Advisoriesby IbisSegfault 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](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...
Affected versions
| Package | Affected | Fixed in |
|---|---|---|
| tensorflow PyPI | >= 2.3.0, < 2.3.1 | 2.3.1 |
Details and references
### 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)
- CVSS 3.1
- CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:N/I:N/A:H
- Severity from
- GitHub (reviewed advisory)
- Weakness
- CWE-122, CWE-20, CWE-787
- Also known as
- BIT-tensorflow-2020-15200, CVE-2020-15200, PYSEC-2020-123, PYSEC-2020-280, PYSEC-2020-315
- github.com/tensorflow/tensorflow/security/advisories/GHSA-x7rp-74x2-mjf3
- nvd.nist.gov/vuln/detail/CVE-2020-15200
- github.com/tensorflow/tensorflow/commit/3cbb917b4714766030b28eba9fb41bb97ce9ee02
- github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2020-280.yaml
- github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2020-315.yaml
- github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2020-123.yaml
- github.com/tensorflow/tensorflow
- github.com/tensorflow/tensorflow/releases/tag/v2.3.1
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