CVE-2021-29518: Session operations in eager mode lead to null pointer dereferences
First published: Fri May 14 2021(Updated: )
### Impact In eager mode (default in TF 2.0 and later), session operations are invalid. However, users could still call the raw ops associated with them and trigger a null pointer dereference: ```python import tensorflow as tf tf.raw_ops.GetSessionTensor(handle=['\x12\x1a\x07'],dtype=4) ``` ```python import tensorflow as tf tf.raw_ops.DeleteSessionTensor(handle=['\x12\x1a\x07']) ``` The [implementation](https://github.com/tensorflow/tensorflow/blob/eebb96c2830d48597d055d247c0e9aebaea94cd5/tensorflow/core/kernels/session_ops.cc#L104) dereferences the session state pointer without checking if it is valid: ```cc OP_REQUIRES_OK(ctx, ctx->session_state()->GetTensor(name, &val)); ``` Thus, in eager mode, `ctx->session_state()` is nullptr and the call of the member function is undefined behavior. ### Patches We have patched the issue in GitHub commit [ff70c47a396ef1e3cb73c90513da4f5cb71bebba](https://github.com/tensorflow/tensorflow/commit/ff70c47a396ef1e3cb73c90513da4f5cb71bebba). 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 members of the Aivul Team from Qihoo 360.
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/ff70c47a396ef1e3cb73c90513da4f5cb71bebba
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-62gx-355r-9fhg
- https://nvd.nist.gov/vuln/detail/CVE-2021-29518
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-446.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-644.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-155.yaml
- https://github.com/advisories/GHSA-62gx-355r-9fhg (Advisory)
Frequently Asked Questions
What is the severity of CVE-2021-29518?
CVE-2021-29518 has a severity rating that may vary based on its impact but generally indicates a significant risk due to potential null pointer dereference.
How do I fix CVE-2021-29518?
You can resolve CVE-2021-29518 by upgrading TensorFlow to version 2.4.2 or later.
Which TensorFlow versions are affected by CVE-2021-29518?
CVE-2021-29518 affects TensorFlow versions prior to 2.4.2, specifically those below 2.1.4, between 2.2.0 and 2.2.3, and between 2.3.0 and 2.3.3.
What is the risk of exploiting CVE-2021-29518?
Exploiting CVE-2021-29518 can lead to application crashes due to null pointer dereference during session operations.
Is there any workaround for CVE-2021-29518?
There is no known workaround for CVE-2021-29518 other than upgrading to a fixed version of TensorFlow.
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