ghsa-8cxv-76p7-jxwr
Vulnerability from github
Published
2022-02-10 00:32
Modified
2024-11-13 22:48
Severity ?
6.5 (Medium) - CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H
7.1 (High) - CVSS:4.0/AV:N/AC:L/AT:N/PR:L/UI:N/VC:N/VI:N/VA:H/SC:N/SI:N/SA:N
Summary
Null-dereference in Tensorflow
Details

Impact

The implementation of GetInitOp is vulnerable to a crash caused by dereferencing a null pointer:

cc const auto& init_op_sig_it = meta_graph_def.signature_def().find(kSavedModelInitOpSignatureKey); if (init_op_sig_it != sig_def_map.end()) { *init_op_name = init_op_sig_it->second.outputs() .find(kSavedModelInitOpSignatureKey) ->second.name(); return Status::OK(); }

Here, we have a nested map and we assume that if the first .find succeeds then so would be the search in the internal map. However, the maps are built based on the SavedModel protobuf format and a malicious user can alter that on disk before loading to cause the second .find to return nullptr.

Patches

We have patched the issue in GitHub commit 4f38b1ac8e42727e18a2f0bde06d3bee8e77b250.

The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, 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.

Show details on source website

To clipboard 

{
  "affected": [
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "0"
            },
            {
              "fixed": "2.5.3"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    },
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "2.6.0"
            },
            {
              "fixed": "2.6.3"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    },
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "2.7.0"
            },
            {
              "fixed": "2.7.1"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ],
      "versions": [
        "2.7.0"
      ]
    },
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow-cpu"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "0"
            },
            {
              "fixed": "2.5.3"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    },
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow-cpu"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "2.6.0"
            },
            {
              "fixed": "2.6.3"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    },
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow-cpu"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "2.7.0"
            },
            {
              "fixed": "2.7.1"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ],
      "versions": [
        "2.7.0"
      ]
    },
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow-gpu"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "0"
            },
            {
              "fixed": "2.5.3"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    },
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow-gpu"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "2.6.0"
            },
            {
              "fixed": "2.6.3"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    },
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow-gpu"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "2.7.0"
            },
            {
              "fixed": "2.7.1"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ],
      "versions": [
        "2.7.0"
      ]
    }
  ],
  "aliases": [
    "CVE-2022-23577"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-476"
    ],
    "github_reviewed": true,
    "github_reviewed_at": "2022-02-04T20:26:34Z",
    "nvd_published_at": "2022-02-04T23:15:00Z",
    "severity": "HIGH"
  },
  "details": "### Impact\nThe [implementation of `GetInitOp`](https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/cc/saved_model/loader_util.cc#L31-L61) is vulnerable to a crash caused by dereferencing a null pointer:\n\n```cc\nconst auto\u0026 init_op_sig_it =\n    meta_graph_def.signature_def().find(kSavedModelInitOpSignatureKey);\nif (init_op_sig_it != sig_def_map.end()) {\n  *init_op_name = init_op_sig_it-\u003esecond.outputs()\n                      .find(kSavedModelInitOpSignatureKey)\n                      -\u003esecond.name();\n  return Status::OK();\n}\n```\n\nHere, we have a nested map and we assume that if the first `.find` succeeds then so would be the search in the internal map. However, the maps are built based on the `SavedModel` protobuf format and a malicious user can alter that on disk before loading to cause the second `.find` to return `nullptr`.\n### Patches\nWe have patched the issue in GitHub commit [4f38b1ac8e42727e18a2f0bde06d3bee8e77b250](https://github.com/tensorflow/tensorflow/commit/4f38b1ac8e42727e18a2f0bde06d3bee8e77b250).\n\nThe fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, 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.",
  "id": "GHSA-8cxv-76p7-jxwr",
  "modified": "2024-11-13T22:48:57Z",
  "published": "2022-02-10T00:32:29Z",
  "references": [
    {
      "type": "WEB",
      "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-8cxv-76p7-jxwr"
    },
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2022-23577"
    },
    {
      "type": "WEB",
      "url": "https://github.com/tensorflow/tensorflow/commit/4f38b1ac8e42727e18a2f0bde06d3bee8e77b250"
    },
    {
      "type": "WEB",
      "url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2022-86.yaml"
    },
    {
      "type": "WEB",
      "url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2022-141.yaml"
    },
    {
      "type": "WEB",
      "url": "https://github.com/tensorflow/tensorflow"
    },
    {
      "type": "WEB",
      "url": "https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/cc/saved_model/loader_util.cc#L31-L61"
    }
  ],
  "schema_version": "1.4.0",
  "severity": [
    {
      "score": "CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H",
      "type": "CVSS_V3"
    },
    {
      "score": "CVSS:4.0/AV:N/AC:L/AT:N/PR:L/UI:N/VC:N/VI:N/VA:H/SC:N/SI:N/SA:N",
      "type": "CVSS_V4"
    }
  ],
  "summary": "Null-dereference in Tensorflow"
}


Log in or create an account to share your comment.




Tags
Taxonomy of the tags.




Sightings

Author Source Type Date

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.