CVE-2021-29550
Description
TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a runtime division by zero error and denial of service in `tf.raw_ops.FractionalAvgPool`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/acc8ee69f5f46f92a3f1f11230f49c6ac266f10c/tensorflow/core/kernels/fractional_avg_pool_op.cc#L85-L89) computes a divisor quantity by dividing two user controlled values. The user controls the values of `input_size[i]` and `pooling_ratio_[i]` (via the `value.shape()` and `pooling_ratio` arguments). If the value in `input_size[i]` is smaller than the `pooling_ratio_[i]`, then the floor operation results in `output_size[i]` being 0. The `DCHECK_GT` line is a no-op outside of debug mode, so in released versions of TF this does not trigger. Later, these computed values are used as arguments(https://github.com/tensorflow/tensorflow/blob/acc8ee69f5f46f92a3f1f11230f49c6ac266f10c/tensorflow/core/kernels/fractional_avg_pool_op.cc#L96-L99) to `GeneratePoolingSequence`(https://github.com/tensorflow/tensorflow/blob/acc8ee69f5f46f92a3f1f11230f49c6ac266f10c/tensorflow/core/kernels/fractional_pool_common.cc#L100-L108). There, the first computation is a division in a modulo operation. Since `output_length` can be 0, this results in runtime crashing. 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.
Predictions
Heuristic predictions, AS-IS, for prioritization only.
Mitigations
No mitigations published for this CVE yet.
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OS impact
Arch Fixed 1 release
| Version | Status | Fixed in |
|---|---|---|
| — | Fixed | 2.5.0-1 |
Debian Fixed 2 releases
| Version | Status | Fixed in |
|---|---|---|
| sid | Fixed | 0 |
| forky | Fixed | 0 |
Package impact
| Ecosystem | Package | Vulnerable | Fixed |
|---|---|---|---|
| PyPI | tensorflow | <2.1.4 | 2.1.4 |
| PyPI | tensorflow | >=2.2.0,<2.2.3 | 2.2.3 |
| PyPI | tensorflow | >=2.3.0,<2.3.3 | 2.3.3 |
| PyPI | tensorflow | >=2.4.0,<2.4.2 | 2.4.2 |
| PyPI | tensorflow-cpu | <2.1.4 | 2.1.4 |
| PyPI | tensorflow-cpu | >=2.2.0,<2.2.3 | 2.2.3 |
| PyPI | tensorflow-cpu | >=2.3.0,<2.3.3 | 2.3.3 |
| PyPI | tensorflow-cpu | >=2.4.0,<2.4.2 | 2.4.2 |
| PyPI | tensorflow-gpu | <2.1.4 | 2.1.4 |
| PyPI | tensorflow-gpu | >=2.2.0,<2.2.3 | 2.2.3 |
| PyPI | tensorflow-gpu | >=2.3.0,<2.3.3 | 2.3.3 |
| PyPI | tensorflow-gpu | >=2.4.0,<2.4.2 | 2.4.2 |
| PyPI | tensorflow-cpu | <548b5eaf23685d86f722233d8fbc21d0a4aecb96||>=2.4.0,<2.4.3 | 548b5eaf23685d86f722233d8fbc21d0a4aecb96 |
| PyPI | tensorflow | <548b5eaf23685d86f722233d8fbc21d0a4aecb96||>=2.4.0,<2.4.3 | 548b5eaf23685d86f722233d8fbc21d0a4aecb96 |
| PyPI | tensorflow-gpu | <548b5eaf23685d86f722233d8fbc21d0a4aecb96||>=2.4.0,<2.4.3 | 548b5eaf23685d86f722233d8fbc21d0a4aecb96 |
References
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-f78g-q7r4-9wcv
- https://nvd.nist.gov/vuln/detail/CVE-2021-29550
- https://github.com/tensorflow/tensorflow/commit/548b5eaf23685d86f722233d8fbc21d0a4aecb96
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-478.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-676.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-187.yaml
- https://github.com/tensorflow/tensorflow
- https://security-tracker.debian.org/tracker/CVE-2021-29550
Community-verified mitigations for this CVE will appear above when contributors publish them.
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