CVE-2021-29552: CHECK-failure in `UnsortedSegmentJoin`
First published: Fri May 14 2021(Updated: )
### Impact An attacker can cause a denial of service by controlling the values of `num_segments` tensor argument for `UnsortedSegmentJoin`: ```python import tensorflow as tf inputs = tf.constant([], dtype=tf.string) segment_ids = tf.constant([], dtype=tf.int32) num_segments = tf.constant([], dtype=tf.int32) separator = '' tf.raw_ops.UnsortedSegmentJoin( inputs=inputs, segment_ids=segment_ids, num_segments=num_segments, separator=separator) ``` This is because the [implementation](https://github.com/tensorflow/tensorflow/blob/a2a607db15c7cd01d754d37e5448d72a13491bdb/tensorflow/core/kernels/unsorted_segment_join_op.cc#L92-L93) assumes that the `num_segments` tensor is a valid scalar: ```cc const Tensor& num_segments_tensor = context->input(2); auto num_segments = num_segments_tensor.scalar<NUM_SEGMENTS_TYPE>()(); ``` Since the tensor is empty the `CHECK` involved in `.scalar<T>()()` that checks that the number of elements is exactly 1 will be invalidated and this would result in process termination. ### Patches We have patched the issue in GitHub commit [704866eabe03a9aeda044ec91a8d0c83fc1ebdbe](https://github.com/tensorflow/tensorflow/commit/704866eabe03a9aeda044ec91a8d0c83fc1ebdbe). 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. ### 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 Ying Wang and Yakun Zhang of Baidu X-Team.
Credit: security-advisories@github.com security-advisories@github.com
| Affected Software | Affected Version | How to fix |
|---|---|---|
| Google TensorFlow | <2.1.4 | |
| Google TensorFlow | >=2.2.0<2.2.3 | |
| Google TensorFlow | >=2.3.0<2.3.3 | |
| Google TensorFlow | >=2.4.0<2.4.2 | |
| 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 |
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Reference Links
- https://github.com/tensorflow/tensorflow/commit/704866eabe03a9aeda044ec91a8d0c83fc1ebdbe
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-jhq9-wm9m-cf89
- https://nvd.nist.gov/vuln/detail/CVE-2021-29552
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-480.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-678.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-189.yaml
- https://github.com/advisories/GHSA-jhq9-wm9m-cf89 (Advisory)
Frequently Asked Questions
What is the severity of CVE-2021-29552?
CVE-2021-29552 is classified as a denial-of-service vulnerability.
How do I fix CVE-2021-29552?
To fix CVE-2021-29552, upgrade TensorFlow to version 2.4.2 or later.
Which TensorFlow versions are affected by CVE-2021-29552?
CVE-2021-29552 affects TensorFlow versions up to 2.1.4 and between 2.2.0 and 2.4.2.
What kind of attack can exploit CVE-2021-29552?
An attacker can exploit CVE-2021-29552 to cause a denial of service by manipulating the `num_segments` tensor argument.
Is CVE-2021-29552 relevant to both TensorFlow CPU and GPU versions?
Yes, CVE-2021-29552 affects both TensorFlow CPU and TensorFlow GPU versions.
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