{"id":"CVE-2021-37663","aliases":["BIT-tensorflow-2021-37663","EUVD-2021-0389","GHSA-g25h-jr74-qp5j","PYSEC-2021-285","PYSEC-2021-576","PYSEC-2021-774","openSUSE-SU-2022:10014-1","openSUSE-SU-2024:12116-1"],"status":"published","title":"Incomplete validation in `QuantizeV2` in TensorFlow","description":"TensorFlow is an end-to-end open source platform for machine learning. In affected versions due to incomplete validation in `tf.raw_ops.QuantizeV2`, 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. The [implementation](https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/quantize_op.cc#L59) has some validation but does not check that `min_range` and `max_range` both have the same non-zero number of elements. If `axis` is provided (i.e., not `-1`), then validation should check that it is a value in range for the rank of `input` tensor and then the lengths of `min_range` and `max_range` inputs match the `axis` dimension of the `input` tensor. We have patched the issue in GitHub commit 6da6620efad397c85493b8f8667b821403516708. The 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.","severity":{"version":"3.1","score":7.8,"vector":"CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H","level":"high","assessor":"cna","from":"cve"},"epss":{"day":"2026-11-10","score":0.00173,"percentile":0.06867,"model":"v2026.06.15"},"facts":{"in_kev":false,"known_exploited":false,"public_exploit":false,"fix_available":true},"cwes":["CWE-20"],"published":"2021-08-12T22:45:18Z","modified":"2026-10-20T14:28:13Z","fixes":[{"ecosystem":"PyPI","package":"tensorflow","fixed_in":["2.3.4","2.4.3","2.5.1"],"sources":["ghsa","osv"]},{"ecosystem":"PyPI","package":"tensorflow-cpu","fixed_in":["2.3.4","2.4.3","2.5.1"],"sources":["ghsa","osv"]},{"ecosystem":"PyPI","package":"tensorflow-gpu","fixed_in":["2.3.4","2.4.3","2.5.1"],"sources":["ghsa","osv"]},{"ecosystem":"Bitnami","package":"tensorflow","fixed_in":["2.3.4","2.4.3","2.5.1"],"sources":["osv"]}],"affected":[{"source":"ghsa","ecosystem":"PyPI","package":"tensorflow","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"0"},{"fixed":"2.3.4"}]}]},{"source":"ghsa","ecosystem":"PyPI","package":"tensorflow","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"2.4.0"},{"fixed":"2.4.3"}]}]},{"source":"ghsa","ecosystem":"PyPI","package":"tensorflow","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"2.5.0"},{"last_affected":"2.5.0"},{"fixed":"2.5.1"}]}],"versions":["2.5.0"]},{"source":"ghsa","ecosystem":"PyPI","package":"tensorflow-cpu","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"0"},{"fixed":"2.3.4"}]}]},{"source":"ghsa","ecosystem":"PyPI","package":"tensorflow-cpu","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"2.4.0"},{"fixed":"2.4.3"}]}]},{"source":"ghsa","ecosystem":"PyPI","package":"tensorflow-cpu","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"2.5.0"},{"last_affected":"2.5.0"},{"fixed":"2.5.1"}]}],"versions":["2.5.0"]},{"source":"ghsa","ecosystem":"PyPI","package":"tensorflow-gpu","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"0"},{"fixed":"2.3.4"}]}]},{"source":"ghsa","ecosystem":"PyPI","package":"tensorflow-gpu","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"2.4.0"},{"fixed":"2.4.3"}]}]},{"source":"ghsa","ecosystem":"PyPI","package":"tensorflow-gpu","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"2.5.0"},{"last_affected":"2.5.0"},{"fixed":"2.5.1"}]}],"versions":["2.5.0"]},{"source":"osv","ecosystem":"Bitnami","package":"tensorflow","ranges":[{"type":"SEMVER","events":[{"introduced":"2.3.0"},{"fixed":"2.3.4"},{"introduced":"2.4.0"},{"fixed":"2.4.3"},{"introduced":"2.5.0"},{"fixed":"2.5.1"}]}]},{"source":"osv","ecosystem":"PyPI","package":"tensorflow","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"0"},{"fixed":"2.3.4"}]}],"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","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"]},{"source":"osv","ecosystem":"PyPI","package":"tensorflow","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"2.4.0"},{"fixed":"2.4.3"}]}],"versions":["2.4.0","2.4.1","2.4.2"]},{"source":"osv","ecosystem":"PyPI","package":"tensorflow","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"2.5.0"},{"fixed":"2.5.1"}]}],"versions":["2.5.0"]},{"source":"osv","ecosystem":"PyPI","package":"tensorflow-cpu","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"0"},{"fixed":"2.3.4"}]}],"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"]},{"source":"osv","ecosystem":"PyPI","package":"tensorflow-cpu","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"2.4.0"},{"fixed":"2.4.3"}]}],"versions":["2.4.0","2.4.1","2.4.2"]},{"source":"osv","ecosystem":"PyPI","package":"tensorflow-cpu","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"2.5.0"},{"fixed":"2.5.1"}]}],"versions":["2.5.0"]},{"source":"osv","ecosystem":"PyPI","package":"tensorflow-gpu","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"0"},{"fixed":"2.3.4"}]}],"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","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"]},{"source":"osv","ecosystem":"PyPI","package":"tensorflow-gpu","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"2.4.0"},{"fixed":"2.4.3"}]}],"versions":["2.4.0","2.4.1","2.4.2"]},{"source":"osv","ecosystem":"PyPI","package":"tensorflow-gpu","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"2.5.0"},{"fixed":"2.5.1"}]}],"versions":["2.5.0"]},{"source":"osv","e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ags":["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":"","tags":["advisory"]},{"url":"https://www.suse.com/security/cve/CVE-2020-10531","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2020-15190","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2020-15191","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2020-15194","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2020-15202","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2020-15203","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2020-15204","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2020-15205","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2020-15206","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2020-15207","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2020-15208","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2020-15209","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2021-22922","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2021-22923","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2021-22926","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-2021-37663","title":"Incomplete validation in `QuantizeV2` in TensorFlow","description":"TensorFlow is an end-to-end open source platform for machine learning. In affected versions due to incomplete validation in `tf.raw_ops.QuantizeV2`, 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. The [implementation](https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/quantize_op.cc#L59) has some validation but does not check that `min_range` and `max_range` both have the same non-zero number of elements. If `axis` is provided (i.e., not `-1`), then validation should check that it is a value in range for the rank of `input` tensor and then the lengths of `min_range` and `max_range` inputs match the `axis` dimension of the `input` tensor. We have patched the issue in GitHub commit 6da6620efad397c85493b8f8667b821403516708. The 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.","status":"published","published":"2021-08-12T22:45:18Z","modified":"2024-08-04T01:23:01.403Z","cvss":[{"version":"3.1","score":7.8,"vector":"CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H","level":"high","assessor":"cna"}],"cwes":["CWE-20"],"updated_at":"2026-11-02T14:32:58.359368Z"}],"euvd":[{"id":"EUVD-2021-0389","description":"TensorFlow is an end-to-end open source platform for machine learning. In affected versions due to incomplete validation in `tf.raw_ops.QuantizeV2`, 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. The [implementation](https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/quantize_op.cc#L59) has some validation but does not check that `min_range` and `max_range` both have the same non-zero number of elements. If `axis` is provided (i.e., not `-1`), then validation should check that it is a value in range for the rank of `input` tensor and then the lengths of `min_range` and `max_range` inputs match the `axis` dimension of the `input` tensor. We have patched the issue in GitHub commit 6da6620efad397c85493b8f8667b821403516708. The 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.","status":"published","published":"2021-08-12T22:45:18Z","modified":"2024-08-04T01:23:01Z","cvss":[{"version":"3.1","score":7.8,"vector":"CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H","level":"high"}],"updated_at":"2026-11-03T03:24:27.254804Z"}],"ghsa":[{"id":"GHSA-g25h-jr74-qp5j","title":"Incomplete validation in `QuantizeV2`","description":"### Impact                                                                                                                                                                                                                                                                                \nDue to incomplete validation in `tf.raw_ops.QuantizeV2`, 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.QuantizeV2(\n  input=[1,2,3],\n  min_range=[1,2],\n  max_range=[],\n  T=tf.qint32,\n  mode='SCALED',\n  round_mode='HALF_AWAY_FROM_ZERO',\n  narrow_range=False,\n  axis=1,\n  ensure_minimum_range=3)\n```\n\nThe [implementation](https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/quantize_op.cc#L59) has some validation but does not check that `min_range` and `max_range` both have the same non-zero number of elements. If `axis` is provided (i.e., not `-1`), then validation should check that it is a value in range for the rank of `input` tensor and then the lengths of `min_range` and `max_range` inputs match the `axis` dimension of the `input` tensor.\n  \n### Patches\nWe have patched the issue in GitHub commit [6da6620efad397c85493b8f8667b821403516708](https://github.com/tensorflow/tensorflow/commit/6da6620efad397c85493b8f8667b821403516708).\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 360.","status":"published","published":"2021-08-25T14:42:23Z","modified":"2024-11-13T20:57:22Z","cvss":[{"version":"4.0","score":8.5,"vector":"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","level":"high"},{"version":"3.1","score":7.8,"vector":"CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H","level":"high"}],"cwes":["CWE-20"],"updated_at":"2026-11-02T14:38:17.357217Z"}],"nvd":[{"id":"CVE-2021-37663","description":"TensorFlow is an end-to-end open source platform for machine learning. In affected versions due to incomplete validation in `tf.raw_ops.QuantizeV2`, 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. The [implementation](https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/quantize_op.cc#L59) has some validation but does not check that `min_range` and `max_range` both have the same non-zero number of elements. If `axis` is provided (i.e., not `-1`), then validation should check that it is a value in range for the rank of `input` tensor and then the lengths of `min_range` and `max_range` inputs match the `axis` dimension of the `input` tensor. We have patched the issue in GitHub commit 6da6620efad397c85493b8f8667b821403516708. The 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.","status":"published","published":"2021-08-12T23:15:07.233Z","modified":"2026-06-17T04:00:56.053Z","cvss":[{"version":"3.1","score":7.8,"vector":"CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H","level":"high","assessor":"secondary"},{"version":"3.1","score":7.8,"vector":"CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H","level":"high","assessor":"primary"},{"version":"2.0","score":4.6,"vector":"AV:L/AC:L/Au:N/C:P/I:P/A:P","level":"medium","assessor":"primary"}],"cwes":["CWE-20"],"updated_at":"2026-11-02T14:06:56.943671Z"}],"osv":[{"id":"CVE-2021-37663","description":"TensorFlow is an end-to-end open source platform for machine learning. In affected versions due to incomplete validation in `tf.raw_ops.QuantizeV2`, 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. The [implementation](https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/quantize_op.cc#L59) has some validation but does not check that `min_range` and `max_range` both have the same non-zero number of elements. If `axis` is provided (i.e., not `-1`), then validation should check that it is a value in range for the rank of `input` tensor and then the lengths of `min_range` and `max_range` inputs match the `axis` dimension of the `input` tensor. We have patched the issue in GitHub commit 6da6620efad397c85493b8f8667b821403516708. The 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.","status":"published","published":"2021-08-12T23:15:07.233Z","modified":"2026-10-20T14:28:13.547050986Z","cvss":[{"version":"3.1","score":7.8,"vector":"CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H","level":"high"}],"updated_at":"2026-11-02T15:44:20.434873Z"},{"id":"GHSA-g25h-jr74-qp5j","title":"Incomplete validation in `QuantizeV2`","description":"### Impact                                                                                                                                                                                                                                                                                \nDue to incomplete validation in `tf.raw_ops.QuantizeV2`, 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.QuantizeV2(\n  input=[1,2,3],\n  min_range=[1,2],\n  max_range=[],\n  T=tf.qint32,\n  mode='SCALED',\n  round_mode='HALF_AWAY_FROM_ZERO',\n  narrow_range=False,\n  axis=1,\n  ensure_minimum_range=3)\n```\n\nThe [implementation](https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/quantize_op.cc#L59) has some validation but does not check that `min_range` and `max_range` both have the same non-zero number of elements. If `axis` is provided (i.e., not `-1`), then validation should check that it is a value in range for the rank of `input` tensor and then the lengths of `min_range` and `max_range` inputs match the `axis` dimension of the `input` tensor.\n  \n### Patches\nWe have patched the issue in GitHub commit [6da6620efad397c85493b8f8667b821403516708](https://github.com/tensorflow/tensorflow/commit/6da6620efad397c85493b8f8667b821403516708).\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 360.","status":"published","published":"2021-08-25T14:42:23Z","modified":"2026-07-08T06:28:29.678876386Z","cvss":[{"version":"3.1","score":7.8,"vector":"CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H","level":"high"},{"version":"4.0","score":8.5,"vector":"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","level":"high"}],"cwes":["CWE-20"],"updated_at":"2026-11-02T15:46:47.119724Z"},{"id":"BIT-tensorflow-2021-37663","title":"Incomplete validation in `QuantizeV2` in TensorFlow","description":"TensorFlow is an end-to-end open source platform for machine learning. In affected versions due to incomplete validation in `tf.raw_ops.QuantizeV2`, 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. The [implementation](https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/quantize_op.cc#L59) has some validation but does not check that `min_range` and `max_range` both have the same non-zero number of elements. If `axis` is provided (i.e., not `-1`), then validation should check that it is a value in range for the rank of `input` tensor and then the lengths of `min_range` and `max_range` inputs match the `axis` dimension of the `input` tensor. We have patched the issue in GitHub commit 6da6620efad397c85493b8f8667b821403516708. The 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.","status":"published","published":"2024-03-06T11:17:17.805Z","modified":"2026-09-08T08:48:20.236493809Z","updated_at":"2026-11-02T15:28:35.51001Z"},{"id":"openSUSE-SU-2022:10014-1","title":"Security update for tensorflow2","description":"\nThis update for tensorflow fixes the following issues:\n\n\nUpdate to TF2 2.6.0 which fixes multiple CVEs (boo#1189423).\n\n- Introduction of bazel6.3 and basel-skylib1.0.3 as build\n  dependencies.\n  The latter has been adapted to all a version in its package\n  name (if %set_ver_suffix is set to 1). This allows multiple\n  versions to exist for one product (not installed).\n  NOTE: basel-skylib1.0.3 does not exist in oS:Factory:\n  basel-skylib in oS:Factory - the base version - is 1.0.3.\n","status":"published","published":"2022-06-18T16:01:26Z","modified":"2026-02-04T02:20:34.480131Z","updated_at":"2026-11-02T16:58:32.653274Z"},{"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-2021-285","description":"TensorFlow is an end-to-end open source platform for machine learning. In affected versions due to incomplete validation in `tf.raw_ops.QuantizeV2`, 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. The [implementation](https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/quantize_op.cc#L59) has some validation but does not check that `min_range` and `max_range` both have the same non-zero number of elements. If `axis` is provided (i.e., not `-1`), then validation should check that it is a value in range for the rank of `input` tensor and then the lengths of `min_range` and `max_range` inputs match the `axis` dimension of the `input` tensor. We have patched the issue in GitHub commit 6da6620efad397c85493b8f8667b821403516708. The 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.","status":"published","published":"2021-08-12T23:15:00Z","modified":"2023-12-06T01:01:22.932027Z","updated_at":"2026-11-02T15:52:52.682031Z"},{"id":"PYSEC-2021-576","description":"TensorFlow is an end-to-end open source platform for machine learning. In affected versions due to incomplete validation in `tf.raw_ops.QuantizeV2`, 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. The [implementation](https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/quantize_op.cc#L59) has some validation but does not check that `min_range` and `max_range` both have the same non-zero number of elements. If `axis` is provided (i.e., not `-1`), then validation should check that it is a value in range for the rank of `input` tensor and then the lengths of `min_range` and `max_range` inputs match the `axis` dimension of the `input` tensor. We have patched the issue in GitHub commit 6da6620efad397c85493b8f8667b821403516708. The 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.","status":"published","published":"2021-08-12T23:15:00Z","modified":"2023-12-06T01:01:22.932027Z","updated_at":"2026-11-02T15:52:53.119977Z"},{"id":"PYSEC-2021-774","description":"TensorFlow is an end-to-end open source platform for machine learning. In affected versions due to incomplete validation in `tf.raw_ops.QuantizeV2`, 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. The [implementation](https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/quantize_op.cc#L59) has some validation but does not check that `min_range` and `max_range` both have the same non-zero number of elements. If `axis` is provided (i.e., not `-1`), then validation should check that it is a value in range for the rank of `input` tensor and then the lengths of `min_range` and `max_range` inputs match the `axis` dimension of the `input` tensor. We have patched the issue in GitHub commit 6da6620efad397c85493b8f8667b821403516708. The 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.","status":"published","published":"2021-08-12T23:15:00Z","modified":"2023-12-06T01:01:22.932027Z","updated_at":"2026-11-02T15:52:53.119977Z"}]}}