CVE-2020-15200: Segfault in Tensorflow
First published: Fri Sep 25 2020(Updated: )
### 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](https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/core/kernels/count_ops.cc#L248-L265 ) sets up conditions to cause a heap buffer overflow: ```cc 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](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 is a variant of [GHSA-p5f8-gfw5-33w4](https://github.com/tensorflow/tensorflow/security/advisories/GHSA-p5f8-gfw5-33w4)
Credit: security-advisories@github.com security-advisories@github.com
| Affected Software | Affected Version | How to fix |
|---|---|---|
| Google TensorFlow | =2.3.0 | |
| pip/tensorflow-gpu | =2.3.0 | 2.3.1 |
| pip/tensorflow-cpu | =2.3.0 | 2.3.1 |
| pip/tensorflow | =2.3.0 | 2.3.1 |
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Reference Links
- https://github.com/tensorflow/tensorflow/commit/3cbb917b4714766030b28eba9fb41bb97ce9ee02
- https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-x7rp-74x2-mjf3
- 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/advisories/GHSA-x7rp-74x2-mjf3 (Advisory)
Frequently Asked Questions
What is the severity of CVE-2020-15200?
CVE-2020-15200 has a high severity due to the lack of validation in the RaggedCountSparseOutput implementation affecting input arguments.
How do I fix CVE-2020-15200?
To fix CVE-2020-15200, upgrade TensorFlow to version 2.3.1 or later.
Which versions of TensorFlow are affected by CVE-2020-15200?
CVE-2020-15200 affects TensorFlow version 2.3.0.
What is the impact of CVE-2020-15200?
The impact of CVE-2020-15200 involves improper validation of ragged tensors, which may lead to potential data corruption or unintended behavior.
What packages are vulnerable due to CVE-2020-15200?
The vulnerable packages include tensorflow, tensorflow-gpu, and tensorflow-cpu, all at version 2.3.0.
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