{"schema_version":"1.7.5","id":"GHSA-v82p-hv3v-p6qp","published":"2021-08-25T14:42:16Z","modified":"2026-07-08T06:27:33.422814703Z","aliases":["BIT-tensorflow-2021-37665","CVE-2021-37665","PYSEC-2021-287","PYSEC-2021-578","PYSEC-2021-776"],"summary":"Incomplete validation in MKL requantization","details":"### Impact\nDue to incomplete validation in MKL implementation of requantization, an  attacker can trigger undefined behavior via binding a reference to a null pointer or can access data outside the bounds of heap allocated arrays:\n\n```python\nimport tensorflow as tf\n\ntf.raw_ops.RequantizationRangePerChannel(\n  input=[],\n  input_min=[0,0,0,0,0],\n  input_max=[1,1,1,1,1],\n  clip_value_max=1)\n```\n  \nThe [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/mkl/mkl_requantization_range_per_channel_op.cc) does not validate the dimensions of the `input` tensor.\n\nA similar issue occurs in `MklRequantizePerChannelOp`:\n\n```python\nimport tensorflow as tf \nfrom tensorflow.python.ops import gen_math_ops\n\ngen_math_ops.requantize_per_channel(\n  input=[],\n  input_min=[-100,-100,-100,-100,-100],\n  input_max=[-100,-100,-100],\n  requested_output_min=[-100,-100,-100,-100,-100],\n  requested_output_max=[],\n  out_type=tf.int)\n``` \n\nThe [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/mkl/mkl_requantize_per_channel_op.cc) does not perform full validation for all the input arguments.\n\n### Patches\nWe have patched the issue in GitHub commit [9e62869465573cb2d9b5053f1fa02a81fce21d69](https://github.com/tensorflow/tensorflow/commit/9e62869465573cb2d9b5053f1fa02a81fce21d69) and in the Github commit [203214568f5bc237603dbab6e1fd389f1572f5c9](https://github.com/tensorflow/tensorflow/commit/203214568f5bc237603dbab6e1fd389f1572f5c9).\n\nThe fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.\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 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tensorflow/tensorflow"}],"database_specific":{"cwe_ids":["CWE-20"],"github_reviewed":true,"github_reviewed_at":"2021-08-24T13:55:57Z","nvd_published_at":"2021-08-12T23:15:00Z","severity":"HIGH"},"severity":[{"type":"CVSS_V3","score":"CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H"},{"type":"CVSS_V4","score":"CVSS:4.0/AV:L/AC:L/AT:N/PR:L/UI:N/VC:H/VI:H/VA:H/SC:N/SI:N/SA:N"}]}