{"id":"CVE-2021-41196","aliases":["BIT-tensorflow-2021-41196","EUVD-2021-0419","GHSA-m539-j985-hcr8","PYSEC-2021-389","PYSEC-2021-606","PYSEC-2021-804","openSUSE-SU-2024:12116-1"],"status":"published","title":"Crash in `max_pool3d` when size argument is 0 or negative","description":"TensorFlow is an open source platform for machine learning. In affected versions the Keras pooling layers can trigger a segfault if the size of the pool is 0 or if a dimension is negative. This is due to the TensorFlow's implementation of pooling operations where the values in the sliding window are not checked to be strictly positive. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.","severity":{"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":"cna","from":"cve"},"epss":{"day":"2026-11-11","score":0.00238,"percentile":0.14928,"model":"v2026.06.15"},"facts":{"in_kev":false,"known_exploited":false,"public_exploit":false,"fix_available":true},"cwes":["CWE-191"],"published":"2021-11-05T19:55:13Z","modified":"2026-10-20T10:54:57Z","fixes":[{"ecosystem":"PyPI","package":"tensorflow","fixed_in":["2.6.1","2.5.2","2.4.4","2.7.0"],"sources":["ghsa","osv"]},{"ecosystem":"PyPI","package":"tensorflow-cpu","fixed_in":["2.6.1","2.5.2","2.4.4","2.7.0"],"sources":["ghsa","osv"]},{"ecosystem":"PyPI","package":"tensorflow-gpu","fixed_in":["2.6.1","2.5.2","2.4.4","2.7.0"],"sources":["ghsa","osv"]},{"ecosystem":"Bitnami","package":"tensorflow","fixed_in":["2.4.4","2.5.2","2.6.1"],"sources":["osv"]}],"affected":[{"source":"ghsa","ecosystem":"PyPI","package":"tensorflow","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"2.6.0"},{"fixed":"2.6.1"}]}]},{"source":"ghsa","ecosystem":"PyPI","package":"tensorflow","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"2.5.0"},{"fixed":"2.5.2"}]}]},{"source":"ghsa","ecosystem":"PyPI","package":"tensorflow","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"0"},{"fixed":"2.4.4"}]}]},{"source":"ghsa","ecosystem":"PyPI","package":"tensorflow-cpu","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"2.6.0"},{"fixed":"2.6.1"}]}]},{"source":"ghsa","ecosystem":"PyPI","package":"tensorflow-cpu","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"2.5.0"},{"fixed":"2.5.2"}]}]},{"source":"ghsa","ecosystem":"PyPI","package":"tensorflow-cpu","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"0"},{"fixed":"2.4.4"}]}]},{"source":"ghsa","ecosystem":"PyPI","package":"tensorflow-gpu","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"2.6.0"},{"fixed":"2.6.1"}]}]},{"source":"ghsa","ecosystem":"PyPI","package":"tensorflow-gpu","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"2.5.0"},{"fixed":"2.5.2"}]}]},{"source":"ghsa","ecosystem":"PyPI","package":"tensorflow-gpu","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"0"},{"fixed":"2.4.4"}]}]},{"source":"osv","ecosystem":"Bitnami","package":"tensorflow","ranges":[{"type":"SEMVER","events":[{"introduced":"0"},{"fixed":"2.4.4"},{"introduced":"2.5.0"},{"fixed":"2.5.2"},{"introduced":"2.6.0"},{"fixed":"2.6.1"}]}]},{"source":"osv","ecosystem":"PyPI","package":"tensorflow","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"2.6.0"},{"fixed":"2.6.1"}]}],"versions":["2.6.0"]},{"source":"osv","ecosystem":"PyPI","package":"tensorflow","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"2.5.0"},{"fixed":"2.5.2"}]}],"versions":["2.5.0","2.5.1"]},{"source":"osv","ecosystem":"PyPI","package":"tensorflow","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"0"},{"fixed":"2.4.4"}]}],"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","2.3.4","2.4.0","2.4.1","2.4.2","2.4.3"]},{"source":"osv","ecosystem":"PyPI","package":"tensorflow-cpu","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"2.6.0"},{"fixed":"2.6.1"}]}],"versions":["2.6.0"]},{"source":"osv","ecosystem":"PyPI","package":"tensorflow-cpu","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"2.5.0"},{"fixed":"2.5.2"}]}],"versions":["2.5.0","2.5.1"]},{"source":"osv","ecosystem":"PyPI","package":"tensorflow-cpu","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"0"},{"fixed":"2.4.4"}]}],"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","2.3.4","2.4.0","2.4.1","2.4.2","2.4.3"]},{"source":"osv","ecosystem":"PyPI","package":"tensorflow-gpu","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"2.6.0"},{"fixed":"2.6.1"}]}],"versions":["2.6.0"]},{"source":"osv","ecosystem":"PyPI","package":"tensorflow-gpu","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"2.5.0"},{"fixed":"2.5.2"}]}],"versions":["2.5.0","2.5.1"]},{"source":"osv","ecosystem":"PyPI","package":"tensorflow-gpu","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"0"},{"fixed":"2.4.4"}]}],"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","2.3.4","2.4.0","2.4.1","2.4.2","2.4.3"]},{"source":"osv","ecosystem":"PyPI","pack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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-2021-41196","title":"Crash in `max_pool3d` when size argument is 0 or negative","description":"TensorFlow is an open source platform for machine learning. In affected versions the Keras pooling layers can trigger a segfault if the size of the pool is 0 or if a dimension is negative. This is due to the TensorFlow's implementation of pooling operations where the values in the sliding window are not checked to be strictly positive. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.","status":"published","published":"2021-11-05T19:55:13Z","modified":"2024-08-04T03:08:31.485Z","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","assessor":"cna"}],"cwes":["CWE-191"],"updated_at":"2026-11-02T14:32:58.739039Z"}],"euvd":[{"id":"EUVD-2021-0419","description":"TensorFlow is an open source platform for machine learning. In affected versions the Keras pooling layers can trigger a segfault if the size of the pool is 0 or if a dimension is negative. This is due to the TensorFlow's implementation of pooling operations where the values in the sliding window are not checked to be strictly positive. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.","status":"published","published":"2021-11-05T19:55:13Z","modified":"2024-08-04T03:08:31Z","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":"2026-11-03T03:24:18.784846Z"}],"ghsa":[{"id":"GHSA-m539-j985-hcr8","title":"Crash in `max_pool3d` when size argument is 0 or negative","description":"### Impact\nThe Keras pooling layers can trigger a segfault if the size of the pool is 0 or if a dimension is negative: \n\n```python\nimport tensorflow as tf\n\npool_size = [2, 2, 0]\nlayer = tf.keras.layers.MaxPooling3D(strides=1, pool_size=pool_size)\ninput_tensor = tf.random.uniform([3, 4, 10, 11, 12], dtype=tf.float32)\nres = layer(input_tensor)\n```\n\nThis is due to the TensorFlow's implementation of pooling operations where the values in the sliding window are not checked to be strictly positive.\n\n### Patches\nWe have patched the issue in GitHub commit [12b1ff82b3f26ff8de17e58703231d5a02ef1b8b](https://github.com/tensorflow/tensorflow/commit/12b1ff82b3f26ff8de17e58703231d5a02ef1b8b) (merging [#51975](https://github.com/tensorflow/tensorflow/pull/51975)).\n\nThe fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, 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 externally via a [GitHub issue](https://github.com/tensorflow/tensorflow/issues/51936).\n","status":"published","published":"2021-11-10T19:36:21Z","modified":"2024-11-13T21:46:29Z","cvss":[{"version":"4.0","score":6.8,"vector":"CVSS:4.0/AV:L/AC:L/AT:N/PR:L/UI:N/VC:N/VI:N/VA:H/SC:N/SI:N/SA:N","level":"medium"},{"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"}],"cwes":["CWE-191"],"updated_at":"2026-11-02T14:38:18.163845Z"}],"nvd":[{"id":"CVE-2021-41196","description":"TensorFlow is an open source platform for machine learning. In affected versions the Keras pooling layers can trigger a segfault if the size of the pool is 0 or if a dimension is negative. This is due to the TensorFlow's implementation of pooling operations where the values in the sliding window are not checked to be strictly positive. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.","status":"published","published":"2021-11-05T20:15:07.78Z","modified":"2026-06-17T04:08:04.427Z","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","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-191"],"updated_at":"2026-11-02T14:07:00.984108Z"}],"osv":[{"id":"CVE-2021-41196","description":"TensorFlow is an open source platform for machine learning. In affected versions the Keras pooling layers can trigger a segfault if the size of the pool is 0 or if a dimension is negative. This is due to the TensorFlow's implementation of pooling operations where the values in the sliding window are not checked to be strictly positive. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.","status":"published","published":"2021-11-05T20:15:07.78Z","modified":"2026-10-20T10:54:57.841009337Z","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":"2026-11-02T15:44:21.249056Z"},{"id":"GHSA-m539-j985-hcr8","title":"Crash in `max_pool3d` when size argument is 0 or negative","description":"### Impact\nThe Keras pooling layers can trigger a segfault if the size of the pool is 0 or if a dimension is negative: \n\n```python\nimport tensorflow as tf\n\npool_size = [2, 2, 0]\nlayer = tf.keras.layers.MaxPooling3D(strides=1, pool_size=pool_size)\ninput_tensor = tf.random.uniform([3, 4, 10, 11, 12], dtype=tf.float32)\nres = layer(input_tensor)\n```\n\nThis is due to the TensorFlow's implementation of pooling operations where the values in the sliding window are not checked to be strictly positive.\n\n### Patches\nWe have patched the issue in GitHub commit [12b1ff82b3f26ff8de17e58703231d5a02ef1b8b](https://github.com/tensorflow/tensorflow/commit/12b1ff82b3f26ff8de17e58703231d5a02ef1b8b) (merging [#51975](https://github.com/tensorflow/tensorflow/pull/51975)).\n\nThe fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, 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 externally via a [GitHub issue](https://github.com/tensorflow/tensorflow/issues/51936).\n","status":"published","published":"2021-11-10T19:36:21Z","modified":"2026-07-08T06:28:58.244764091Z","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"},{"version":"4.0","score":6.8,"vector":"CVSS:4.0/AV:L/AC:L/AT:N/PR:L/UI:N/VC:N/VI:N/VA:H/SC:N/SI:N/SA:N","level":"medium"}],"cwes":["CWE-191"],"updated_at":"2026-11-02T15:46:51.463834Z"},{"id":"BIT-tensorflow-2021-41196","title":"Crash in `max_pool3d` when size argument is 0 or negative","description":"TensorFlow is an open source platform for machine learning. In affected versions the Keras pooling layers can trigger a segfault if the size of the pool is 0 or if a dimension is negative. This is due to the TensorFlow's implementation of pooling operations where the values in the sliding window are not checked to be strictly positive. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.","status":"published","published":"2024-03-06T11:16:37.772Z","modified":"2026-09-08T08:48:16.686628678Z","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-2021-389","description":"TensorFlow is an open source platform for machine learning. In affected versions the Keras pooling layers can trigger a segfault if the size of the pool is 0 or if a dimension is negative. This is due to the TensorFlow's implementation of pooling operations where the values in the sliding window are not checked to be strictly positive. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.","status":"published","published":"2021-11-05T20:15:00Z","modified":"2023-12-06T01:01:33.508741Z","updated_at":"2026-11-02T15:52:52.682031Z"},{"id":"PYSEC-2021-606","description":"TensorFlow is an open source platform for machine learning. In affected versions the Keras pooling layers can trigger a segfault if the size of the pool is 0 or if a dimension is negative. This is due to the TensorFlow's implementation of pooling operations where the values in the sliding window are not checked to be strictly positive. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.","status":"published","published":"2021-11-05T20:15:00Z","modified":"2023-12-06T01:01:33.508741Z","updated_at":"2026-11-02T15:52:53.119977Z"},{"id":"PYSEC-2021-804","description":"TensorFlow is an open source platform for machine learning. In affected versions the Keras pooling layers can trigger a segfault if the size of the pool is 0 or if a dimension is negative. This is due to the TensorFlow's implementation of pooling operations where the values in the sliding window are not checked to be strictly positive. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.","status":"published","published":"2021-11-05T20:15:00Z","modified":"2023-12-06T01:01:33.508741Z","updated_at":"2026-11-02T15:52:53.119977Z"}]}}