{"id":"GHSA-hr84-fqvp-48mm","summary":"Segfault in SparseCountSparseOutput","details":"### Impact\nSpecifying 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.\n\n```python\nimport tensorflow as tf\n\nindices = tf.constant([], shape=[0, 0], dtype=tf.int64)\nvalues = tf.constant([], shape=[0, 0], dtype=tf.int64)\ndense_shape = tf.constant([-100, -100, -100], shape=[3], dtype=tf.int64)\nweights = tf.constant([], shape=[0, 0], dtype=tf.int64)\n\ntf.raw_ops.SparseCountSparseOutput(indices=indices, values=values, dense_shape=dense_shape, weights=weights, minlength=79, maxlength=96, binary_output=False)\n```\n\nThis 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\u003cT\u003e` (i.e., [`std::vector\u003cabsl::flat_hash_map\u003cint64,T\u003e\u003e`](https://github.com/tensorflow/tensorflow/blob/8f7b60ee8c0206a2c99802e3a4d1bb55d2bc0624/tensorflow/core/kernels/count_ops.cc#L27)) data structure.\n\n```cc\n  bool is_1d = shape.NumElements() == 1;\n  int num_batches = is_1d ? 1 : shape.flat\u003cint64\u003e()(0);\n  ...\n  auto per_batch_counts = BatchedMap\u003cW\u003e(num_batches); \n```\n\nIf the `shape` tensor has more than one element, `num_batches` is the first value in `shape`.\n                       \nEnsuring that the `dense_shape` argument is a valid tensor shape (that is, all elements are non-negative) solves this issue.\n\n### Patches\nWe have patched the issue in GitHub commit [c57c0b9f3a4f8684f3489dd9a9ec627ad8b599f5](https://github.com/tensorflow/tensorflow/commit/c57c0b9f3a4f8684f3489dd9a9ec627ad8b599f5).\n\nThe 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.\n\n### For more information\nPlease 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.\n\n### Attribution\nThis vulnerability has been reported by Yakun Zhang and Ying Wang of Baidu X-Team.","aliases":["BIT-tensorflow-2021-29521","CVE-2021-29521","PYSEC-2021-158","PYSEC-2021-449","PYSEC-2021-647"],"modified":"2026-09-10T03:49:14.741706917Z","published":"2021-05-21T14:21:16Z","database_specific":{"severity":"LOW","github_reviewed":true,"github_reviewed_at":"2021-05-18T23:23:47Z","nvd_published_at":"2021-05-14T20:15:00Z","cwe_ids":["CWE-131"]},"references":[{"type":"WEB","url":"https://github.com/tensorflow/tensorflow/security/advisories/GHSA-hr84-fqvp-48mm"},{"type":"ADVISORY","url":"https://nvd.nist.gov/vuln/detail/CVE-2021-29521"},{"type":"WEB","url":"https://github.com/tensorflow/tensorflow/commit/c57c0b9f3a4f8684f3489dd9a9ec627ad8b599f5"},{"type":"WEB","url":"https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-449.yaml"},{"type":"WEB","url":"https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-647.yaml"},{"type":"WEB","url":"https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-158.yaml"}],"affected":[{"package":{"name":"tensorflow","ecosystem":"PyPI","purl":"pkg:pypi/tensorflow"},"ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"2.3.0"},{"fixed":"2.3.3"}]}],"versions":["2.3.0","2.3.1","2.3.2"],"database_specific":{"source":"https://github.com/github/advisory-database/blob/main/advisories/github-reviewed/2021/05/GHSA-hr84-fqvp-48mm/GHSA-hr84-fqvp-48mm.json"}},{"package":{"name":"tensorflow","ecosystem":"PyPI","purl":"pkg:pypi/tensorflow"},"ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"2.4.0"},{"fixed":"2.4.2"}]}],"versions":["2.4.0","2.4.1"],"database_specific":{"source":"https://github.com/github/advisory-database/blob/main/advisories/github-reviewed/2021/05/GHSA-hr84-fqvp-48mm/GHSA-hr84-fqvp-48mm.json"}},{"package":{"name":"tensorflow-cpu","ecosystem":"PyPI","purl":"pkg:pypi/tensorflow-cpu"},"ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"2.3.0"},{"fixed":"2.3.3"}]}],"versions":["2.3.0","2.3.1","2.3.2"],"database_specific":{"source":"https://github.com/github/advisory-database/blob/main/advisories/github-reviewed/2021/05/GHSA-hr84-fqvp-48mm/GHSA-hr84-fqvp-48mm.json"}},{"package":{"name":"tensorflow-cpu","ecosystem":"PyPI","purl":"pkg:pypi/tensorflow-cpu"},"ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"2.4.0"},{"fixed":"2.4.2"}]}],"versions":["2.4.0","2.4.1"],"database_specific":{"source":"https://github.com/github/advisory-database/blob/main/advisories/github-reviewed/2021/05/GHSA-hr84-fqvp-48mm/GHSA-hr84-fqvp-48mm.json"}},{"package":{"name":"tensorflow-gpu","ecosystem":"PyPI","purl":"pkg:pypi/tensorflow-gpu"},"ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"2.3.0"},{"fixed":"2.3.3"}]}],"versions":["2.3.0","2.3.1","2.3.2"],"database_specific":{"source":"https://github.com/github/advisory-database/blob/main/advisories/github-reviewed/2021/05/GHSA-hr84-fqvp-48mm/GHSA-hr84-fqvp-48mm.json"}},{"package":{"name":"tensorflow-gpu","ecosystem":"PyPI","purl":"pkg:pypi/tensorflow-gpu"},"ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"2.4.0"},{"fixed":"2.4.2"}]}],"versions":["2.4.0","2.4.1"],"database_specific":{"source":"https://github.com/github/advisory-database/blob/main/advisories/github-reviewed/2021/05/GHSA-hr84-fqvp-48mm/GHSA-hr84-fqvp-48mm.json"}}],"schema_version":"1.9.0","severity":[{"type":"CVSS_V3","score":"CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L"},{"type":"CVSS_V4","score":"CVSS:4.0/AV:N/AC:L/AT:P/PR:L/UI:N/VC:N/VI:N/VA:L/SC:N/SI:N/SA:N"}]}