GHSA-2xgj-xhgf-ggjv
Vulnerability from github
Impact
An attacker can trigger a heap buffer overflow in Eigen implementation of tf.raw_ops.BandedTriangularSolve
:
```python import tensorflow as tf import numpy as np
matrix_array = np.array([]) matrix_tensor = tf.convert_to_tensor(np.reshape(matrix_array,(0,1)),dtype=tf.float32) rhs_array = np.array([1,1]) rhs_tensor = tf.convert_to_tensor(np.reshape(rhs_array,(1,2)),dtype=tf.float32) tf.raw_ops.BandedTriangularSolve(matrix=matrix_tensor,rhs=rhs_tensor) ```
The implementation calls ValidateInputTensors
for input validation but fails to validate that the two tensors are not empty:
```cc void ValidateInputTensors(OpKernelContext* ctx, const Tensor& in0, const Tensor& in1) { OP_REQUIRES( ctx, in0.dims() >= 2, errors::InvalidArgument("In[0] ndims must be >= 2: ", in0.dims()));
OP_REQUIRES( ctx, in1.dims() >= 2, errors::InvalidArgument("In[1] ndims must be >= 2: ", in1.dims())); } ```
Furthermore, since OP_REQUIRES
macro only stops execution of current function after setting ctx->status()
to a non-OK value, callers of helper functions that use OP_REQUIRES
must check value of ctx->status()
before continuing. This doesn't happen in this op's implementation, hence the validation that is present is also not effective.
Patches
We have patched the issue in GitHub commit ba6822bd7b7324ba201a28b2f278c29a98edbef2 followed by GitHub commit 0ab290774f91a23bebe30a358fde4e53ab4876a0.
The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.
For more information
Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.
Attribution
This vulnerability has been reported by Ye Zhang and Yakun Zhang of Baidu X-Team.
{ "affected": [ { "package": { "ecosystem": "PyPI", "name": "tensorflow" }, "ranges": [ { "events": [ { "introduced": "0" }, { "fixed": "2.1.4" } ], "type": "ECOSYSTEM" } ] }, { "package": { "ecosystem": "PyPI", "name": "tensorflow" }, "ranges": [ { "events": [ { "introduced": "2.2.0" }, { "fixed": "2.2.3" } ], "type": "ECOSYSTEM" } ] }, { "package": { "ecosystem": "PyPI", "name": "tensorflow" }, "ranges": [ { "events": [ { "introduced": "2.3.0" }, { "fixed": "2.3.3" } ], "type": "ECOSYSTEM" } ] }, { "package": { "ecosystem": "PyPI", "name": "tensorflow" }, "ranges": [ { "events": [ { "introduced": "2.4.0" }, { "fixed": "2.4.2" } ], "type": "ECOSYSTEM" } ] }, { "package": { "ecosystem": "PyPI", "name": "tensorflow-cpu" }, "ranges": [ { "events": [ { "introduced": "0" }, { "fixed": "2.1.4" } ], "type": "ECOSYSTEM" } ] }, { "package": { "ecosystem": "PyPI", "name": "tensorflow-cpu" }, "ranges": [ { "events": [ { "introduced": "2.2.0" }, { "fixed": "2.2.3" } ], "type": "ECOSYSTEM" } ] }, { "package": { "ecosystem": "PyPI", "name": "tensorflow-cpu" }, "ranges": [ { "events": [ { "introduced": "2.3.0" }, { "fixed": "2.3.3" } ], "type": "ECOSYSTEM" } ] }, { "package": { "ecosystem": "PyPI", "name": "tensorflow-cpu" }, "ranges": [ { "events": [ { "introduced": "2.4.0" }, { "fixed": "2.4.2" } ], "type": "ECOSYSTEM" } ] }, { "package": { "ecosystem": "PyPI", "name": "tensorflow-gpu" }, "ranges": [ { "events": [ { "introduced": "0" }, { "fixed": "2.1.4" } ], "type": "ECOSYSTEM" } ] }, { "package": { "ecosystem": "PyPI", "name": "tensorflow-gpu" }, "ranges": [ { "events": [ { "introduced": "2.2.0" }, { "fixed": "2.2.3" } ], "type": "ECOSYSTEM" } ] }, { "package": { "ecosystem": "PyPI", "name": "tensorflow-gpu" }, "ranges": [ { "events": [ { "introduced": "2.3.0" }, { "fixed": "2.3.3" } ], "type": "ECOSYSTEM" } ] }, { "package": { "ecosystem": "PyPI", "name": "tensorflow-gpu" }, "ranges": [ { "events": [ { "introduced": "2.4.0" }, { "fixed": "2.4.2" } ], "type": "ECOSYSTEM" } ] } ], "aliases": [ "CVE-2021-29612" ], "database_specific": { "cwe_ids": [ "CWE-120", "CWE-787" ], "github_reviewed": true, "github_reviewed_at": "2021-05-17T21:56:29Z", "nvd_published_at": "2021-05-14T20:15:00Z", "severity": "LOW" }, "details": "### Impact\nAn attacker can trigger a heap buffer overflow in Eigen implementation of `tf.raw_ops.BandedTriangularSolve`:\n\n```python\nimport tensorflow as tf\nimport numpy as np\n \nmatrix_array = np.array([])\nmatrix_tensor = tf.convert_to_tensor(np.reshape(matrix_array,(0,1)),dtype=tf.float32)\nrhs_array = np.array([1,1])\nrhs_tensor = tf.convert_to_tensor(np.reshape(rhs_array,(1,2)),dtype=tf.float32)\ntf.raw_ops.BandedTriangularSolve(matrix=matrix_tensor,rhs=rhs_tensor)\n```\n\nThe [implementation](https://github.com/tensorflow/tensorflow/blob/eccb7ec454e6617738554a255d77f08e60ee0808/tensorflow/core/kernels/linalg/banded_triangular_solve_op.cc#L269-L278) calls `ValidateInputTensors` for input validation but fails to validate that the two tensors are not empty:\n \n```cc\nvoid ValidateInputTensors(OpKernelContext* ctx, const Tensor\u0026 in0, const Tensor\u0026 in1) {\n OP_REQUIRES(\n ctx, in0.dims() \u003e= 2, \n errors::InvalidArgument(\"In[0] ndims must be \u003e= 2: \", in0.dims()));\n\n OP_REQUIRES(\n ctx, in1.dims() \u003e= 2,\n errors::InvalidArgument(\"In[1] ndims must be \u003e= 2: \", in1.dims()));\n}\n``` \n\nFurthermore, since `OP_REQUIRES` macro only stops execution of current function after setting `ctx-\u003estatus()` to a non-OK value, callers of helper functions that use `OP_REQUIRES` must check value of `ctx-\u003estatus()` before continuing. This doesn\u0027t happen [in this op\u0027s implementation](https://github.com/tensorflow/tensorflow/blob/eccb7ec454e6617738554a255d77f08e60ee0808/tensorflow/core/kernels/linalg/banded_triangular_solve_op.cc#L219), hence the validation that is present is also not effective.\n\n### Patches\nWe have patched the issue in GitHub commit [ba6822bd7b7324ba201a28b2f278c29a98edbef2](https://github.com/tensorflow/tensorflow/commit/ba6822bd7b7324ba201a28b2f278c29a98edbef2) followed by GitHub commit [0ab290774f91a23bebe30a358fde4e53ab4876a0](https://github.com/tensorflow/tensorflow/commit/0ab290774f91a23bebe30a358fde4e53ab4876a0).\n\nThe fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, 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 Ye Zhang and Yakun Zhang of Baidu X-Team.", "id": "GHSA-2xgj-xhgf-ggjv", "modified": "2024-11-13T16:02:19Z", "published": "2021-05-21T14:28:37Z", "references": [ { "type": "WEB", "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-2xgj-xhgf-ggjv" }, { "type": "ADVISORY", "url": "https://nvd.nist.gov/vuln/detail/CVE-2021-29612" }, { "type": "WEB", "url": "https://github.com/tensorflow/tensorflow/commit/0ab290774f91a23bebe30a358fde4e53ab4876a0" }, { "type": "WEB", "url": "https://github.com/tensorflow/tensorflow/commit/ba6822bd7b7324ba201a28b2f278c29a98edbef2" }, { "type": "WEB", "url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-540.yaml" }, { "type": "WEB", "url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-738.yaml" }, { "type": "WEB", "url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-249.yaml" } ], "schema_version": "1.4.0", "severity": [ { "score": "CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:L/A:L", "type": "CVSS_V3" }, { "score": "CVSS:4.0/AV:L/AC:L/AT:P/PR:L/UI:N/VC:N/VI:L/VA:L/SC:N/SI:N/SA:N", "type": "CVSS_V4" } ], "summary": "Heap buffer overflow in `BandedTriangularSolve`" }
Sightings
Author | Source | Type | Date |
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Nomenclature
- Seen: The vulnerability was mentioned, discussed, or seen somewhere by the user.
- Confirmed: The vulnerability is confirmed from an analyst perspective.
- Exploited: This vulnerability was exploited and seen by the user reporting the sighting.
- Patched: This vulnerability was successfully patched by the user reporting the sighting.
- Not exploited: This vulnerability was not exploited or seen by the user reporting the sighting.
- Not confirmed: The user expresses doubt about the veracity of the vulnerability.
- Not patched: This vulnerability was not successfully patched by the user reporting the sighting.