CVE-2021-29521
Segfault in SparseCountSparseOutput
Impact
### Impact Specifying a negative dense shape in `tf.raw_ops.SparseCountSparseOutput` results in a segmentation fault being thrown out from the standard library as `std::vector` invariants are broken. ```python import tensorflow as tf indices = tf.constant([], shape=[0, 0], dtype=tf.int64) values = tf.constant([], shape=[0, 0], dtype=tf.int64) dense_shape = tf.constant([-100, -100, -100], shape=[3], dtype=tf.int64) weights = tf.constant([], shape=[0, 0], dtype=tf.int64) tf.raw_ops.SparseCountSparseOutput(indices=indices, values=values, dense_shape=dense_shape, weights=weights, minlength=79, maxlength=96, binary_output=False) ``` This is because the [implementation](https://github.com/tensorflow/tensorflow/blob/8f7b60ee8c0206a2c99802e3a4d1bb55d2bc0624/tensorflow/core/kernels/count_ops.cc#L199-L213) assumes the first element of the dense shape is always positive and uses it to initialize a `BatchedMap<T>` (i.e., [`std::vector<absl::flat_hash_map<int64,T>>`](https://github.com/tensorflow/tensorflow/blob/8f7b60ee8c0206a2c99802e3a4d1bb55d2bc0624/tensorflow/core/kernels/count_ops.cc#L27)) data structure. ```cc bool is_1d = shape.NumElements() == 1; int num_batches = is_1d ? 1 : shape.flat<int64>()(0); ... auto per_batch_counts = BatchedMap<W>(num_batches); ``` If the `shape` tensor has more than one element, `num_batches` is the first value in `shape`. Ensuring that the `dense_shape` argument is a valid tensor shape (that is, all elements are non-negative) solves this issue. ### Patches We have patched the issue in GitHub commit [c57c0b9f3a4f8684f3489dd9a9ec627ad8b599f5](https://github.com/tensorflow/tensorflow/commit/c57c0b9f3a4f8684f3489dd9a9ec627ad8b599f5). The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2 and TensorFlow 2.3.3. ### 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 Yakun Zhang and Ying Wang of Baidu X-Team.
Affected packages
| Ecosystem | Package | Affected | Fixed in |
|---|---|---|---|
| pip | tensorflow | >= 2.3.0, < 2.3.3 | 2.3.3 |
| pip | tensorflow-cpu | >= 2.3.0, < 2.3.3 | 2.3.3 |
| pip | tensorflow-gpu | >= 2.3.0, < 2.3.3 | 2.3.3 |
Affected products
| Vendor | Product | Affected versions |
|---|---|---|
| Tensorflow | from 2.3.0 before 2.3.3 from 2.4.0 before 2.4.2 |
Weakness classification
References
- NVD record
- CVE.org record
- GitHub advisory
- https://github.com/tensorflow/tensorflow/commit/c57c0b9f3a4f8684f3489dd9a9ec627ad8b599f5
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-hr84-fqvp-48mm
- https://github.com/advisories/GHSA-hr84-fqvp-48mm
- https://nvd.nist.gov/vuln/detail/CVE-2021-29521
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-449.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-647.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-158.yaml