Segfault in Tensorflow
Impact
The RaggedCountSparseOutput implementation does not validate that the input arguments form a valid ragged tensor. In particular, there is no validation that the values in the splits tensor generate a valid partitioning of the values tensor. Thus, the following code sets up conditions to cause a heap buffer overflow:
auto per_batch_counts = BatchedMap<W>(num_batches);
int batch_idx = 0;
for (int idx = 0; idx < num_values; ++idx) {
while (idx >= splits_values(batch_idx)) {
batch_idx++;
}
const auto& value = values_values(idx);
if (value >= 0 && (maxlength_ <= 0 || value < maxlength_)) {
per_batch_counts[batch_idx - 1][value] = 1;
}
}
A BatchedMap is equivalent to a vector where each element is a hashmap. However, if the first element of splits_values is not 0, batch_idx will never be 1, hence there will be no hashmap at index 0 in per_batch_counts. Trying to access that in the user code results in a segmentation fault.
Patches
We have patched the issue in 3cbb917b4714766030b28eba9fb41bb97ce9ee02 and will release a patch release.
We recommend users to upgrade to TensorFlow 2.3.1.
For more information
Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.
Attribution
This vulnerability is a variant of GHSA-p5f8-gfw5-33w4
Affected software
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| Platform | Package | Vulnerable versions | Fixed in |
|---|---|---|---|
| Python (PyPI) | tensorflow | >= 2.3.0, < 2.3.1 | 2.3.1 |
| Python (PyPI) | tensorflow-cpu | >= 2.3.0, < 2.3.1 | 2.3.1 |
| Python (PyPI) | tensorflow-gpu | >= 2.3.0, < 2.3.1 | 2.3.1 |
References
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-x7rp-74x2-mjf3
- https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1
- https://github.com/tensorflow/tensorflow/commit/3cbb917b4714766030b28eba9fb41bb97ce9ee02
- https://nvd.nist.gov/vuln/detail/CVE-2020-15200
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2020-280.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2020-315.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2020-123.yaml
- https://github.com/tensorflow/tensorflow
- CVSS vector
CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:N/I:N/A:H- Weakness (CWE)
CWE-122, CWE-20, CWE-787- Platform
- Python (PyPI)
- Package
tensorflow, tensorflow-cpu, tensorflow-gpu- Disclosed
- 25.09.2020
- Updated
- 11.10.2026 13:19
Sources and licenses
OSV / GitHub Advisory Database - PYSEC-2020-123 - PyPA Advisory Database; CC BY 4.0
OSV / GitHub Advisory Database - PYSEC-2020-280 - PyPA Advisory Database; CC BY 4.0
OSV / GitHub Advisory Database - PYSEC-2020-315 - PyPA Advisory Database; CC BY 4.0
OSV / GitHub Advisory Database - GHSA-x7rp-74x2-mjf3 - CC BY 4.0
Vulnerability history: tensorflow-gpu
All published vulnerabilities of this package (Python (PyPI)), newest first. Shows whether this is a one-off slip or a package that needs watching.
- Total entries
- 427
- First → latest
- 04.2019 07.2024
- Without a fix
- 0
- Exploited in attacks
- 0
Severity breakdown
- Critical 6
- High 109
- Medium 207
- Low 105
Disclosures per year
- 2019: 7
- 2020: 35
- 2021: 200
- 2022: 166
- 2023: 18
- 2024: 1
- 2025: 0
- 2026: 0
-
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