{"id":"CVE-2023-25661","aliases":["AZL-31197","BIT-tensorflow-2023-25661","EUVD-2023-1006","GHSA-fxgc-95xx-grvq","PYSEC-2026-1952","PYSEC-2026-3182"],"status":"published","title":"Denial of Service in TensorFlow","description":"TensorFlow is an Open Source Machine Learning Framework. In versions prior to 2.11.1 a malicious invalid input crashes a tensorflow model (Check Failed) and can be used to trigger a denial of service attack. A proof of concept can be constructed with the `Convolution3DTranspose` function. This Convolution3DTranspose layer is a very common API in modern neural networks. The ML models containing such vulnerable components could be deployed in ML applications or as cloud services. This failure could be potentially used to trigger a denial of service attack on ML cloud services. An attacker must have privilege to provide input to a `Convolution3DTranspose` call. This issue has been patched and users are advised to upgrade to version 2.11.1. There are no known workarounds for this vulnerability.","severity":{"version":"3.1","score":6.5,"vector":"CVSS:3.1/AV:N/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":0.00436,"percentile":0.3832,"model":"v2026.06.15"},"facts":{"in_kev":false,"known_exploited":false,"public_exploit":false,"fix_available":true},"cwes":["CWE-20"],"published":"2023-03-27T19:52:07Z","modified":"2026-10-28T10:38:31Z","fixes":[{"ecosystem":"PyPI","package":"tensorflow","fixed_in":["2.11.1"],"sources":["ghsa","osv"]},{"ecosystem":"PyPI","package":"tensorflow-cpu","fixed_in":["2.11.1"],"sources":["ghsa","osv"]},{"ecosystem":"Azure Linux:2","package":"tensorflow","fixed_in":["2.11.1-1"],"sources":["osv"]},{"ecosystem":"Bitnami","package":"tensorflow","fixed_in":["2.11.1"],"sources":["osv"]}],"affected":[{"source":"ghsa","ecosystem":"PyPI","package":"tensorflow","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"0"},{"fixed":"2.11.1"}]}]},{"source":"ghsa","ecosystem":"PyPI","package":"tensorflow-cpu","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"0"},{"fixed":"2.11.1"}]}]},{"source":"osv","ecosystem":"Azure Linux:2","package":"tensorflow","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"0"},{"fixed":"2.11.1-1"}]}]},{"source":"osv","ecosystem":"Bitnami","package":"tensorflow","ranges":[{"type":"SEMVER","events":[{"introduced":"0"},{"fixed":"2.11.1"}]}]},{"source":"osv","ecosystem":"PyPI","package":"tensorflow","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"0"},{"fixed":"2.11.1"}]}],"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","2.10.0","2.10.0rc0","2.10.0rc1","2.10.0rc2","2.10.0rc3","2.10.1","2.11.0","2.11.0rc0","2.11.0rc1","2.11.0rc2","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","2.5.3","2.6.0","2.6.0rc0","2.6.0rc1","2.6.0rc2","2.6.1","2.6.2","2.6.3","2.6.4","2.6.5","2.7.0","2.7.0rc0","2.7.0rc1","2.7.1","2.7.2","2.7.3","2.7.4","2.8.0","2.8.0rc0","2.8.0rc1","2.8.1","2.8.2","2.8.3","2.8.4","2.9.0","2.9.0rc0","2.9.0rc1","2.9.0rc2","2.9.1","2.9.2","2.9.3"]},{"source":"osv","ecosystem":"PyPI","package":"tensorflow-cpu","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"0"},{"fixed":"2.11.1"}]}],"versions":["1.15.0","2.1.0","2.1.1","2.1.2","2.1.3","2.1.4","2.10.0","2.10.0rc0","2.10.0rc1","2.10.0rc2","2.10.0rc3","2.10.1","2.11.0","2.11.0rc0","2.11.0rc1","2.11.0rc2","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","2.5.3","2.6.0","2.6.1","2.6.2","2.6.3","2.6.4","2.6.5","2.7.0","2.7.1","2.7.2","2.7.3","2.7.4","2.8.0","2.8.1","2.8.2","2.8.3","2.8.4","2.9.0","2.9.0rc0","2.9.0rc1","2.9.0rc2","2.9.1","2.9.2","2.9.3"]},{"source":"osv","ecosystem":"PyPI","package":"tensorflow-cpu","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"0"},{"fixed":"2.11.1"}]}],"versions":["1.15.0","2.1.0","2.1.1","2.1.2","2.1.3","2.1.4","2.10.0","2.10.0rc0","2.10.0rc1","2.10.0rc2","2.10.0rc3","2.10.1","2.11.0","2.11.0rc0","2.11.0rc1","2.11.0rc2","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","2.5.3","2.6.0","2.6.1","2.6.2","2.6.3","2.6.4","2.6.5","2.7.0","2.7.1","2.7.2","2.7.3","2.7.4","2.8.0","2.8.1","2.8.2","2.8.3","2.8.4","2.9.0","2.9.0rc0","2.9.0rc1","2.9.0rc2","2.9.1","2.9.2","2.9.3"]},{"source":"osv","ecosystem":"PyPI","package":"tensorflow","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"0"},{"fixed":"2.11.1"}]}],"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","2.10.0","2.10.0rc0","2.10.0rc1","2.10.0rc2","2.10.0rc3","2.10.1","2.11.0","2.11.0rc0","2.11.0rc1","2.11.0rc2","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","2.5.3","2.6.0","2.6.0rc0","2.6.0rc1","2.6.0rc2","2.6.1","2.6.2","2.6.3","2.6.4","2.6.5","2.7.0","2.7.0rc0","2.7.0rc1","2.7.1","2.7.2","2.7.3","2.7.4","2.8.0","2.8.0rc0","2.8.0rc1","2.8.1","2.8.2","2.8.3","2.8.4","2.9.0","2.9.0rc0","2.9.0rc1","2.9.0rc2","2.9.1","2.9.2","2.9.3"]}],"products":[{"vendor":"google","product":"tensorflow"},{"vendor":"tensorflow","product":"tensorflow"}],"references":[{"url":"https://github.com/tensorflow/tensorflow/security/advisories/GHSA-fxgc-95xx-grvq","tags":["x_refsource_CONFIRM","x_transferred","Exploit","Patch","Vendor Advisory","advisory","web"]},{"url":"https://github.com/tensorflow/tensorflow/commit/948fe6369a5711d4b4568ea9bbf6015c6dfb77e2","tags":["x_refsource_MISC","x_transferred","Patch","fix","web"]},{"url":"https://github.com/keras-team/keras/commit/85db5d07db54b853484bfd358c3894d948c36baf","tags":["web"]},{"url":"https://nvd.nist.gov/vuln/detail/CVE-2023-25661","tags":["web","advisory"]},{"url":"https://github.com/advisories/GHSA-fxgc-95xx-grvq","tags":["advisory"]},{"url":"https://github.com/CVEProject/cvelistV5/tree/main/cves/2023/25xxx/CVE-2023-25661.json","tags":["advisory"]},{"url":"https://github.com/tensorflow/tensorflow","tags":["package"]},{"url":"https://pypi.org/project/tensorflow-cpu","tags":["package"]},{"url":"https://pypi.org/project/tensorflow","tags":["package"]}],"sources":{"cve":[{"id":"CVE-2023-25661","title":"Denial of Service in TensorFlow","description":"TensorFlow is an Open Source Machine Learning Framework. In versions prior to 2.11.1 a malicious invalid input crashes a tensorflow model (Check Failed) and can be used to trigger a denial of service attack. A proof of concept can be constructed with the `Convolution3DTranspose` function. This Convolution3DTranspose layer is a very common API in modern neural networks. The ML models containing such vulnerable components could be deployed in ML applications or as cloud services. This failure could be potentially used to trigger a denial of service attack on ML cloud services. An attacker must have privilege to provide input to a `Convolution3DTranspose` call. This issue has been patched and users are advised to upgrade to version 2.11.1. There are no known workarounds for this vulnerability.","status":"published","published":"2023-03-27T19:52:07.826Z","modified":"2025-02-19T15:26:33.556Z","cvss":[{"version":"3.1","score":6.5,"vector":"CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H","level":"medium","assessor":"cna"}],"cwes":["CWE-20"],"updated_at":"2026-11-02T14:35:19.559321Z"}],"euvd":[{"id":"EUVD-2023-1006","description":"TensorFlow is an Open Source Machine Learning Framework. In versions prior to 2.11.1 a malicious invalid input crashes a tensorflow model (Check Failed) and can be used to trigger a denial of service attack. A proof of concept can be constructed with the `Convolution3DTranspose` function. This Convolution3DTranspose layer is a very common API in modern neural networks. The ML models containing such vulnerable components could be deployed in ML applications or as cloud services. This failure could be potentially used to trigger a denial of service attack on ML cloud services. An attacker must have privilege to provide input to a `Convolution3DTranspose` call. This issue has been patched and users are advised to upgrade to version 2.11.1. There are no known workarounds for this vulnerability.","status":"published","published":"2023-03-27T19:52:07Z","modified":"2025-02-19T15:26:33Z","cvss":[{"version":"3.1","score":6.5,"vector":"CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H","level":"medium"}],"updated_at":"2026-11-02T20:53:27.295607Z"}],"ghsa":[{"id":"GHSA-fxgc-95xx-grvq","title":"TensorFlow Denial of Service vulnerability","description":"### Impact\nA malicious invalid input crashes a tensorflow model (Check Failed) and can be used to trigger a denial of service attack.\nTo minimize the bug, we built a simple single-layer TensorFlow model containing a Convolution3DTranspose layer, which works well with expected inputs and can be deployed in real-world systems. However, if we call the model with a malicious input which has a zero dimension, it gives Check Failed failure and crashes. \n```python\nimport tensorflow as tf\n\nclass MyModel(tf.keras.Model):\n    def __init__(self):\n        super().__init__()\n        self.conv = tf.keras.layers.Convolution3DTranspose(2, [3,3,3], padding=\"same\")\n        \n    def call(self, input):\n        return self.conv(input)\nmodel = MyModel() # Defines a valid model.\n\nx = tf.random.uniform([1, 32, 32, 32, 3], minval=0, maxval=0, dtype=tf.float32) # This is a valid input.\noutput = model.predict(x)\nprint(output.shape) # (1, 32, 32, 32, 2)\n\nx = tf.random.uniform([1, 32, 32, 0, 3], dtype=tf.float32) # This is an invalid input.\noutput = model(x) # crash\n```\nThis Convolution3DTranspose layer is a very common API in modern neural networks. The ML models containing such vulnerable components could be deployed in ML applications or as cloud services. This failure could be potentially used to trigger a denial of service attack on ML cloud services.\n\n### Patches\nWe have patched the issue in\n- GitHub commit [948fe6369a5711d4b4568ea9bbf6015c6dfb77e2](https://github.com/tensorflow/tensorflow/commit/948fe6369a5711d4b4568ea9bbf6015c6dfb77e2)\n- GitHub commit [85db5d07db54b853484bfd358c3894d948c36baf](https://github.com/keras-team/keras/commit/85db5d07db54b853484bfd358c3894d948c36baf).\n\nThe fix will be included in TensorFlow 2.12.0. We will also cherrypick this commit on TensorFlow 2.11.1\n\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\n","status":"published","published":"2023-03-27T21:05:10Z","modified":"2023-09-01T15:23:50Z","cvss":[{"version":"3.1","score":6.5,"vector":"CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H","level":"medium"}],"cwes":["CWE-20"],"updated_at":"2026-11-02T14:37:02.095916Z"}],"nvd":[{"id":"CVE-2023-25661","description":"TensorFlow is an Open Source Machine Learning Framework. In versions prior to 2.11.1 a malicious invalid input crashes a tensorflow model (Check Failed) and can be used to trigger a denial of service attack. A proof of concept can be constructed with the `Convolution3DTranspose` function. This Convolution3DTranspose layer is a very common API in modern neural networks. The ML models containing such vulnerable components could be deployed in ML applications or as cloud services. This failure could be potentially used to trigger a denial of service attack on ML cloud services. An attacker must have privilege to provide input to a `Convolution3DTranspose` call. This issue has been patched and users are advised to upgrade to version 2.11.1. There are no known workarounds for this vulnerability.","status":"published","published":"2023-03-27T20:15:09.417Z","modified":"2026-06-17T05:41:41.16Z","cvss":[{"version":"3.1","score":6.5,"vector":"CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H","level":"medium","assessor":"secondary"},{"version":"3.1","score":6.5,"vector":"CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H","level":"medium","assessor":"primary"}],"cwes":["CWE-20"],"updated_at":"2026-11-02T14:07:51.883939Z"}],"osv":[{"id":"AZL-31197","title":"CVE-2023-25661 affecting package tensorflow for versions less than 2.11.1-1","description":"TensorFlow is an Open Source Machine Learning Framework. In versions prior to 2.11.1 a malicious invalid input crashes a tensorflow model (Check Failed) and can be used to trigger a denial of service attack. A proof of concept can be constructed with the `Convolution3DTranspose` function. This Convolution3DTranspose layer is a very common API in modern neural networks. The ML models containing such vulnerable components could be deployed in ML applications or as cloud services. This failure could be potentially used to trigger a denial of service attack on ML cloud services. An attacker must have privilege to provide input to a `Convolution3DTranspose` call. This issue has been patched and users are advised to upgrade to version 2.11.1. There are no known workarounds for this vulnerability.","status":"published","published":"2023-03-27T20:15:09Z","modified":"2026-04-21T04:26:07.668112Z","cvss":[{"version":"3.1","score":6.5,"vector":"CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H","level":"medium"}],"updated_at":"2026-11-02T15:28:03.46006Z"},{"id":"CVE-2023-25661","title":"Denial of Service in TensorFlow","description":"TensorFlow is an Open Source Machine Learning Framework. In versions prior to 2.11.1 a malicious invalid input crashes a tensorflow model (Check Failed) and can be used to trigger a denial of service attack. A proof of concept can be constructed with the `Convolution3DTranspose` function. This Convolution3DTranspose layer is a very common API in modern neural networks. The ML models containing such vulnerable components could be deployed in ML applications or as cloud services. This failure could be potentially used to trigger a denial of service attack on ML cloud services. An attacker must have privilege to provide input to a `Convolution3DTranspose` call. This issue has been patched and users are advised to upgrade to version 2.11.1. There are no known workarounds for this vulnerability.","status":"published","published":"2023-03-27T19:52:07.826Z","modified":"2026-10-28T10:38:31.93565321Z","cvss":[{"version":"3.1","score":6.5,"vector":"CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H","level":"medium"}],"cwes":["CWE-20"],"updated_at":"2026-11-02T15:44:30.709518Z"},{"id":"GHSA-fxgc-95xx-grvq","title":"TensorFlow Denial of Service vulnerability","description":"### Impact\nA malicious invalid input crashes a tensorflow model (Check Failed) and can be used to trigger a denial of service attack.\nTo minimize the bug, we built a simple single-layer TensorFlow model containing a Convolution3DTranspose layer, which works well with expected inputs and can be deployed in real-world systems. However, if we call the model with a malicious input which has a zero dimension, it gives Check Failed failure and crashes. \n```python\nimport tensorflow as tf\n\nclass MyModel(tf.keras.Model):\n    def __init__(self):\n        super().__init__()\n        self.conv = tf.keras.layers.Convolution3DTranspose(2, [3,3,3], padding=\"same\")\n        \n    def call(self, input):\n        return self.conv(input)\nmodel = MyModel() # Defines a valid model.\n\nx = tf.random.uniform([1, 32, 32, 32, 3], minval=0, maxval=0, dtype=tf.float32) # This is a valid input.\noutput = model.predict(x)\nprint(output.shape) # (1, 32, 32, 32, 2)\n\nx = tf.random.uniform([1, 32, 32, 0, 3], dtype=tf.float32) # This is an invalid input.\noutput = model(x) # crash\n```\nThis Convolution3DTranspose layer is a very common API in modern neural networks. The ML models containing such vulnerable components could be deployed in ML applications or as cloud services. This failure could be potentially used to trigger a denial of service attack on ML cloud services.\n\n### Patches\nWe have patched the issue in\n- GitHub commit [948fe6369a5711d4b4568ea9bbf6015c6dfb77e2](https://github.com/tensorflow/tensorflow/commit/948fe6369a5711d4b4568ea9bbf6015c6dfb77e2)\n- GitHub commit [85db5d07db54b853484bfd358c3894d948c36baf](https://github.com/keras-team/keras/commit/85db5d07db54b853484bfd358c3894d948c36baf).\n\nThe fix will be included in TensorFlow 2.12.0. We will also cherrypick this commit on TensorFlow 2.11.1\n\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\n","status":"published","published":"2023-03-27T21:05:10Z","modified":"2026-07-13T16:43:29.472027604Z","cvss":[{"version":"3.1","score":6.5,"vector":"CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H","level":"medium"}],"cwes":["CWE-20"],"updated_at":"2026-11-02T15:46:47.119724Z"},{"id":"PYSEC-2026-1952","title":"TensorFlow Denial of Service vulnerability","description":"### Impact\nA malicious invalid input crashes a tensorflow model (Check Failed) and can be used to trigger a denial of service attack.\nTo minimize the bug, we built a simple single-layer TensorFlow model containing a Convolution3DTranspose layer, which works well with expected inputs and can be deployed in real-world systems. However, if we call the model with a malicious input which has a zero dimension, it gives Check Failed failure and crashes. \n```python\nimport tensorflow as tf\n\nclass MyModel(tf.keras.Model):\n    def __init__(self):\n        super().__init__()\n        self.conv = tf.keras.layers.Convolution3DTranspose(2, [3,3,3], padding=\"same\")\n        \n    def call(self, input):\n        return self.conv(input)\nmodel = MyModel() # Defines a valid model.\n\nx = tf.random.uniform([1, 32, 32, 32, 3], minval=0, maxval=0, dtype=tf.float32) # This is a valid input.\noutput = model.predict(x)\nprint(output.shape) # (1, 32, 32, 32, 2)\n\nx = tf.random.uniform([1, 32, 32, 0, 3], dtype=tf.float32) # This is an invalid input.\noutput = model(x) # crash\n```\nThis Convolution3DTranspose layer is a very common API in modern neural networks. The ML models containing such vulnerable components could be deployed in ML applications or as cloud services. This failure could be potentially used to trigger a denial of service attack on ML cloud services.\n\n### Patches\nWe have patched the issue in\n- GitHub commit [948fe6369a5711d4b4568ea9bbf6015c6dfb77e2](https://github.com/tensorflow/tensorflow/commit/948fe6369a5711d4b4568ea9bbf6015c6dfb77e2)\n- GitHub commit [85db5d07db54b853484bfd358c3894d948c36baf](https://github.com/keras-team/keras/commit/85db5d07db54b853484bfd358c3894d948c36baf).\n\nThe fix will be included in TensorFlow 2.12.0. We will also cherrypick this commit on TensorFlow 2.11.1\n\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\n","status":"published","published":"2026-07-07T11:45:17.595902Z","modified":"2026-07-13T16:43:29.472027604Z","cvss":[{"version":"3.1","score":6.5,"vector":"CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H","level":"medium"}],"updated_at":"2026-11-02T15:52:55.537808Z"},{"id":"PYSEC-2026-3182","title":"TensorFlow Denial of Service vulnerability","description":"### Impact\nA malicious invalid input crashes a tensorflow model (Check Failed) and can be used to trigger a denial of service attack.\nTo minimize the bug, we built a simple single-layer TensorFlow model containing a Convolution3DTranspose layer, which works well with expected inputs and can be deployed in real-world systems. However, if we call the model with a malicious input which has a zero dimension, it gives Check Failed failure and crashes. \n```python\nimport tensorflow as tf\n\nclass MyModel(tf.keras.Model):\n    def __init__(self):\n        super().__init__()\n        self.conv = tf.keras.layers.Convolution3DTranspose(2, [3,3,3], padding=\"same\")\n        \n    def call(self, input):\n        return self.conv(input)\nmodel = MyModel() # Defines a valid model.\n\nx = tf.random.uniform([1, 32, 32, 32, 3], minval=0, maxval=0, dtype=tf.float32) # This is a valid input.\noutput = model.predict(x)\nprint(output.shape) # (1, 32, 32, 32, 2)\n\nx = tf.random.uniform([1, 32, 32, 0, 3], dtype=tf.float32) # This is an invalid input.\noutput = model(x) # crash\n```\nThis Convolution3DTranspose layer is a very common API in modern neural networks. The ML models containing such vulnerable components could be deployed in ML applications or as cloud services. This failure could be potentially used to trigger a denial of service attack on ML cloud services.\n\n### Patches\nWe have patched the issue in\n- GitHub commit [948fe6369a5711d4b4568ea9bbf6015c6dfb77e2](https://github.com/tensorflow/tensorflow/commit/948fe6369a5711d4b4568ea9bbf6015c6dfb77e2)\n- GitHub commit [85db5d07db54b853484bfd358c3894d948c36baf](https://github.com/keras-team/keras/commit/85db5d07db54b853484bfd358c3894d948c36baf).\n\nThe fix will be included in TensorFlow 2.12.0. We will also cherrypick this commit on TensorFlow 2.11.1\n\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\n","status":"published","published":"2026-07-13T14:20:01.188911Z","modified":"2026-07-13T16:43:29.472027604Z","cvss":[{"version":"3.1","score":6.5,"vector":"CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H","level":"medium"}],"updated_at":"2026-11-02T15:52:56.429374Z"},{"id":"BIT-tensorflow-2023-25661","title":"Denial of Service in TensorFlow","description":"TensorFlow is an Open Source Machine Learning Framework. In versions prior to 2.11.1 a malicious invalid input crashes a tensorflow model (Check Failed) and can be used to trigger a denial of service attack. A proof of concept can be constructed with the `Convolution3DTranspose` function. This Convolution3DTranspose layer is a very common API in modern neural networks. The ML models containing such vulnerable components could be deployed in ML applications or as cloud services. This failure could be potentially used to trigger a denial of service attack on ML cloud services. An attacker must have privilege to provide input to a `Convolution3DTranspose` call. This issue has been patched and users are advised to upgrade to version 2.11.1. There are no known workarounds for this vulnerability.","status":"published","published":"2024-03-06T11:09:28.08Z","modified":"2026-09-08T08:48:20.495798263Z","updated_at":"2026-11-02T15:28:36.104787Z"}]}}