{"schema_version":"1.9.0","id":"GHSA-9c84-4hx6-xmm4","published":"2021-05-21T14:28:08Z","modified":"2026-07-08T06:28:10.739267158Z","aliases":["BIT-tensorflow-2021-29601","CVE-2021-29601","PYSEC-2021-238","PYSEC-2021-529","PYSEC-2021-727"],"summary":"Integer overflow in TFLite concatentation","details":"### Impact\nThe TFLite implementation of concatenation is [vulnerable to an integer overflow issue](https://github.com/tensorflow/tensorflow/blob/7b7352a724b690b11bfaae2cd54bc3907daf6285/tensorflow/lite/kernels/concatenation.cc#L70-L76):\n\n```cc\nfor (int d = 0; d < t0->dims->size; ++d) {\n  if (d == axis) { \n    sum_axis += t->dims->data[axis]; \n  } else {\n    TF_LITE_ENSURE_EQ(context, t->dims->data[d], t0->dims->data[d]);\n  }\n}\n```\n\nAn attacker can craft a model such that the dimensions of one of the concatenation input overflow the values of `int`. TFLite uses `int` to represent tensor dimensions, whereas TF uses `int64`. Hence, valid TF models can trigger an integer overflow when converted to TFLite format.\n\n### Patches\nWe have patched the issue in GitHub commit [4253f96a58486ffe84b61c0415bb234a4632ee73](https://github.com/tensorflow/tensorflow/commit/4253f96a58486ffe84b61c0415bb234a4632ee73).\n\nThe fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, 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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