{"id":"CVE-2022-41894","aliases":["AZL-11532","BIT-tensorflow-2022-41894","EUVD-2022-7356","GHSA-h6q3-vv32-2cq5","PYSEC-2026-936"],"status":"published","title":"Buffer overflow in `CONV_3D_TRANSPOSE` on TFLite","description":"TensorFlow is an open source platform for machine learning. The reference kernel of the `CONV_3D_TRANSPOSE` TensorFlow Lite operator wrongly increments the data_ptr when adding the bias to the result. Instead of `data_ptr += num_channels;` it should be `data_ptr += output_num_channels;` as if the number of input channels is different than the number of output channels, the wrong result will be returned and a buffer overflow will occur if num_channels \u003e output_num_channels. An attacker can craft a model with a specific number of input channels. It is then possible to write specific values through the bias of the layer outside the bounds of the buffer. This attack only works if the reference kernel resolver is used in the interpreter. We have patched the issue in GitHub commit 72c0bdcb25305b0b36842d746cc61d72658d2941. The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1, 2.9.3, and TensorFlow 2.8.4, as these are also affected and still in supported range.","severity":{"version":"3.1","score":7.1,"vector":"CVSS:3.1/AV:N/AC:H/PR:L/UI:R/S:U/C:H/I:H/A:H","level":"high","assessor":"cna","from":"cve"},"epss":{"day":"2026-11-09","score":0.00586,"percentile":0.48295,"model":"v2026.06.15"},"facts":{"in_kev":false,"known_exploited":false,"public_exploit":false,"fix_available":true},"cwes":["CWE-120"],"published":"2022-11-18T00:00:00Z","modified":"2026-10-28T10:30:51Z","fixes":[{"ecosystem":"PyPI","package":"tensorflow","fixed_in":["2.8.4","2.9.3","2.10.1"],"sources":["ghsa","osv"]},{"ecosystem":"Azure Linux:2","package":"tensorflow","fixed_in":["2.11.0-1"],"sources":["osv"]},{"ecosystem":"Bitnami","package":"tensorflow","fixed_in":["2.8.4","2.9.3","2.10.1"],"sources":["osv"]}],"affected":[{"source":"ghsa","ecosystem":"PyPI","package":"tensorflow","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"0"},{"fixed":"2.8.4"}]}]},{"source":"ghsa","ecosystem":"PyPI","package":"tensorflow","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"2.9.0"},{"fixed":"2.9.3"}]}]},{"source":"ghsa","ecosystem":"PyPI","package":"tensorflow","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"2.10.0"},{"fixed":"2.10.1"}]}]},{"source":"osv","ecosystem":"Azure Linux:2","package":"tensorflow","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"0"},{"fixed":"2.11.0-1"}]}]},{"source":"osv","ecosystem":"Bitnami","package":"tensorflow","ranges":[{"type":"SEMVER","events":[{"introduced":"0"},{"fixed":"2.8.4"},{"introduced":"2.9.0"},{"fixed":"2.9.3"},{"introduced":"2.10.0"},{"fixed":"2.10.1"}]}]},{"source":"osv","ecosystem":"PyPI","package":"tensorflow","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"0"},{"fixed":"2.8.4"}]}],"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","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"]},{"source":"osv","ecosystem":"PyPI","package":"tensorflow","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"2.9.0"},{"fixed":"2.9.3"}]}],"versions":["2.9.0","2.9.1","2.9.2"]},{"source":"osv","ecosystem":"PyPI","package":"tensorflow","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"2.10.0"},{"fixed":"2.10.1"}]}],"versions":["2.10.0"]},{"source":"osv","ecosystem":"PyPI","package":"tensorflow","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"0"},{"fixed":"2.8.4"},{"introduced":"2.9.0"},{"fixed":"2.9.3"},{"introduced":"2.10.0"},{"fixed":"2.10.1"}]}],"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","2.2.0","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","2.9.0","2.9.1","2.9.2"]}],"products":[{"vendor":"google","product":"tensorflow"},{"vendor":"tensorflow","product":"tensorflow"}],"references":[{"url":"https://github.com/tensorflow/tensorflow/security/advisories/GHSA-h6q3-vv32-2cq5","tags":["x_transferred","Exploit","Patch","Third Party Advisory","advisory","web"]},{"url":"https://github.com/tensorflow/tensorflow/commit/72c0bdcb25305b0b36842d746cc61d72658d2941","tags":["x_transferred","Patch","Third Party Advisory","advisory","fix","web"]},{"url":"https://github.com/tensorflow/tensorflow/blob/091e63f0ea33def7ecad661a5ac01dcafbafa90b/tensorflow/lite/kernels/internal/reference/conv3d_transpose.h#L121","tags":["x_transferred","Third Party Advisory","advisory","web"]},{"url":"https://nvd.nist.gov/vuln/detail/CVE-2022-41894","tags":["web","advisory"]},{"url":"https://github.com/advisories/GHSA-h6q3-vv32-2cq5","tags":["advisory"]},{"url":"https://github.com/CVEProject/cvelistV5/tree/main/cves/2022/41xxx/CVE-2022-41894.json","tags":["advisory"]},{"url":"https://github.com/tensorflow/tensorflow","tags":["package"]},{"url":"https://pypi.org/project/tensorflow","tags":["package"]}],"sources":{"cve":[{"id":"CVE-2022-41894","title":"Buffer overflow in `CONV_3D_TRANSPOSE` on TFLite","description":"TensorFlow is an open source platform for machine learning. The reference kernel of the `CONV_3D_TRANSPOSE` TensorFlow Lite operator wrongly increments the data_ptr when adding the bias to the result. Instead of `data_ptr += num_channels;` it should be `data_ptr += output_num_channels;` as if the number of input channels is different than the number of output channels, the wrong result will be returned and a buffer overflow will occur if num_channels \u003e output_num_channels. An attacker can craft a model with a specific number of input channels. It is then possible to write specific values through the bias of the layer outside the bounds of the buffer. This attack only works if the reference kernel resolver is used in the interpreter. We have patched the issue in GitHub commit 72c0bdcb25305b0b36842d746cc61d72658d2941. The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1, 2.9.3, and TensorFlow 2.8.4, as these are also affected and still in supported range.","status":"published","published":"2022-11-18T00:00:00Z","modified":"2025-04-22T16:04:55.192Z","cvss":[{"version":"3.1","score":7.1,"vector":"CVSS:3.1/AV:N/AC:H/PR:L/UI:R/S:U/C:H/I:H/A:H","level":"high","assessor":"cna"}],"cwes":["CWE-120"],"updated_at":"2026-11-02T14:34:28.038197Z"}],"euvd":[{"id":"EUVD-2022-7356","description":"TensorFlow is an open source platform for machine learning. The reference kernel of the `CONV_3D_TRANSPOSE` TensorFlow Lite operator wrongly increments the data_ptr when adding the bias to the result. Instead of `data_ptr += num_channels;` it should be `data_ptr += output_num_channels;` as if the number of input channels is different than the number of output channels, the wrong result will be returned and a buffer overflow will occur if num_channels \u003e output_num_channels. An attacker can craft a model with a specific number of input channels. It is then possible to write specific values through the bias of the layer outside the bounds of the buffer. This attack only works if the reference kernel resolver is used in the interpreter. We have patched the issue in GitHub commit 72c0bdcb25305b0b36842d746cc61d72658d2941. The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1, 2.9.3, and TensorFlow 2.8.4, as these are also affected and still in supported range.","status":"published","published":"2022-11-18T00:00:00Z","modified":"2025-04-22T16:04:55Z","cvss":[{"version":"3.1","score":7.1,"vector":"CVSS:3.1/AV:N/AC:H/PR:L/UI:R/S:U/C:H/I:H/A:H","level":"high"}],"updated_at":"2026-11-02T20:52:16.784071Z"}],"ghsa":[{"id":"GHSA-h6q3-vv32-2cq5","title":"Buffer overflow in `CONV_3D_TRANSPOSE` on TFLite","description":"### Impact\nThe reference kernel of the [`CONV_3D_TRANSPOSE`](https://github.com/tensorflow/tensorflow/blob/091e63f0ea33def7ecad661a5ac01dcafbafa90b/tensorflow/lite/kernels/internal/reference/conv3d_transpose.h#L121) TensorFlow Lite operator wrongly increments the data_ptr when adding the bias to the result.\n\nInstead of `data_ptr += num_channels;` it should be `data_ptr += output_num_channels;` as if the number of input channels is different than the number of output channels, the wrong result will be returned and a buffer overflow will occur if num_channels \u003e output_num_channels.\n\nAn attacker can craft a model with a specific number of input channels in a way similar to the attached example script. It is then possible to write specific values through the bias of the layer outside the bounds of the buffer. This attack only works if the reference kernel resolver is used in the interpreter (i.e. `experimental_op_resolver_type=tf.lite.experimental.OpResolverType.BUILTIN_REF` is used).\n```python\nimport tensorflow as tf\nmodel = tf.keras.Sequential(\n    [\n        tf.keras.layers.InputLayer(input_shape=(2, 2, 2, 1024), batch_size=1),\n        tf.keras.layers.Conv3DTranspose(\n            filters=8,\n            kernel_size=(2, 2, 2),\n            padding=\"same\",\n            data_format=\"channels_last\",\n        ),\n    ]\n)\n\nconverter = tf.lite.TFLiteConverter.from_keras_model(model)\ntflite_model = converter.convert()\n\ninterpreter = tf.lite.Interpreter(\n    model_content=tflite_model,\n    experimental_op_resolver_type=tf.lite.experimental.OpResolverType.BUILTIN_REF,\n)\n\ninterpreter.allocate_tensors()\ninterpreter.set_tensor(\n    interpreter.get_input_details()[0][\"index\"], tf.zeros(shape=[1, 2, 2, 2, 1024])\n)\ninterpreter.invoke()\n```\n\n### Patches\nWe have patched the issue in GitHub commit [72c0bdcb25305b0b36842d746cc61d72658d2941](https://github.com/tensorflow/tensorflow/commit/72c0bdcb25305b0b36842d746cc61d72658d2941).\n\nThe fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1, 2.9.3, and TensorFlow 2.8.4, as these are also affected and still in supported range.\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### Attribution\nThis vulnerability has been reported by Thibaut Goetghebuer-Planchon, Arm Ltd.\n","status":"published","published":"2022-11-21T20:44:24Z","modified":"2023-04-10T19:02:13Z","cvss":[{"version":"3.1","score":7.1,"vector":"CVSS:3.1/AV:N/AC:H/PR:L/UI:R/S:U/C:H/I:H/A:H","level":"high"}],"cwes":["CWE-120"],"updated_at":"2026-11-02T14:36:50.476488Z"}],"nvd":[{"id":"CVE-2022-41894","description":"TensorFlow is an open source platform for machine learning. The reference kernel of the `CONV_3D_TRANSPOSE` TensorFlow Lite operator wrongly increments the data_ptr when adding the bias to the result. Instead of `data_ptr += num_channels;` it should be `data_ptr += output_num_channels;` as if the number of input channels is different than the number of output channels, the wrong result will be returned and a buffer overflow will occur if num_channels \u003e output_num_channels. An attacker can craft a model with a specific number of input channels. It is then possible to write specific values through the bias of the layer outside the bounds of the buffer. This attack only works if the reference kernel resolver is used in the interpreter. We have patched the issue in GitHub commit 72c0bdcb25305b0b36842d746cc61d72658d2941. The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1, 2.9.3, and TensorFlow 2.8.4, as these are also affected and still in supported range.","status":"published","published":"2022-11-18T22:15:17.523Z","modified":"2026-06-17T05:04:00.723Z","cvss":[{"version":"3.1","score":7.1,"vector":"CVSS:3.1/AV:N/AC:H/PR:L/UI:R/S:U/C:H/I:H/A:H","level":"high","assessor":"secondary"},{"version":"3.1","score":8.1,"vector":"CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:H/I:H/A:H","level":"high","assessor":"primary"}],"cwes":["CWE-120"],"updated_at":"2026-11-02T14:07:32.395456Z"}],"osv":[{"id":"AZL-11532","title":"CVE-2022-41894 affecting package tensorflow for versions less than 2.11.0-1","description":"TensorFlow is an open source platform for machine learning. The reference kernel of the `CONV_3D_TRANSPOSE` TensorFlow Lite operator wrongly increments the data_ptr when adding the bias to the result. Instead of `data_ptr += num_channels;` it should be `data_ptr += output_num_channels;` as if the number of input channels is different than the number of output channels, the wrong result will be returned and a buffer overflow will occur if num_channels \u003e output_num_channels. An attacker can craft a model with a specific number of input channels. It is then possible to write specific values through the bias of the layer outside the bounds of the buffer. This attack only works if the reference kernel resolver is used in the interpreter. We have patched the issue in GitHub commit 72c0bdcb25305b0b36842d746cc61d72658d2941. The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1, 2.9.3, and TensorFlow 2.8.4, as these are also affected and still in supported range.","status":"published","published":"2022-11-18T22:15:17Z","modified":"2026-04-21T04:22:14.045579Z","cvss":[{"version":"3.1","score":8.1,"vector":"CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:H/I:H/A:H","level":"high"}],"updated_at":"2026-11-02T15:28:02.666535Z"},{"id":"CVE-2022-41894","title":"Buffer overflow in `CONV_3D_TRANSPOSE` on TFLite","description":"TensorFlow is an open source platform for machine learning. The reference kernel of the `CONV_3D_TRANSPOSE` TensorFlow Lite operator wrongly increments the data_ptr when adding the bias to the result. Instead of `data_ptr += num_channels;` it should be `data_ptr += output_num_channels;` as if the number of input channels is different than the number of output channels, the wrong result will be returned and a buffer overflow will occur if num_channels \u003e output_num_channels. An attacker can craft a model with a specific number of input channels. It is then possible to write specific values through the bias of the layer outside the bounds of the buffer. This attack only works if the reference kernel resolver is used in the interpreter. We have patched the issue in GitHub commit 72c0bdcb25305b0b36842d746cc61d72658d2941. The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1, 2.9.3, and TensorFlow 2.8.4, as these are also affected and still in supported range.","status":"published","published":"2022-11-18T00:00:00Z","modified":"2026-10-28T10:30:51.312787472Z","cvss":[{"version":"3.1","score":7.1,"vector":"CVSS:3.1/AV:N/AC:H/PR:L/UI:R/S:U/C:H/I:H/A:H","level":"high"}],"cwes":["CWE-120"],"updated_at":"2026-11-02T15:44:27.186787Z"},{"id":"GHSA-h6q3-vv32-2cq5","title":"Buffer overflow in `CONV_3D_TRANSPOSE` on TFLite","description":"### Impact\nThe reference kernel of the [`CONV_3D_TRANSPOSE`](https://github.com/tensorflow/tensorflow/blob/091e63f0ea33def7ecad661a5ac01dcafbafa90b/tensorflow/lite/kernels/internal/reference/conv3d_transpose.h#L121) TensorFlow Lite operator wrongly increments the data_ptr when adding the bias to the result.\n\nInstead of `data_ptr += num_channels;` it should be `data_ptr += output_num_channels;` as if the number of input channels is different than the number of output channels, the wrong result will be returned and a buffer overflow will occur if num_channels \u003e output_num_channels.\n\nAn attacker can craft a model with a specific number of input channels in a way similar to the attached example script. It is then possible to write specific values through the bias of the layer outside the bounds of the buffer. This attack only works if the reference kernel resolver is used in the interpreter (i.e. `experimental_op_resolver_type=tf.lite.experimental.OpResolverType.BUILTIN_REF` is used).\n```python\nimport tensorflow as tf\nmodel = tf.keras.Sequential(\n    [\n        tf.keras.layers.InputLayer(input_shape=(2, 2, 2, 1024), batch_size=1),\n        tf.keras.layers.Conv3DTranspose(\n            filters=8,\n            kernel_size=(2, 2, 2),\n            padding=\"same\",\n            data_format=\"channels_last\",\n        ),\n    ]\n)\n\nconverter = tf.lite.TFLiteConverter.from_keras_model(model)\ntflite_model = converter.convert()\n\ninterpreter = tf.lite.Interpreter(\n    model_content=tflite_model,\n    experimental_op_resolver_type=tf.lite.experimental.OpResolverType.BUILTIN_REF,\n)\n\ninterpreter.allocate_tensors()\ninterpreter.set_tensor(\n    interpreter.get_input_details()[0][\"index\"], tf.zeros(shape=[1, 2, 2, 2, 1024])\n)\ninterpreter.invoke()\n```\n\n### Patches\nWe have patched the issue in GitHub commit [72c0bdcb25305b0b36842d746cc61d72658d2941](https://github.com/tensorflow/tensorflow/commit/72c0bdcb25305b0b36842d746cc61d72658d2941).\n\nThe fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1, 2.9.3, and TensorFlow 2.8.4, as these are also affected and still in supported range.\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### Attribution\nThis vulnerability has been reported by Thibaut Goetghebuer-Planchon, Arm Ltd.\n","status":"published","published":"2022-11-21T20:44:24Z","modified":"2026-07-07T11:56:23.40530352Z","cvss":[{"version":"3.1","score":7.1,"vector":"CVSS:3.1/AV:N/AC:H/PR:L/UI:R/S:U/C:H/I:H/A:H","level":"high"}],"cwes":["CWE-120"],"updated_at":"2026-11-02T15:46:48.546324Z"},{"id":"PYSEC-2026-936","title":"Buffer overflow in `CONV_3D_TRANSPOSE` on TFLite","description":"### Impact\nThe reference kernel of the [`CONV_3D_TRANSPOSE`](https://github.com/tensorflow/tensorflow/blob/091e63f0ea33def7ecad661a5ac01dcafbafa90b/tensorflow/lite/kernels/internal/reference/conv3d_transpose.h#L121) TensorFlow Lite operator wrongly increments the data_ptr when adding the bias to the result.\n\nInstead of `data_ptr += num_channels;` it should be `data_ptr += output_num_channels;` as if the number of input channels is different than the number of output channels, the wrong result will be returned and a buffer overflow will occur if num_channels \u003e output_num_channels.\n\nAn attacker can craft a model with a specific number of input channels in a way similar to the attached example script. It is then possible to write specific values through the bias of the layer outside the bounds of the buffer. This attack only works if the reference kernel resolver is used in the interpreter (i.e. `experimental_op_resolver_type=tf.lite.experimental.OpResolverType.BUILTIN_REF` is used).\n```python\nimport tensorflow as tf\nmodel = tf.keras.Sequential(\n    [\n        tf.keras.layers.InputLayer(input_shape=(2, 2, 2, 1024), batch_size=1),\n        tf.keras.layers.Conv3DTranspose(\n            filters=8,\n            kernel_size=(2, 2, 2),\n            padding=\"same\",\n            data_format=\"channels_last\",\n        ),\n    ]\n)\n\nconverter = tf.lite.TFLiteConverter.from_keras_model(model)\ntflite_model = converter.convert()\n\ninterpreter = tf.lite.Interpreter(\n    model_content=tflite_model,\n    experimental_op_resolver_type=tf.lite.experimental.OpResolverType.BUILTIN_REF,\n)\n\ninterpreter.allocate_tensors()\ninterpreter.set_tensor(\n    interpreter.get_input_details()[0][\"index\"], tf.zeros(shape=[1, 2, 2, 2, 1024])\n)\ninterpreter.invoke()\n```\n\n### Patches\nWe have patched the issue in GitHub commit [72c0bdcb25305b0b36842d746cc61d72658d2941](https://github.com/tensorflow/tensorflow/commit/72c0bdcb25305b0b36842d746cc61d72658d2941).\n\nThe fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1, 2.9.3, and TensorFlow 2.8.4, as these are also affected and still in supported range.\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### Attribution\nThis vulnerability has been reported by Thibaut Goetghebuer-Planchon, Arm Ltd.\n","status":"published","published":"2026-07-07T10:17:24.149856Z","modified":"2026-07-07T11:56:23.40530352Z","cvss":[{"version":"3.1","score":7.1,"vector":"CVSS:3.1/AV:N/AC:H/PR:L/UI:R/S:U/C:H/I:H/A:H","level":"high"}],"updated_at":"2026-11-02T15:52:58.117212Z"},{"id":"BIT-tensorflow-2022-41894","title":"Buffer overflow in `CONV_3D_TRANSPOSE` on TFLite","description":"TensorFlow is an open source platform for machine learning. The reference kernel of the `CONV_3D_TRANSPOSE` TensorFlow Lite operator wrongly increments the data_ptr when adding the bias to the result. Instead of `data_ptr += num_channels;` it should be `data_ptr += output_num_channels;` as if the number of input channels is different than the number of output channels, the wrong result will be returned and a buffer overflow will occur if num_channels \u003e output_num_channels. An attacker can craft a model with a specific number of input channels. It is then possible to write specific values through the bias of the layer outside the bounds of the buffer. This attack only works if the reference kernel resolver is used in the interpreter. We have patched the issue in GitHub commit 72c0bdcb25305b0b36842d746cc61d72658d2941. The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1, 2.9.3, and TensorFlow 2.8.4, as these are also affected and still in supported range.","status":"published","published":"2024-03-06T11:10:59.811Z","modified":"2026-09-08T08:48:20.621302829Z","updated_at":"2026-11-02T15:28:36.104787Z"}]}}