GHSA-GGWR-4VR8-G7WV

Vulnerability from github – Published: 2023-07-12 12:31 – Updated: 2024-11-18 16:26
VLAI
Summary
Apache Airflow Path Traversal vulnerability
Details

Apache Airflow, versions before 2.6.3, is affected by a vulnerability that allows an attacker to perform unauthorized file access outside the intended directory structure by manipulating the run_id parameter. This vulnerability is considered low since it requires an authenticated user to exploit it. It is recommended to upgrade to a version that is not affected

Show details on source website

{
  "affected": [
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "apache-airflow"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "0"
            },
            {
              "fixed": "2.6.3"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    }
  ],
  "aliases": [
    "CVE-2023-22887"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-22"
    ],
    "github_reviewed": true,
    "github_reviewed_at": "2023-07-12T17:30:42Z",
    "nvd_published_at": "2023-07-12T10:15:09Z",
    "severity": "HIGH"
  },
  "details": "Apache Airflow, versions before 2.6.3, is affected by a vulnerability that allows an attacker to perform unauthorized file access outside the intended directory structure by manipulating the run_id parameter. This vulnerability is considered low since it requires an authenticated user to exploit it. It is recommended to upgrade to a version that is not affected",
  "id": "GHSA-ggwr-4vr8-g7wv",
  "modified": "2024-11-18T16:26:31Z",
  "published": "2023-07-12T12:31:36Z",
  "references": [
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2023-22887"
    },
    {
      "type": "WEB",
      "url": "https://github.com/apache/airflow/pull/32293"
    },
    {
      "type": "WEB",
      "url": "https://github.com/apache/airflow/commit/05bd90f563649f2e9c8f0c85cf5838315a665a02"
    },
    {
      "type": "WEB",
      "url": "https://github.com/apache/airflow/commit/8ff7dfbd9e76aa40b04adeb231df3820606f5ba3"
    },
    {
      "type": "PACKAGE",
      "url": "https://github.com/apache/airflow"
    },
    {
      "type": "WEB",
      "url": "https://github.com/pypa/advisory-database/tree/main/vulns/apache-airflow/PYSEC-2023-104.yaml"
    },
    {
      "type": "WEB",
      "url": "https://lists.apache.org/thread/rxddqs76r6rkxsg1n24d029zys67qwwo"
    }
  ],
  "schema_version": "1.4.0",
  "severity": [
    {
      "score": "CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:N/A:N",
      "type": "CVSS_V3"
    },
    {
      "score": "CVSS:4.0/AV:N/AC:L/AT:N/PR:L/UI:N/VC:H/VI:N/VA:N/SC:N/SI:N/SA:N",
      "type": "CVSS_V4"
    }
  ],
  "summary": "Apache Airflow Path Traversal vulnerability"
}



Log in or create an account to share your comment.




Tags
Taxonomy of the tags.


Loading…

Loading…

Loading…

Forecast uses a logistic model when the trend is rising, or an exponential decay model when the trend is falling. Fitted via linearized least squares.

Sightings

Author Source Type Date Other

Nomenclature

  • Seen: The vulnerability was mentioned, discussed, or observed by the user.
  • Confirmed: The vulnerability has been validated from an analyst's perspective.
  • Published Proof of Concept: A public proof of concept is available for this vulnerability.
  • Exploited: The vulnerability was observed as exploited by the user who reported the sighting.
  • Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
  • Not exploited: The vulnerability was not observed as exploited by the user who reported the sighting.
  • Not confirmed: The user expressed doubt about the validity of the vulnerability.
  • Not patched: The vulnerability was not observed as successfully patched by the user who reported the sighting.

Loading…

Loading…

Loading…

Related by attack behaviour

Vulnerabilities whose description is nearest to this one in the vector space of the CIRCL/vulnerability-attack-technique-biencoder model. This is a similarity search over the bi-encoder space (plain cosine), not a classification, and it has no measured accuracy.


Loading…