{"id":"CVE-2021-29554","aliases":["GHSA-qg48-85hg-mqc5","BIT-tensorflow-2021-29554","PYSEC-2021-191","PYSEC-2021-482","PYSEC-2021-680"],"title":"Division by 0 in `DenseCountSparseOutput`","summary":"Division by 0 in `DenseCountSparseOutput`","severity":"low","cvss":2.5,"cvssVector":"CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L","vendor":"tensorflow","product":"tensorflow","ecosystem":"pip","affected":["tensorflow >= 2.3.0, < 2.3.3","tensorflow >= 2.4.0, < 2.4.2","tensorflow-cpu >= 2.3.0, < 2.3.3","tensorflow-cpu >= 2.4.0, < 2.4.2","tensorflow-gpu >= 2.3.0, < 2.3.3","tensorflow-gpu >= 2.4.0, < 2.4.2"],"patched":["tensorflow 2.3.3","tensorflow 2.4.2","tensorflow-cpu 2.3.3","tensorflow-cpu 2.4.2","tensorflow-gpu 2.3.3","tensorflow-gpu 2.4.2"],"published":"2021-05-21","updated":"2026-07-08","source":"OSV","sourceUrl":"https://osv.dev/vulnerability/GHSA-qg48-85hg-mqc5","references":[{"url":"https://github.com/tensorflow/tensorflow/security/advisories/GHSA-qg48-85hg-mqc5"},{"url":"https://nvd.nist.gov/vuln/detail/CVE-2021-29554"},{"url":"https://github.com/tensorflow/tensorflow/commit/da5ff2daf618591f64b2b62d9d9803951b945e9f"},{"url":"https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-482.yaml"},{"url":"https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-680.yaml"},{"url":"https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-191.yaml"},{"url":"https://github.com/tensorflow/tensorflow"}],"tags":["osv","pip"],"epss":0.00189,"epssPercentile":0.08773,"ingestedAt":"2026-07-08T18:25:52.410Z","slug":"CVE-2021-29554","body":"## Overview\n\n### Impact\nAn attacker can cause a denial of service via a FPE runtime error in `tf.raw_ops.DenseCountSparseOutput`:\n\n```python\nimport tensorflow as tf\n\nvalues = tf.constant([], shape=[0, 0], dtype=tf.int64)\nweights = tf.constant([])\n\ntf.raw_ops.DenseCountSparseOutput(\n  values=values, weights=weights,\n  minlength=-1, maxlength=58, binary_output=True)\n```\n  \nThis is because the [implementation](https://github.com/tensorflow/tensorflow/blob/efff014f3b2d8ef6141da30c806faf141297eca1/tensorflow/core/kernels/count_ops.cc#L123-L127) computes a divisor value from user data but does not check that the result is 0 before doing the division:\n\n```cc\nint num_batch_elements = 1;\nfor (int i = 0; i < num_batch_dimensions; ++i) {\n  num_batch_elements *= data.shape().dim_size(i);\n}\nint num_value_elements = data.shape().num_elements() / num_batch_elements;\n```\n\nSince `data` is given by the `values` argument, `num_batch_elements` is 0.\n\n### Patches\nWe have patched the issue in GitHub commit [da5ff2daf618591f64b2b62d9d9803951b945e9f](https://github.com/tensorflow/tensorflow/commit/da5ff2daf618591f64b2b62d9d9803951b945e9f).\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, as these are also affected.\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.\n\n## Affected packages\n\n- `tensorflow >= 2.3.0, < 2.3.3`\n- `tensorflow >= 2.4.0, < 2.4.2`\n- `tensorflow-cpu >= 2.3.0, < 2.3.3`\n- `tensorflow-cpu >= 2.4.0, < 2.4.2`\n- `tensorflow-gpu >= 2.3.0, < 2.3.3`\n- `tensorflow-gpu >= 2.4.0, < 2.4.2`\n\n## Remediation\n\nUpgrade to a patched release:\n\n- `tensorflow 2.3.3`\n- `tensorflow 2.4.2`\n- `tensorflow-cpu 2.3.3`\n- `tensorflow-cpu 2.4.2`\n- `tensorflow-gpu 2.3.3`\n- `tensorflow-gpu 2.4.2`","depth":"sunlit","depthScore":14,"depthScoreParts":{"impact":13.8,"likelihood":0,"exploitation":0,"ransomware":0},"changes":[]}