{"id":"CVE-2022-21727","aliases":["BIT-tensorflow-2022-21727","EUVD-2022-0290","GHSA-c6fh-56w7-fvjw","PYSEC-2022-106","PYSEC-2022-51","PYSEC-2026-3159","openSUSE-SU-2024:12116-1"],"status":"published","title":"Integer overflow in Tensorflow","description":"Tensorflow is an Open Source Machine Learning Framework. The implementation of shape inference for `Dequantize` is vulnerable to an integer overflow weakness. The `axis` argument can be `-1` (the default value for the optional argument) or any other positive value at most the number of dimensions of the input. Unfortunately, the upper bound is not checked, and, since the code computes `axis + 1`, an attacker can trigger an integer overflow. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.","severity":{"version":"3.1","score":7.6,"vector":"CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:L/I:L/A:H","level":"high","assessor":"cna","from":"cve"},"epss":{"day":"2026-11-12","score":0.00665,"percentile":0.52271,"model":"v2026.06.15"},"facts":{"in_kev":false,"known_exploited":false,"public_exploit":false,"fix_available":true},"cwes":["CWE-190"],"published":"2022-02-03T11:07:40Z","modified":"2026-10-28T10:25:11Z","fixes":[{"ecosystem":"PyPI","package":"tensorflow","fixed_in":["2.5.3","2.6.3","2.7.1"],"sources":["ghsa","osv"]},{"ecosystem":"PyPI","package":"tensorflow-cpu","fixed_in":["2.5.3","2.6.3","2.7.1"],"sources":["ghsa","osv"]},{"ecosystem":"PyPI","package":"tensorflow-gpu","fixed_in":["2.5.3","2.6.3","2.7.1"],"sources":["ghsa","osv"]},{"ecosystem":"Bitnami","package":"tensorflow","fixed_in":["2.5.3","2.6.3","2.7.1"],"sources":["osv"]}],"affected":[{"source":"ghsa","ecosystem":"PyPI","package":"tensorflow","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"0"},{"fixed":"2.5.3"}]}]},{"source":"ghsa","ecosystem":"PyPI","package":"tensorflow","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"2.6.0"},{"fixed":"2.6.3"}]}]},{"source":"ghsa","ecosystem":"PyPI","package":"tensorflow","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"2.7.0"},{"last_affected":"2.7.0"},{"fixed":"2.7.1"}]}],"versions":["2.7.0"]},{"source":"ghsa","ecosystem":"PyPI","package":"tensorflow-cpu","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"0"},{"fixed":"2.5.3"}]}]},{"source":"ghsa","ecosystem":"PyPI","package":"tensorflow-cpu","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"2.6.0"},{"fixed":"2.6.3"}]}]},{"source":"ghsa","ecosystem":"PyPI","package":"tensorflow-cpu","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"2.7.0"},{"last_affected":"2.7.0"},{"fixed":"2.7.1"}]}],"versions":["2.7.0"]},{"source":"ghsa","ecosystem":"PyPI","package":"tensorflow-gpu","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"0"},{"fixed":"2.5.3"}]}]},{"source":"ghsa","ecosystem":"PyPI","package":"tensorflow-gpu","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"2.6.0"},{"fixed":"2.6.3"}]}]},{"source":"ghsa","ecosystem":"PyPI","package":"tensorflow-gpu","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"2.7.0"},{"last_affected":"2.7.0"},{"fixed":"2.7.1"}]}],"versions":["2.7.0"]},{"source":"osv","ecosystem":"Bitnami","package":"tensorflow","ranges":[{"type":"SEMVER","events":[{"introduced":"0"},{"fixed":"2.5.3"},{"introduced":"2.6.0"},{"fixed":"2.6.3"},{"introduced":"2.7.0"},{"fixed":"2.7.1"}]}]},{"source":"osv","ecosystem":"PyPI","package":"tensorflow","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"0"},{"fixed":"2.5.3"}]}],"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","2.1.4","2.2.0","2.2.1","2.2.2","2.2.3","2.3.0","2.3.1","2.3.2","2.3.3","2.3.4","2.4.0","2.4.1","2.4.2","2.4.3","2.4.4","2.5.0","2.5.1","2.5.2"]},{"source":"osv","ecosystem":"PyPI","package":"tensorflow","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"2.6.0"},{"fixed":"2.6.3"}]}],"versions":["2.6.0","2.6.1","2.6.2"]},{"source":"osv","ecosystem":"PyPI","package":"tensorflow","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"2.7.0"},{"fixed":"2.7.1"}]}],"versions":["2.7.0"]},{"source":"osv","ecosystem":"PyPI","package":"tensorflow-cpu","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"0"},{"fixed":"2.5.3"}]}],"versions":["1.15.0","2.1.0","2.1.1","2.1.2","2.1.3","2.1.4","2.2.0","2.2.1","2.2.2","2.2.3","2.3.0","2.3.1","2.3.2","2.3.3","2.3.4","2.4.0","2.4.1","2.4.2","2.4.3","2.4.4","2.5.0","2.5.1","2.5.2"]},{"source":"osv","ecosystem":"PyPI","package":"tensorflow-cpu","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"2.6.0"},{"fixed":"2.6.3"}]}],"versions":["2.6.0","2.6.1","2.6.2"]},{"source":"osv","ecosystem":"PyPI","package":"tensorflow-cpu","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"2.7.0"},{"fixed":"2.7.1"}]}],"versions":["2.7.0"]},{"source":"osv","ecosystem":"PyPI","package":"tensorflow-gpu","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"0"},{"fixed":"2.5.3"}]}],"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","2.1.4","2.2.0","2.2.1","2.2.2","2.2.3","2.3.0","2.3.1","2.3.2","2.3.3","2.3.4","2.4.0","2.4.1","2.4.2","2.4.3","2.4.4","2.5.0","2.5.1","2.5.2"]},{"source":"osv","ecosystem":"PyPI","package":"tensorflow-gpu","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"2.6.0"},{"fixed":"2.6.3"}]}],"versions":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url":"https://www.suse.com/security/cve/CVE-2021-37676","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2021-37677","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2021-37678","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2021-37679","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2021-37680","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2021-37681","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2021-37682","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2021-37683","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2021-37684","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2021-37685","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2021-37686","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2021-37687","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2021-37688","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2021-37689","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2021-37690","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2021-37691","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2021-37692","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2021-41195","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2021-41196","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2021-41197","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2021-41198","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2021-41199","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2021-41200","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2021-41201","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2021-41202","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2021-41203","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2021-41204","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2021-41205","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2021-41206","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2021-41207","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2021-41208","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2021-41209","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2021-41210","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2021-41211","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2021-41212","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2021-41213","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2021-41214","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2021-41215","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2021-41216","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2021-41217","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2021-41218","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2021-41219","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2021-41220","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2021-41221","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2021-41222","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2021-41223","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2021-41224","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2021-41225","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2021-41226","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2021-41227","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2021-41228","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2022-21725","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2022-21726","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2022-21727","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2022-21728","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2022-21729","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2022-21730","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2022-21731","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2022-21732","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2022-21733","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2022-21734","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2022-21735","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2022-21736","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2022-21737","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2022-21738","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2022-21739","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2022-21740","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2022-21741","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2022-23557","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2022-23558","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2022-23559","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2022-23560","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2022-23561","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2022-23562","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2022-23563","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2022-23564","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2022-23565","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2022-23566","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2022-23567","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2022-23568","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2022-23569","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2022-23570","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2022-23571","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2022-23572","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2022-23573","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2022-23574","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2022-23575","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2022-23576","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2022-23577","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2022-23578","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2022-23579","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2022-23580","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2022-23581","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2022-23582","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2022-23583","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2022-23584","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2022-23585","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2022-23586","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2022-23587","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2022-23588","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2022-23589","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2022-23590","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2022-23591","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2022-23594","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2022-23595","tags":["web"]}],"sources":{"cve":[{"id":"CVE-2022-21727","title":"Integer overflow in Tensorflow","description":"Tensorflow is an Open Source Machine Learning Framework. The implementation of shape inference for `Dequantize` is vulnerable to an integer overflow weakness. The `axis` argument can be `-1` (the default value for the optional argument) or any other positive value at most the number of dimensions of the input. Unfortunately, the upper bound is not checked, and, since the code computes `axis + 1`, an attacker can trigger an integer overflow. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.","status":"published","published":"2022-02-03T11:07:40Z","modified":"2025-05-05T16:32:51.904Z","cvss":[{"version":"3.1","score":7.6,"vector":"CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:L/I:L/A:H","level":"high","assessor":"cna"}],"cwes":["CWE-190"],"updated_at":"2026-11-02T14:34:29.057251Z"}],"euvd":[{"id":"EUVD-2022-0290","description":"Tensorflow is an Open Source Machine Learning Framework. The implementation of shape inference for `Dequantize` is vulnerable to an integer overflow weakness. The `axis` argument can be `-1` (the default value for the optional argument) or any other positive value at most the number of dimensions of the input. Unfortunately, the upper bound is not checked, and, since the code computes `axis + 1`, an attacker can trigger an integer overflow. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.","status":"published","published":"2022-02-03T11:07:40Z","modified":"2025-05-05T16:32:51Z","cvss":[{"version":"3.1","score":7.6,"vector":"CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:L/I:L/A:H","level":"high"}],"updated_at":"2026-11-02T20:32:56.549702Z"}],"ghsa":[{"id":"GHSA-c6fh-56w7-fvjw","title":"Integer overflow in Tensorflow","description":"### Impact \nThe [implementation of shape inference for `Dequantize`](https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/ops/array_ops.cc#L3001-L3034) is vulnerable to an integer overflow weakness:\n\n```python\nimport tensorflow as tf\n\ninput = tf.constant([1,1],dtype=tf.qint32)\n\n@tf.function\ndef test():\n  y = tf.raw_ops.Dequantize(\n    input=input,\n    min_range=[1.0],\n    max_range=[10.0],\n    mode='MIN_COMBINED',\n    narrow_range=False,\n    axis=2**31-1,\n    dtype=tf.bfloat16)\n  return y\n\ntest()\n``` \n\nThe `axis` argument can be `-1` (the default value for the optional argument) or any other positive value at most the number of dimensions of the input. Unfortunately, the upper bound is not checked, and, since the code computes `axis + 1`, an attacker can trigger an integer overflow:\n\n```cc\n  int axis = -1; \n  Status s = c-\u003eGetAttr(\"axis\", \u0026axis);\n  // ...\n  if (axis \u003c -1) {\n    return errors::InvalidArgument(\"axis should be at least -1, got \",\n                                   axis);\n  }\n  // ...\n  if (axis != -1) {\n    ShapeHandle input;\n    TF_RETURN_IF_ERROR(c-\u003eWithRankAtLeast(c-\u003einput(0), axis + 1, \u0026input));\n    // ...\n  }\n```\n  \n### Patches\nWe have patched the issue in GitHub commit [b64638ec5ccaa77b7c1eb90958e3d85ce381f91b](https://github.com/tensorflow/tensorflow/commit/b64638ec5ccaa77b7c1eb90958e3d85ce381f91b).\n\nThe fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, 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 Yu Tian of Qihoo 360 AIVul Team.","status":"published","published":"2022-02-09T18:29:13Z","modified":"2024-11-13T22:10:04Z","cvss":[{"version":"4.0","score":7.2,"vector":"CVSS:4.0/AV:N/AC:L/AT:N/PR:L/UI:N/VC:L/VI:L/VA:H/SC:N/SI:N/SA:N","level":"high"},{"version":"3.1","score":7.6,"vector":"CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:L/I:L/A:H","level":"high"}],"cwes":["CWE-190"],"updated_at":"2026-11-02T14:38:18.163845Z"}],"nvd":[{"id":"CVE-2022-21727","description":"Tensorflow is an Open Source Machine Learning Framework. The implementation of shape inference for `Dequantize` is vulnerable to an integer overflow weakness. The `axis` argument can be `-1` (the default value for the optional argument) or any other positive value at most the number of dimensions of the input. Unfortunately, the upper bound is not checked, and, since the code computes `axis + 1`, an attacker can trigger an integer overflow. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.","status":"published","published":"2022-02-03T11:15:07.953Z","modified":"2026-06-17T04:26:52.097Z","cvss":[{"version":"3.1","score":7.6,"vector":"CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:L/I:L/A:H","level":"high","assessor":"secondary"},{"version":"3.1","score":8.8,"vector":"CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H","level":"high","assessor":"primary"},{"version":"2.0","score":6.5,"vector":"AV:N/AC:L/Au:S/C:P/I:P/A:P","level":"medium","assessor":"primary"}],"cwes":["CWE-190"],"updated_at":"2026-11-02T14:07:11.52734Z"}],"osv":[{"id":"CVE-2022-21727","title":"Integer overflow in Tensorflow","description":"Tensorflow is an Open Source Machine Learning Framework. The implementation of shape inference for `Dequantize` is vulnerable to an integer overflow weakness. The `axis` argument can be `-1` (the default value for the optional argument) or any other positive value at most the number of dimensions of the input. Unfortunately, the upper bound is not checked, and, since the code computes `axis + 1`, an attacker can trigger an integer overflow. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.","status":"published","published":"2022-02-03T11:07:40Z","modified":"2026-10-28T10:25:11.161492679Z","cvss":[{"version":"3.1","score":7.6,"vector":"CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:L/I:L/A:H","level":"high"}],"updated_at":"2026-11-02T15:44:23.655778Z"},{"id":"GHSA-c6fh-56w7-fvjw","title":"Integer overflow in Tensorflow","description":"### Impact \nThe [implementation of shape inference for `Dequantize`](https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/ops/array_ops.cc#L3001-L3034) is vulnerable to an integer overflow weakness:\n\n```python\nimport tensorflow as tf\n\ninput = tf.constant([1,1],dtype=tf.qint32)\n\n@tf.function\ndef test():\n  y = tf.raw_ops.Dequantize(\n    input=input,\n    min_range=[1.0],\n    max_range=[10.0],\n    mode='MIN_COMBINED',\n    narrow_range=False,\n    axis=2**31-1,\n    dtype=tf.bfloat16)\n  return y\n\ntest()\n``` \n\nThe `axis` argument can be `-1` (the default value for the optional argument) or any other positive value at most the number of dimensions of the input. Unfortunately, the upper bound is not checked, and, since the code computes `axis + 1`, an attacker can trigger an integer overflow:\n\n```cc\n  int axis = -1; \n  Status s = c-\u003eGetAttr(\"axis\", \u0026axis);\n  // ...\n  if (axis \u003c -1) {\n    return errors::InvalidArgument(\"axis should be at least -1, got \",\n                                   axis);\n  }\n  // ...\n  if (axis != -1) {\n    ShapeHandle input;\n    TF_RETURN_IF_ERROR(c-\u003eWithRankAtLeast(c-\u003einput(0), axis + 1, \u0026input));\n    // ...\n  }\n```\n  \n### Patches\nWe have patched the issue in GitHub commit [b64638ec5ccaa77b7c1eb90958e3d85ce381f91b](https://github.com/tensorflow/tensorflow/commit/b64638ec5ccaa77b7c1eb90958e3d85ce381f91b).\n\nThe fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, 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 Yu Tian of Qihoo 360 AIVul Team.","status":"published","published":"2022-02-09T18:29:13Z","modified":"2026-07-13T16:42:48.784678647Z","cvss":[{"version":"3.1","score":7.6,"vector":"CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:L/I:L/A:H","level":"high"},{"version":"4.0","score":7.2,"vector":"CVSS:4.0/AV:N/AC:L/AT:N/PR:L/UI:N/VC:L/VI:L/VA:H/SC:N/SI:N/SA:N","level":"high"}],"cwes":["CWE-190"],"updated_at":"2026-11-02T15:46:44.571161Z"},{"id":"PYSEC-2026-3159","title":"Integer overflow in Tensorflow","description":"### Impact \nThe [implementation of shape inference for `Dequantize`](https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/ops/array_ops.cc#L3001-L3034) is vulnerable to an integer overflow weakness:\n\n```python\nimport tensorflow as tf\n\ninput = tf.constant([1,1],dtype=tf.qint32)\n\n@tf.function\ndef test():\n  y = tf.raw_ops.Dequantize(\n    input=input,\n    min_range=[1.0],\n    max_range=[10.0],\n    mode='MIN_COMBINED',\n    narrow_range=False,\n    axis=2**31-1,\n    dtype=tf.bfloat16)\n  return y\n\ntest()\n``` \n\nThe `axis` argument can be `-1` (the default value for the optional argument) or any other positive value at most the number of dimensions of the input. Unfortunately, the upper bound is not checked, and, since the code computes `axis + 1`, an attacker can trigger an integer overflow:\n\n```cc\n  int axis = -1; \n  Status s = c-\u003eGetAttr(\"axis\", \u0026axis);\n  // ...\n  if (axis \u003c -1) {\n    return errors::InvalidArgument(\"axis should be at least -1, got \",\n                                   axis);\n  }\n  // ...\n  if (axis != -1) {\n    ShapeHandle input;\n    TF_RETURN_IF_ERROR(c-\u003eWithRankAtLeast(c-\u003einput(0), axis + 1, \u0026input));\n    // ...\n  }\n```\n  \n### Patches\nWe have patched the issue in GitHub commit [b64638ec5ccaa77b7c1eb90958e3d85ce381f91b](https://github.com/tensorflow/tensorflow/commit/b64638ec5ccaa77b7c1eb90958e3d85ce381f91b).\n\nThe fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, 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 Yu Tian of Qihoo 360 AIVul Team.","status":"published","published":"2026-07-09T16:49:38.988123Z","modified":"2026-07-13T16:42:48.784678647Z","cvss":[{"version":"3.1","score":7.6,"vector":"CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:L/I:L/A:H","level":"high"},{"version":"4.0","score":7.2,"vector":"CVSS:4.0/AV:N/AC:L/AT:N/PR:L/UI:N/VC:L/VI:L/VA:H/SC:N/SI:N/SA:N","level":"high"}],"updated_at":"2026-11-02T15:52:56.429374Z"},{"id":"BIT-tensorflow-2022-21727","title":"Integer overflow in Tensorflow","description":"Tensorflow is an Open Source Machine Learning Framework. The implementation of shape inference for `Dequantize` is vulnerable to an integer overflow weakness. The `axis` argument can be `-1` (the default value for the optional argument) or any other positive value at most the number of dimensions of the input. Unfortunately, the upper bound is not checked, and, since the code computes `axis + 1`, an attacker can trigger an integer overflow. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.","status":"published","published":"2024-03-06T11:15:51.115Z","modified":"2026-09-08T08:48:25.191452461Z","updated_at":"2026-11-02T15:28:35.51001Z"},{"id":"openSUSE-SU-2024:12116-1","title":"tensorflow-lite-2.9.1-1.1 on GA media","description":"These are all security issues fixed in the tensorflow-lite-2.9.1-1.1 package on the GA media of openSUSE Tumbleweed.","status":"published","published":"2024-06-15T00:00:00Z","modified":"2026-02-04T03:04:19.609756Z","updated_at":"2026-11-02T16:58:35.485255Z"},{"id":"PYSEC-2022-106","description":"Tensorflow is an Open Source Machine Learning Framework. The implementation of shape inference for `Dequantize` is vulnerable to an integer overflow weakness. The `axis` argument can be `-1` (the default value for the optional argument) or any other positive value at most the number of dimensions of the input. Unfortunately, the upper bound is not checked, and, since the code computes `axis + 1`, an attacker can trigger an integer overflow. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.","status":"published","published":"2022-02-03T11:15:00Z","modified":"2026-07-13T16:42:48.784678647Z","updated_at":"2026-11-02T15:52:53.449407Z"},{"id":"PYSEC-2022-51","description":"Tensorflow is an Open Source Machine Learning Framework. The implementation of shape inference for `Dequantize` is vulnerable to an integer overflow weakness. The `axis` argument can be `-1` (the default value for the optional argument) or any other positive value at most the number of dimensions of the input. Unfortunately, the upper bound is not checked, and, since the code computes `axis + 1`, an attacker can trigger an integer overflow. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.","status":"published","published":"2022-02-03T11:15:00Z","modified":"2026-07-13T16:42:48.784678647Z","updated_at":"2026-11-02T15:52:53.837392Z"}]}}