is a `RaggedTensor` and there is no function signature provided, code assumes the output is a fully specified tensor and fills output buffer with uninitialized contents from the heap:
```python
import tensorflow as tf
x = tf.ragged.constant([[1,2,3], [4,5], [6]])
t = tf.map_fn(lambda r: tf.map_fn(lambda y: r, r), x)
z = tf.ragged.constant([[[1,2,3],[1,2,3],[1,2,3]],[[4,5],[4,5]],[[6]]])
```
The `t` and `z` outputs should be identical, however this is not the case. The last row of `t` contains data from the heap which can be used to leak other memory information.
The bug lies in the conversion from a `Variant` tensor to a `RaggedTensor`. The implementation does not check that all inner shapes match and this results in the additional dimensions in the above example.
The same implementation can result in data loss, if input tensor is tweaked:
```python
import tensorflow as tf
x = tf.ragged.constant([[1,2], [3,4,5], [6]])
t = tf.map_fn(lambda r: tf.map_fn(lambda y: r, r), x)
```
Here, the output tensor will only have 2 elements for each inner dimension.
Patches
We have patched the issue in GitHub commit 4e2565483d0ffcadc719bd44893fb7f609bb5f12.
The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.
For more information
Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.
Attribution
This vulnerability has been reported by Haris Sahovic.
Related
Alias · CVE (1)
- CVE-2021-37679 TensorFlow is an end-to-end open source platform for machine learning. vulnerability
References
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-g8wg-cjwc-xhhp web
- https://nvd.nist.gov/vuln/detail/CVE-2021-37679 advisory
- https://github.com/tensorflow/tensorflow/commit/4e2565483d0ffcadc719bd44893fb7f609bb5f12 web
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-592.yaml web
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-790.yaml web
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-301.yaml web
- https://github.com/tensorflow/tensorflow package