{"id":"CVE-2021-29512","aliases":["GHSA-4278-2v5v-65r4","BIT-tensorflow-2021-29512","PYSEC-2021-149","PYSEC-2021-440","PYSEC-2021-638"],"title":"Heap buffer overflow in `RaggedBinCount`","summary":"Heap buffer overflow in `RaggedBinCount`","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-4278-2v5v-65r4","references":[{"url":"https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4278-2v5v-65r4"},{"url":"https://nvd.nist.gov/vuln/detail/CVE-2021-29512"},{"url":"https://github.com/tensorflow/tensorflow/commit/eebb96c2830d48597d055d247c0e9aebaea94cd5"},{"url":"https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-440.yaml"},{"url":"https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-638.yaml"},{"url":"https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-149.yaml"}],"tags":["osv","pip"],"epss":0.00211,"epssPercentile":0.11653,"ingestedAt":"2026-07-08T18:25:45.224Z","slug":"CVE-2021-29512","body":"## Overview\n\n### Impact\nIf the `splits` argument of `RaggedBincount` does not specify a valid [`SparseTensor`](https://www.tensorflow.org/api_docs/python/tf/sparse/SparseTensor), then an attacker can trigger a heap buffer overflow:\n\n```python\nimport tensorflow as tf\ntf.raw_ops.RaggedBincount(splits=[0], values=[1,1,1,1,1], size=5, weights=[1,2,3,4], binary_output=False)\n```\n\nThis will cause a read from outside the bounds of the `splits` tensor buffer in the [implementation of the `RaggedBincount` op](https://github.com/tensorflow/tensorflow/blob/8b677d79167799f71c42fd3fa074476e0295413a/tensorflow/core/kernels/bincount_op.cc#L430-L433):\n\n```cc\n    for (int idx = 0; idx < num_values; ++idx) {\n      while (idx >= splits(batch_idx)) {\n        batch_idx++;\n      }\n      ...\n    }\n```\n\nBefore the `for` loop, `batch_idx` is set to 0. The user controls the `splits` array, making it contain only one element, 0. Thus, the code in the `while` loop would increment `batch_idx` and then try to read `splits(1)`, which is outside of bounds.\n\n### Patches\nWe have patched the issue in GitHub commit [eebb96c2830d48597d055d247c0e9aebaea94cd5](https://github.com/tensorflow/tensorflow/commit/eebb96c2830d48597d055d247c0e9aebaea94cd5).\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 members of the Aivul Team from Qihoo 360.\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":[]}