GHSA-GV66-V8C8-V69C

Vulnerability from github – Published: 2023-06-25 18:30 – Updated: 2023-06-25 18:30
VLAI
Details

The legacy email.utils.parseaddr function in Python through 3.11.4 allows attackers to trigger "RecursionError: maximum recursion depth exceeded while calling a Python object" via a crafted argument. This argument is plausibly an untrusted value from an application's input data that was supposed to contain a name and an e-mail address. NOTE: email.utils.parseaddr is categorized as a Legacy API in the documentation of the Python email package. Applications should instead use the email.parser.BytesParser or email.parser.Parser class.

Show details on source website

{
  "affected": [],
  "aliases": [
    "CVE-2023-36632"
  ],
  "database_specific": {
    "cwe_ids": [],
    "github_reviewed": false,
    "github_reviewed_at": null,
    "nvd_published_at": "2023-06-25T18:15:09Z",
    "severity": null
  },
  "details": "The legacy email.utils.parseaddr function in Python through 3.11.4 allows attackers to trigger \"RecursionError: maximum recursion depth exceeded while calling a Python object\" via a crafted argument. This argument is plausibly an untrusted value from an application\u0027s input data that was supposed to contain a name and an e-mail address. NOTE: email.utils.parseaddr is categorized as a Legacy API in the documentation of the Python email package. Applications should instead use the email.parser.BytesParser or email.parser.Parser class.",
  "id": "GHSA-gv66-v8c8-v69c",
  "modified": "2023-06-25T18:30:27Z",
  "published": "2023-06-25T18:30:27Z",
  "references": [
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2023-36632"
    },
    {
      "type": "WEB",
      "url": "https://docs.python.org/3/library/email.html"
    },
    {
      "type": "WEB",
      "url": "https://docs.python.org/3/library/email.utils.html"
    },
    {
      "type": "WEB",
      "url": "https://github.com/Daybreak2019/PoC_python3.9_Vul/blob/main/RecursionError-email.utils.parseaddr.py"
    }
  ],
  "schema_version": "1.4.0",
  "severity": []
}



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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.

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