ghsa-q263-fvxm-m5mw
Vulnerability from github
4.8 (Medium) - CVSS:4.0/AV:L/AC:L/AT:N/PR:L/UI:N/VC:N/VI:L/VA:L/SC:N/SI:N/SA:N
Impact
Under certain cases, loading a saved model can result in accessing uninitialized memory while building the computation graph. The MakeEdge
function creates an edge between one output tensor of the src
node (given by output_index
) and the input slot of the dst
node (given by input_index
). This is only possible if the types of the tensors on both sides coincide, so the function begins by obtaining the corresponding DataType
values and comparing these for equality:
cc
DataType src_out = src->output_type(output_index);
DataType dst_in = dst->input_type(input_index);
//...
However, there is no check that the indices point to inside of the arrays they index into. Thus, this can result in accessing data out of bounds of the corresponding heap allocated arrays.
In most scenarios, this can manifest as unitialized data access, but if the index points far away from the boundaries of the arrays this can be used to leak addresses from the library.
Patches
We have patched the issue in GitHub commit 0cc38aaa4064fd9e79101994ce9872c6d91f816b and will release TensorFlow 2.4.0 containing the patch. TensorFlow nightly packages after this commit will also have the issue resolved.
Since this issue also impacts TF versions before 2.4, we will patch all releases between 1.15 and 2.3 inclusive.
For more information
Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.
{ "affected": [ { "package": { "ecosystem": "PyPI", "name": "tensorflow" }, "ranges": [ { "events": [ { "introduced": "0" }, { "fixed": "1.15.5" } ], "type": "ECOSYSTEM" } ] }, { "package": { "ecosystem": "PyPI", "name": "tensorflow" }, "ranges": [ { "events": [ { "introduced": "2.0.0" }, { "fixed": "2.0.4" } ], "type": "ECOSYSTEM" } ] }, { "package": { "ecosystem": "PyPI", "name": "tensorflow" }, "ranges": [ { "events": [ { "introduced": "2.1.0" }, { "fixed": "2.1.3" } ], "type": "ECOSYSTEM" } ] }, { "package": { "ecosystem": "PyPI", "name": "tensorflow" }, "ranges": [ { "events": [ { "introduced": "2.2.0" }, { "fixed": "2.2.2" } ], "type": "ECOSYSTEM" } ] }, { "package": { "ecosystem": "PyPI", "name": "tensorflow" }, "ranges": [ { "events": [ { "introduced": "2.3.0" }, { "fixed": "2.3.2" } ], "type": "ECOSYSTEM" } ] }, { "package": { "ecosystem": "PyPI", "name": "tensorflow-cpu" }, "ranges": [ { "events": [ { "introduced": "0" }, { "fixed": "1.15.5" } ], "type": "ECOSYSTEM" } ] }, { "package": { "ecosystem": "PyPI", "name": "tensorflow-cpu" }, "ranges": [ { "events": [ { "introduced": "2.0.0" }, { "fixed": "2.0.4" } ], "type": "ECOSYSTEM" } ] }, { "package": { "ecosystem": "PyPI", "name": "tensorflow-cpu" }, "ranges": [ { "events": [ { "introduced": "2.1.0" }, { "fixed": "2.1.3" } ], "type": "ECOSYSTEM" } ] }, { "package": { "ecosystem": "PyPI", "name": "tensorflow-cpu" }, "ranges": [ { "events": [ { "introduced": "2.2.0" }, { "fixed": "2.2.2" } ], "type": "ECOSYSTEM" } ] }, { "package": { "ecosystem": "PyPI", "name": "tensorflow-cpu" }, "ranges": [ { "events": [ { "introduced": "2.3.0" }, { "fixed": "2.3.2" } ], "type": "ECOSYSTEM" } ] }, { "package": { "ecosystem": "PyPI", "name": "tensorflow-gpu" }, "ranges": [ { "events": [ { "introduced": "0" }, { "fixed": "1.15.5" } ], "type": "ECOSYSTEM" } ] }, { "package": { "ecosystem": "PyPI", "name": "tensorflow-gpu" }, "ranges": [ { "events": [ { "introduced": "2.0.0" }, { "fixed": "2.0.4" } ], "type": "ECOSYSTEM" } ] }, { "package": { "ecosystem": "PyPI", "name": "tensorflow-gpu" }, "ranges": [ { "events": [ { "introduced": "2.1.0" }, { "fixed": "2.1.3" } ], "type": "ECOSYSTEM" } ] }, { "package": { "ecosystem": "PyPI", "name": "tensorflow-gpu" }, "ranges": [ { "events": [ { "introduced": "2.2.0" }, { "fixed": "2.2.2" } ], "type": "ECOSYSTEM" } ] }, { "package": { "ecosystem": "PyPI", "name": "tensorflow-gpu" }, "ranges": [ { "events": [ { "introduced": "2.3.0" }, { "fixed": "2.3.2" } ], "type": "ECOSYSTEM" } ] } ], "aliases": [ "CVE-2020-26271" ], "database_specific": { "cwe_ids": [ "CWE-125", "CWE-908" ], "github_reviewed": true, "github_reviewed_at": "2020-12-10T19:06:50Z", "nvd_published_at": null, "severity": "MODERATE" }, "details": "### Impact\nUnder certain cases, loading a saved model can result in accessing uninitialized memory while building the computation graph. The [`MakeEdge` function](https://github.com/tensorflow/tensorflow/blob/3616708cb866365301d8e67b43b32b46d94b08a0/tensorflow/core/common_runtime/graph_constructor.cc#L1426-L1438) creates an edge between one output tensor of the `src` node (given by `output_index`) and the input slot of the `dst` node (given by `input_index`). This is only possible if the types of the tensors on both sides coincide, so the function begins by obtaining the corresponding `DataType` values and comparing these for equality:\n\n```cc\n DataType src_out = src-\u003eoutput_type(output_index);\n DataType dst_in = dst-\u003einput_type(input_index);\n //...\n```\n\nHowever, there is no check that the indices point to inside of the arrays they index into. Thus, this can result in accessing data out of bounds of the corresponding heap allocated arrays.\n\nIn most scenarios, this can manifest as unitialized data access, but if the index points far away from the boundaries of the arrays this can be used to leak addresses from the library.\n\n### Patches\nWe have patched the issue in GitHub commit [0cc38aaa4064fd9e79101994ce9872c6d91f816b](https://github.com/tensorflow/tensorflow/commit/0cc38aaa4064fd9e79101994ce9872c6d91f816b) and will release TensorFlow 2.4.0 containing the patch. TensorFlow nightly packages after this commit will also have the issue resolved.\n\nSince this issue also impacts TF versions before 2.4, we will patch all releases between 1.15 and 2.3 inclusive.\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.", "id": "GHSA-q263-fvxm-m5mw", "modified": "2024-10-30T21:25:05Z", "published": "2020-12-10T19:07:34Z", "references": [ { "type": "WEB", "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-q263-fvxm-m5mw" }, { "type": "ADVISORY", "url": "https://nvd.nist.gov/vuln/detail/CVE-2020-26271" }, { "type": "WEB", "url": "https://github.com/tensorflow/tensorflow/commit/0cc38aaa4064fd9e79101994ce9872c6d91f816b" }, { "type": "WEB", "url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2020-302.yaml" }, { "type": "WEB", "url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2020-337.yaml" }, { "type": "WEB", "url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2020-257.yaml" } ], "schema_version": "1.4.0", "severity": [ { "score": "CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:L/A:L", "type": "CVSS_V3" }, { "score": "CVSS:4.0/AV:L/AC:L/AT:N/PR:L/UI:N/VC:N/VI:L/VA:L/SC:N/SI:N/SA:N", "type": "CVSS_V4" } ], "summary": "Heap out of bounds access in MakeEdge in TensorFlow" }
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.