CVE-2021-37672: Heap OOB in `SdcaOptimizerV2` in TensorFlow
First published: Thu Aug 12 2021(Updated: )
### Impact An attacker can read from outside of bounds of heap allocated data by sending specially crafted illegal arguments to `tf.raw_ops.SdcaOptimizerV2`: ```python import tensorflow as tf tf.raw_ops.SdcaOptimizerV2( sparse_example_indices=[[1]], sparse_feature_indices=[[1]], sparse_feature_values=[[1.0,2.0]], dense_features=[[1.0]], example_weights=[1.0], example_labels=[], sparse_indices=[1], sparse_weights=[1.0], dense_weights=[[1.0]], example_state_data=[[100.0,100.0,100.0,100.0]], loss_type='logistic_loss', l1=100.0, l2=100.0, num_loss_partitions=1, num_inner_iterations=1, adaptive=True) ``` The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/sdca_internal.cc#L320-L353) does not check that the length of `example_labels` is the same as the number of examples. ### Patches We have patched the issue in GitHub commit [a4e138660270e7599793fa438cd7b2fc2ce215a6](https://github.com/tensorflow/tensorflow/commit/a4e138660270e7599793fa438cd7b2fc2ce215a6). 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](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 members of the Aivul Team from Qihoo 360.
Credit: security-advisories@github.com security-advisories@github.com
| Affected Software | Affected Version | How to fix |
|---|---|---|
| Google TensorFlow | >=2.3.0<2.3.4 | |
| Google TensorFlow | >=2.4.0<2.4.3 | |
| Google TensorFlow | =2.5.0 | |
| Google TensorFlow | =2.6.0-rc0 | |
| Google TensorFlow | =2.6.0-rc1 | |
| Google TensorFlow | =2.6.0-rc2 | |
| pip/tensorflow-gpu | =2.5.0 | 2.5.1 |
| pip/tensorflow-gpu | >=2.4.0<2.4.3 | 2.4.3 |
| pip/tensorflow-gpu | <2.3.4 | 2.3.4 |
| pip/tensorflow-cpu | =2.5.0 | 2.5.1 |
| pip/tensorflow-cpu | >=2.4.0<2.4.3 | 2.4.3 |
| pip/tensorflow-cpu | <2.3.4 | 2.3.4 |
| pip/tensorflow | =2.5.0 | 2.5.1 |
| pip/tensorflow | >=2.4.0<2.4.3 | 2.4.3 |
| pip/tensorflow | <2.3.4 | 2.3.4 |
| >=2.3.0<2.3.4 | ||
| >=2.4.0<2.4.3 | ||
| =2.5.0 | ||
| =2.6.0-rc0 | ||
| =2.6.0-rc1 | ||
| =2.6.0-rc2 |
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Reference Links
- https://github.com/tensorflow/tensorflow/commit/a4e138660270e7599793fa438cd7b2fc2ce215a6
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-5hj3-vjjf-f5m7
- https://nvd.nist.gov/vuln/detail/CVE-2021-37672
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-585.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-783.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-294.yaml
- https://github.com/advisories/GHSA-5hj3-vjjf-f5m7 (Advisory)
Frequently Asked Questions
What is the severity of CVE-2021-37672?
CVE-2021-37672 is considered a medium severity vulnerability as it allows attackers to read out-of-bounds memory segments.
How do I fix CVE-2021-37672?
To remediate CVE-2021-37672, update TensorFlow to version 2.5.1 or later, or to version 2.4.3 if you are using TensorFlow 2.4.x.
What versions of TensorFlow are affected by CVE-2021-37672?
CVE-2021-37672 affects TensorFlow versions from 2.3.0 to 2.4.3, including version 2.5.0 and release candidates of 2.6.0.
What type of exploit is associated with CVE-2021-37672?
The exploit for CVE-2021-37672 involves sending specially crafted illegal arguments to the tf.raw_ops.SdcaOptimizerV2 function.
What are the potential impacts of CVE-2021-37672?
The potential impact of CVE-2021-37672 includes unauthorized access to sensitive information due to heap memory read vulnerabilities.
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