{"id":"CVE-2020-15193","aliases":["GHSA-rjjg-hgv6-h69v","BIT-tensorflow-2020-15193","PYSEC-2020-116","PYSEC-2020-273","PYSEC-2020-308"],"title":"Memory corruption in Tensorflow","summary":"Memory corruption in Tensorflow","severity":"high","cvss":7.1,"cvssVector":"CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:H/A:L","vendor":"tensorflow","product":"tensorflow","ecosystem":"pip","affected":["tensorflow >= 2.2.0, < 2.2.1","tensorflow >= 2.3.0, < 2.3.1","tensorflow-cpu >= 2.2.0, < 2.2.1","tensorflow-cpu >= 2.3.0, < 2.3.1","tensorflow-gpu >= 2.2.0, < 2.2.1","tensorflow-gpu >= 2.3.0, < 2.3.1"],"patched":["tensorflow 2.2.1","tensorflow 2.3.1","tensorflow-cpu 2.2.1","tensorflow-cpu 2.3.1","tensorflow-gpu 2.2.1","tensorflow-gpu 2.3.1"],"published":"2020-09-25","updated":"2026-07-08","source":"OSV","sourceUrl":"https://osv.dev/vulnerability/GHSA-rjjg-hgv6-h69v","references":[{"url":"https://github.com/tensorflow/tensorflow/security/advisories/GHSA-rjjg-hgv6-h69v"},{"url":"https://nvd.nist.gov/vuln/detail/CVE-2020-15193"},{"url":"https://github.com/tensorflow/tensorflow/commit/22e07fb204386768e5bcbea563641ea11f96ceb8"},{"url":"https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2020-273.yaml"},{"url":"https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2020-308.yaml"},{"url":"https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2020-116.yaml"},{"url":"https://github.com/tensorflow/tensorflow"},{"url":"https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1"},{"url":"http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html"}],"tags":["osv","pip"],"epss":0.0083,"epssPercentile":0.55691,"ingestedAt":"2026-07-08T18:25:52.744Z","slug":"CVE-2020-15193","body":"## Overview\n\n### Impact\nThe implementation of `dlpack.to_dlpack` can be made to use uninitialized memory resulting in further memory corruption. This is because the pybind11 glue code assumes that the argument is a tensor:\nhttps://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/python/tfe_wrapper.cc#L1361\n\nHowever, there is nothing stopping users from passing in a Python object instead of a tensor.\n```python\nIn [2]: tf.experimental.dlpack.to_dlpack([2])                                                                                                                                            \n==1720623==WARNING: MemorySanitizer: use-of-uninitialized-value                                                                                                                            \n    #0 0x55b0ba5c410a in tensorflow::(anonymous namespace)::GetTensorFromHandle(TFE_TensorHandle*, TF_Status*) third_party/tensorflow/c/eager/dlpack.cc:46:7\n    #1 0x55b0ba5c38f4 in tensorflow::TFE_HandleToDLPack(TFE_TensorHandle*, TF_Status*) third_party/tensorflow/c/eager/dlpack.cc:252:26\n... \n```\n\nThe uninitialized memory address is due to a `reinterpret_cast`\nhttps://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/python/eager/pywrap_tensor.cc#L848-L850\n\nSince the `PyObject` is a Python object, not a TensorFlow Tensor, the cast to `EagerTensor` fails. \n\n### Patches\nWe have patched the issue in 22e07fb204386768e5bcbea563641ea11f96ceb8 and will release a patch release for all affected versions.\n\nWe recommend users to upgrade to TensorFlow 2.2.1 or 2.3.1.\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 >= 2.2.0, < 2.2.1`\n- `tensorflow >= 2.3.0, < 2.3.1`\n- `tensorflow-cpu >= 2.2.0, < 2.2.1`\n- `tensorflow-cpu >= 2.3.0, < 2.3.1`\n- `tensorflow-gpu >= 2.2.0, < 2.2.1`\n- `tensorflow-gpu >= 2.3.0, < 2.3.1`\n\n## Remediation\n\nUpgrade to a patched release:\n\n- `tensorflow 2.2.1`\n- `tensorflow 2.3.1`\n- `tensorflow-cpu 2.2.1`\n- `tensorflow-cpu 2.3.1`\n- `tensorflow-gpu 2.2.1`\n- `tensorflow-gpu 2.3.1`","depth":"twilight","depthScore":39,"depthScoreParts":{"impact":39.1,"likelihood":0.2,"exploitation":0,"ransomware":0},"changes":[]}