{"id":"CVE-2022-29198","aliases":["BIT-tensorflow-2022-29198","EUVD-2022-4559","GHSA-mg66-qvc5-rm93","PYSEC-2026-1018","PYSEC-2026-3214","PYSEC-2026-3350"],"status":"published","title":"Missing validation causes denial of service in TensorFlow via `SparseTensorToCSRSparseMatrix`","description":"TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, the implementation of `tf.raw_ops.SparseTensorToCSRSparseMatrix` does not fully validate the input arguments. This results in a `CHECK`-failure which can be used to trigger a denial of service attack. The code assumes `dense_shape` is a vector and `indices` is a matrix (as part of requirements for sparse tensors) but there is no validation for this. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.","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":"2026-11-10","score":0.00325,"percentile":0.2581,"model":"v2026.06.15"},"facts":{"in_kev":false,"known_exploited":false,"public_exploit":false,"fix_available":true},"cwes":["CWE-20"],"published":"2022-05-20T21:50:12Z","modified":"2026-11-11T02:45:42Z","fixes":[{"ecosystem":"PyPI","package":"tensorflow","fixed_in":["2.6.4","2.7.2","2.8.1"],"sources":["ghsa","osv"]},{"ecosystem":"PyPI","package":"tensorflow-cpu","fixed_in":["2.6.4","2.7.2","2.8.1"],"sources":["ghsa","osv"]},{"ecosystem":"PyPI","package":"tensorflow-gpu","fixed_in":["2.6.4","2.7.2","2.8.1"],"sources":["ghsa","osv"]},{"ecosystem":"Bitnami","package":"tensorflow","fixed_in":["2.6.4","2.7.2"],"sources":["osv"]}],"affected":[{"source":"ghsa","ecosystem":"PyPI","package":"tensorflow","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"0"},{"fixed":"2.6.4"}]}]},{"source":"ghsa","ecosystem":"PyPI","package":"tensorflow","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"2.7.0"},{"fixed":"2.7.2"}]}]},{"source":"ghsa","ecosystem":"PyPI","package":"tensorflow","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"2.8.0"},{"fixed":"2.8.1"}]}]},{"source":"ghsa","ecosystem":"PyPI","package":"tensorflow-cpu","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"0"},{"fixed":"2.6.4"}]}]},{"source":"ghsa","ecosystem":"PyPI","package":"tensorflow-cpu","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"2.7.0"},{"fixed":"2.7.2"}]}]},{"source":"ghsa","ecosystem":"PyPI","package":"tensorflow-cpu","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"2.8.0"},{"fixed":"2.8.1"}]}]},{"source":"ghsa","ecosystem":"PyPI","package":"tensorflow-gpu","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"0"},{"fixed":"2.6.4"}]}]},{"source":"ghsa","ecosystem":"PyPI","package":"tensorflow-gpu","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"2.7.0"},{"fixed":"2.7.2"}]}]},{"source":"ghsa","ecosystem":"PyPI","package":"tensorflow-gpu","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"2.8.0"},{"fixed":"2.8.1"}]}]},{"source":"osv","ecosystem":"Bitnami","package":"tensorflow","ranges":[{"type":"SEMVER","events":[{"introduced":"0"},{"fixed":"2.6.4"},{"introduced":"2.7.0"},{"fixed":"2.7.2"}]}]},{"source":"osv","ecosystem":"PyPI","package":"tensorflow","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"0"},{"fixed":"2.6.4"}]}],"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"]},{"source":"osv","ecosystem":"PyPI","package":"tensorflow","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"2.7.0"},{"fixed":"2.7.2"}]}],"versions":["2.7.0","2.7.1"]},{"source":"osv","ecosystem":"PyPI","package":"tensorflow","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"2.8.0"},{"fixed":"2.8.1"}]}],"versions":["2.8.0"]},{"source":"osv","ecosystem":"PyPI","package":"tensorflow-cpu","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"0"},{"fixed":"2.6.4"}]}],"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","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"]},{"source":"osv","ecosystem":"PyPI","package":"tensorflow-cpu","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"2.7.0"},{"fixed":"2.7.2"}]}],"versions":["2.7.0","2.7.1"]},{"source":"osv","ecosystem":"PyPI","package":"tensorflow-cpu","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"2.8.0"},{"fixed":"2.8.1"}]}],"versions":["2.8.0"]},{"source":"osv","ecosystem":"PyPI","package":"tensorflow-gpu","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"0"},{"fixed":"2.6.4"}]}],"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","2.6.1","2.6.2","2.6.3"]},{"source":"osv","ecosystem":"PyPI","package":"tensorflow-gpu","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"2.7.0"},{"fixed":"2.7.2"}]}],"versions":["2.7.0","2.7.1"]},{"source":"osv","ecosystem":"PyPI"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Notes","Third Party Advisory","advisory","web"]},{"url":"https://github.com/tensorflow/tensorflow/releases/tag/v2.7.2","tags":["x_refsource_MISC","x_transferred","Release Notes","Third Party Advisory","advisory","web"]},{"url":"https://github.com/tensorflow/tensorflow/releases/tag/v2.8.1","tags":["x_refsource_MISC","x_transferred","Release Notes","Third Party Advisory","advisory","web"]},{"url":"https://github.com/tensorflow/tensorflow/releases/tag/v2.9.0","tags":["x_refsource_MISC","x_transferred","Release Notes","Third Party Advisory","advisory","web"]},{"url":"https://github.com/tensorflow/tensorflow/security/advisories/GHSA-mg66-qvc5-rm93","tags":["x_refsource_CONFIRM","x_transferred","Exploit","Patch","Third Party Advisory","advisory","web"]},{"url":"https://github.com/tensorflow/tensorflow/commit/ea50a40e84f6bff15a0912728e35b657548cef11","tags":["x_refsource_MISC","x_transferred","Patch","Third Party Advisory","advisory","fix","web"]},{"url":"https://github.com/tensorflow/tensorflow/blob/f3b9bf4c3c0597563b289c0512e98d4ce81f886e/tensorflow/core/kernels/sparse/sparse_tensor_to_csr_sparse_matrix_op.cc#L65-L119","tags":["x_refsource_MISC","x_transferred","Third Party Advisory","advisory","web"]},{"url":"https://nvd.nist.gov/vuln/detail/CVE-2022-29198","tags":["web","advisory"]},{"url":"https://github.com/advisories/GHSA-mg66-qvc5-rm93","tags":["advisory"]},{"url":"https://github.com/CVEProject/cvelistV5/tree/main/cves/2022/29xxx/CVE-2022-29198.json","tags":["advisory"]},{"url":"https://github.com/tensorflow/tensorflow","tags":["package"]},{"url":"https://pypi.org/project/tensorflow-gpu","tags":["package"]},{"url":"https://pypi.org/project/tensorflow","tags":["package"]},{"url":"https://pypi.org/project/tensorflow-cpu","tags":["package"]}],"sources":{"cve":[{"id":"CVE-2022-29198","title":"Missing validation causes denial of service in TensorFlow via `SparseTensorToCSRSparseMatrix`","description":"TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, the implementation of `tf.raw_ops.SparseTensorToCSRSparseMatrix` does not fully validate the input arguments. This results in a `CHECK`-failure which can be used to trigger a denial of service attack. The code assumes `dense_shape` is a vector and `indices` is a matrix (as part of requirements for sparse tensors) but there is no validation for this. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.","status":"published","published":"2022-05-20T21:50:12Z","modified":"2025-04-22T17:59:29.034Z","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-20"],"updated_at":"2026-11-02T14:34:35.945659Z"}],"euvd":[{"id":"EUVD-2022-4559","description":"TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, the implementation of `tf.raw_ops.SparseTensorToCSRSparseMatrix` does not fully validate the input arguments. This results in a `CHECK`-failure which can be used to trigger a denial of service attack. The code assumes `dense_shape` is a vector and `indices` is a matrix (as part of requirements for sparse tensors) but there is no validation for this. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.","status":"published","published":"2022-05-20T21:50:12Z","modified":"2025-04-22T17:59:29Z","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":"2026-11-02T20:52:15.540112Z"}],"ghsa":[{"id":"GHSA-mg66-qvc5-rm93","title":"Missing validation causes denial of service via `SparseTensorToCSRSparseMatrix`","description":"### Impact\nThe implementation of [`tf.raw_ops.SparseTensorToCSRSparseMatrix`](https://github.com/tensorflow/tensorflow/blob/f3b9bf4c3c0597563b289c0512e98d4ce81f886e/tensorflow/core/kernels/sparse/sparse_tensor_to_csr_sparse_matrix_op.cc#L65-L119) does not fully validate the input arguments. This results in a `CHECK`-failure which can be used to trigger a denial of service attack:\n\n```python\nimport tensorflow as tf\n\nindices = tf.constant(53, shape=[3], dtype=tf.int64)\nvalues = tf.constant(0.554979503, shape=[218650], dtype=tf.float32)\ndense_shape = tf.constant(53, shape=[3], dtype=tf.int64)\n    \ntf.raw_ops.SparseTensorToCSRSparseMatrix(\n  indices=indices,\n  values=values,\n  dense_shape=dense_shape)\n```\n\nThe code assumes `dense_shape` is a vector and `indices` is a matrix (as part of requirements for sparse tensors) but there is no validation for this:\n  \n```cc\n    const Tensor\u0026 indices = ctx-\u003einput(0);\n    const Tensor\u0026 values = ctx-\u003einput(1);\n    const Tensor\u0026 dense_shape = ctx-\u003einput(2);\n    const int rank = dense_shape.NumElements();\n    OP_REQUIRES(ctx, rank == 2 || rank == 3,\n                errors::InvalidArgument(\"SparseTensor must have rank 2 or 3; \",\n                                        \"but indices has rank: \", rank));\n    auto dense_shape_vec = dense_shape.vec\u003cint64_t\u003e();\n    // ...\n    OP_REQUIRES_OK(\n        ctx,\n        coo_to_csr(batch_size, num_rows, indices.template matrix\u003cint64_t\u003e(),\n                   batch_ptr.vec\u003cint32\u003e(), csr_row_ptr.vec\u003cint32\u003e(),\n                   csr_col_ind.vec\u003cint32\u003e()));\n```\n\n### Patches\nWe have patched the issue in GitHub commit [ea50a40e84f6bff15a0912728e35b657548cef11](https://github.com/tensorflow/tensorflow/commit/ea50a40e84f6bff15a0912728e35b657548cef11).\n\nThe fix will be included in TensorFlow 2.9.0. We will also cherrypick this commit on TensorFlow 2.8.1, TensorFlow 2.7.2, and TensorFlow 2.6.4, 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 Neophytos Christou from Secure Systems Lab at Brown University.","status":"published","published":"2022-05-24T22:08:45Z","modified":"2023-01-27T05:02:16Z","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"}],"cwes":["CWE-20"],"updated_at":"2026-11-02T14:36:17.742171Z"}],"nvd":[{"id":"CVE-2022-29198","description":"TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, the implementation of `tf.raw_ops.SparseTensorToCSRSparseMatrix` does not fully validate the input arguments. This results in a `CHECK`-failure which can be used to trigger a denial of service attack. The code assumes `dense_shape` is a vector and `indices` is a matrix (as part of requirements for sparse tensors) but there is no validation for this. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.","status":"published","published":"2022-05-20T22:16:40.81Z","modified":"2026-06-17T04:39:47.997Z","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-20"],"updated_at":"2026-11-02T14:07:20.264424Z"}],"osv":[{"id":"CVE-2022-29198","title":"Missing validation causes denial of service in TensorFlow via `SparseTensorToCSRSparseMatrix`","description":"TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, the implementation of `tf.raw_ops.SparseTensorToCSRSparseMatrix` does not fully validate the input arguments. This results in a `CHECK`-failure which can be used to trigger a denial of service attack. The code assumes `dense_shape` is a vector and `indices` is a matrix (as part of requirements for sparse tensors) but there is no validation for this. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.","status":"published","published":"2022-05-20T21:50:12Z","modified":"2026-11-11T07:19:28.573165595Z","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"}],"cwes":["CWE-20"],"updated_at":"2026-11-11T02:50:43.46164Z"},{"id":"GHSA-mg66-qvc5-rm93","title":"Missing validation causes denial of service via `SparseTensorToCSRSparseMatrix`","description":"### Impact\nThe implementation of [`tf.raw_ops.SparseTensorToCSRSparseMatrix`](https://github.com/tensorflow/tensorflow/blob/f3b9bf4c3c0597563b289c0512e98d4ce81f886e/tensorflow/core/kernels/sparse/sparse_tensor_to_csr_sparse_matrix_op.cc#L65-L119) does not fully validate the input arguments. This results in a `CHECK`-failure which can be used to trigger a denial of service attack:\n\n```python\nimport tensorflow as tf\n\nindices = tf.constant(53, shape=[3], dtype=tf.int64)\nvalues = tf.constant(0.554979503, shape=[218650], dtype=tf.float32)\ndense_shape = tf.constant(53, shape=[3], dtype=tf.int64)\n    \ntf.raw_ops.SparseTensorToCSRSparseMatrix(\n  indices=indices,\n  values=values,\n  dense_shape=dense_shape)\n```\n\nThe code assumes `dense_shape` is a vector and `indices` is a matrix (as part of requirements for sparse tensors) but there is no validation for this:\n  \n```cc\n    const Tensor\u0026 indices = ctx-\u003einput(0);\n    const Tensor\u0026 values = ctx-\u003einput(1);\n    const Tensor\u0026 dense_shape = ctx-\u003einput(2);\n    const int rank = dense_shape.NumElements();\n    OP_REQUIRES(ctx, rank == 2 || rank == 3,\n                errors::InvalidArgument(\"SparseTensor must have rank 2 or 3; \",\n                                        \"but indices has rank: \", rank));\n    auto dense_shape_vec = dense_shape.vec\u003cint64_t\u003e();\n    // ...\n    OP_REQUIRES_OK(\n        ctx,\n        coo_to_csr(batch_size, num_rows, indices.template matrix\u003cint64_t\u003e(),\n                   batch_ptr.vec\u003cint32\u003e(), csr_row_ptr.vec\u003cint32\u003e(),\n                   csr_col_ind.vec\u003cint32\u003e()));\n```\n\n### Patches\nWe have patched the issue in GitHub commit [ea50a40e84f6bff15a0912728e35b657548cef11](https://github.com/tensorflow/tensorflow/commit/ea50a40e84f6bff15a0912728e35b657548cef11).\n\nThe fix will be included in TensorFlow 2.9.0. We will also cherrypick this commit on TensorFlow 2.8.1, TensorFlow 2.7.2, and TensorFlow 2.6.4, 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 Neophytos Christou from Secure Systems Lab at Brown University.","status":"published","published":"2022-05-24T22:08:45Z","modified":"2026-07-13T16:43:41.80643227Z","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"}],"cwes":["CWE-20"],"updated_at":"2026-11-02T15:46:51.811331Z"},{"id":"PYSEC-2026-1018","title":"Missing validation causes denial of service via `SparseTensorToCSRSparseMatrix`","description":"### Impact\nThe implementation of [`tf.raw_ops.SparseTensorToCSRSparseMatrix`](https://github.com/tensorflow/tensorflow/blob/f3b9bf4c3c0597563b289c0512e98d4ce81f886e/tensorflow/core/kernels/sparse/sparse_tensor_to_csr_sparse_matrix_op.cc#L65-L119) does not fully validate the input arguments. This results in a `CHECK`-failure which can be used to trigger a denial of service attack:\n\n```python\nimport tensorflow as tf\n\nindices = tf.constant(53, shape=[3], dtype=tf.int64)\nvalues = tf.constant(0.554979503, shape=[218650], dtype=tf.float32)\ndense_shape = tf.constant(53, shape=[3], dtype=tf.int64)\n    \ntf.raw_ops.SparseTensorToCSRSparseMatrix(\n  indices=indices,\n  values=values,\n  dense_shape=dense_shape)\n```\n\nThe code assumes `dense_shape` is a vector and `indices` is a matrix (as part of requirements for sparse tensors) but there is no validation for this:\n  \n```cc\n    const Tensor\u0026 indices = ctx-\u003einput(0);\n    const Tensor\u0026 values = ctx-\u003einput(1);\n    const Tensor\u0026 dense_shape = ctx-\u003einput(2);\n    const int rank = dense_shape.NumElements();\n    OP_REQUIRES(ctx, rank == 2 || rank == 3,\n                errors::InvalidArgument(\"SparseTensor must have rank 2 or 3; \",\n                                        \"but indices has rank: \", rank));\n    auto dense_shape_vec = dense_shape.vec\u003cint64_t\u003e();\n    // ...\n    OP_REQUIRES_OK(\n        ctx,\n        coo_to_csr(batch_size, num_rows, indices.template matrix\u003cint64_t\u003e(),\n                   batch_ptr.vec\u003cint32\u003e(), csr_row_ptr.vec\u003cint32\u003e(),\n                   csr_col_ind.vec\u003cint32\u003e()));\n```\n\n### Patches\nWe have patched the issue in GitHub commit [ea50a40e84f6bff15a0912728e35b657548cef11](https://github.com/tensorflow/tensorflow/commit/ea50a40e84f6bff15a0912728e35b657548cef11).\n\nThe fix will be included in TensorFlow 2.9.0. We will also cherrypick this commit on TensorFlow 2.8.1, TensorFlow 2.7.2, and TensorFlow 2.6.4, 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 Neophytos Christou from Secure Systems Lab at Brown University.","status":"published","published":"2026-07-06T08:03:29.409171Z","modified":"2026-07-13T16:43:41.80643227Z","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":"2026-11-02T15:52:54.622769Z"},{"id":"PYSEC-2026-3214","title":"Missing validation causes denial of service via `SparseTensorToCSRSparseMatrix`","description":"### Impact\nThe implementation of [`tf.raw_ops.SparseTensorToCSRSparseMatrix`](https://github.com/tensorflow/tensorflow/blob/f3b9bf4c3c0597563b289c0512e98d4ce81f886e/tensorflow/core/kernels/sparse/sparse_tensor_to_csr_sparse_matrix_op.cc#L65-L119) does not fully validate the input arguments. This results in a `CHECK`-failure which can be used to trigger a denial of service attack:\n\n```python\nimport tensorflow as tf\n\nindices = tf.constant(53, shape=[3], dtype=tf.int64)\nvalues = tf.constant(0.554979503, shape=[218650], dtype=tf.float32)\ndense_shape = tf.constant(53, shape=[3], dtype=tf.int64)\n    \ntf.raw_ops.SparseTensorToCSRSparseMatrix(\n  indices=indices,\n  values=values,\n  dense_shape=dense_shape)\n```\n\nThe code assumes `dense_shape` is a vector and `indices` is a matrix (as part of requirements for sparse tensors) but there is no validation for this:\n  \n```cc\n    const Tensor\u0026 indices = ctx-\u003einput(0);\n    const Tensor\u0026 values = ctx-\u003einput(1);\n    const Tensor\u0026 dense_shape = ctx-\u003einput(2);\n    const int rank = dense_shape.NumElements();\n    OP_REQUIRES(ctx, rank == 2 || rank == 3,\n                errors::InvalidArgument(\"SparseTensor must have rank 2 or 3; \",\n                                        \"but indices has rank: \", rank));\n    auto dense_shape_vec = dense_shape.vec\u003cint64_t\u003e();\n    // ...\n    OP_REQUIRES_OK(\n        ctx,\n        coo_to_csr(batch_size, num_rows, indices.template matrix\u003cint64_t\u003e(),\n                   batch_ptr.vec\u003cint32\u003e(), csr_row_ptr.vec\u003cint32\u003e(),\n                   csr_col_ind.vec\u003cint32\u003e()));\n```\n\n### Patches\nWe have patched the issue in GitHub commit [ea50a40e84f6bff15a0912728e35b657548cef11](https://github.com/tensorflow/tensorflow/commit/ea50a40e84f6bff15a0912728e35b657548cef11).\n\nThe fix will be included in TensorFlow 2.9.0. We will also cherrypick this commit on TensorFlow 2.8.1, TensorFlow 2.7.2, and TensorFlow 2.6.4, 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 Neophytos Christou from Secure Systems Lab at Brown University.","status":"published","published":"2026-07-09T16:49:48.479696Z","modified":"2026-07-13T16:43:41.80643227Z","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":"2026-11-02T15:52:56.429374Z"},{"id":"PYSEC-2026-3350","title":"Missing validation causes denial of service via `SparseTensorToCSRSparseMatrix`","description":"### Impact\nThe implementation of [`tf.raw_ops.SparseTensorToCSRSparseMatrix`](https://github.com/tensorflow/tensorflow/blob/f3b9bf4c3c0597563b289c0512e98d4ce81f886e/tensorflow/core/kernels/sparse/sparse_tensor_to_csr_sparse_matrix_op.cc#L65-L119) does not fully validate the input arguments. This results in a `CHECK`-failure which can be used to trigger a denial of service attack:\n\n```python\nimport tensorflow as tf\n\nindices = tf.constant(53, shape=[3], dtype=tf.int64)\nvalues = tf.constant(0.554979503, shape=[218650], dtype=tf.float32)\ndense_shape = tf.constant(53, shape=[3], dtype=tf.int64)\n    \ntf.raw_ops.SparseTensorToCSRSparseMatrix(\n  indices=indices,\n  values=values,\n  dense_shape=dense_shape)\n```\n\nThe code assumes `dense_shape` is a vector and `indices` is a matrix (as part of requirements for sparse tensors) but there is no validation for this:\n  \n```cc\n    const Tensor\u0026 indices = ctx-\u003einput(0);\n    const Tensor\u0026 values = ctx-\u003einput(1);\n    const Tensor\u0026 dense_shape = ctx-\u003einput(2);\n    const int rank = dense_shape.NumElements();\n    OP_REQUIRES(ctx, rank == 2 || rank == 3,\n                errors::InvalidArgument(\"SparseTensor must have rank 2 or 3; \",\n                                        \"but indices has rank: \", rank));\n    auto dense_shape_vec = dense_shape.vec\u003cint64_t\u003e();\n    // ...\n    OP_REQUIRES_OK(\n        ctx,\n        coo_to_csr(batch_size, num_rows, indices.template matrix\u003cint64_t\u003e(),\n                   batch_ptr.vec\u003cint32\u003e(), csr_row_ptr.vec\u003cint32\u003e(),\n                   csr_col_ind.vec\u003cint32\u003e()));\n```\n\n### Patches\nWe have patched the issue in GitHub commit [ea50a40e84f6bff15a0912728e35b657548cef11](https://github.com/tensorflow/tensorflow/commit/ea50a40e84f6bff15a0912728e35b657548cef11).\n\nThe fix will be included in TensorFlow 2.9.0. We will also cherrypick this commit on TensorFlow 2.8.1, TensorFlow 2.7.2, and TensorFlow 2.6.4, 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 Neophytos Christou from Secure Systems Lab at Brown University.","status":"published","published":"2026-07-09T16:49:48.570255Z","modified":"2026-07-13T16:43:41.80643227Z","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":"2026-11-02T15:52:56.847083Z"},{"id":"BIT-tensorflow-2022-29198","title":"Missing validation causes denial of service in TensorFlow via `SparseTensorToCSRSparseMatrix`","description":"TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, the implementation of `tf.raw_ops.SparseTensorToCSRSparseMatrix` does not fully validate the input arguments. This results in a `CHECK`-failure which can be used to trigger a denial of service attack. The code assumes `dense_shape` is a vector and `indices` is a matrix (as part of requirements for sparse tensors) but there is no validation for this. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.","status":"published","published":"2024-03-06T11:14:32.596Z","modified":"2026-09-08T08:48:25.227890349Z","updated_at":"2026-11-02T15:28:35.51001Z"}]}}