PYSEC-2026-142

Vulnerability from pysec - Published: 2026-05-13 16:16 - Updated: 2026-05-20 09:19
VLAI?
Details

urllib3 is an HTTP client library for Python. From 2.6.0 to before 2.7.0, urllib3 could decompress the whole response instead of the requested portion (1) during the second HTTPResponse.read(amt=N) call when the response was decompressed using the official Brotli library or (2) when HTTPResponse.drain_conn() was called after the response had been read and decompressed partially (compression algorithm did not matter here). These issues could cause urllib3 to fully decode a small amount of highly compressed data in a single operation. This could result in excessive resource consumption (high CPU usage and massive memory allocation for the decompressed data) on the client side. This vulnerability is fixed in 2.7.0.

Impacted products
Name purl
urllib3 pkg:pypi/urllib3

{
  "affected": [
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "urllib3",
        "purl": "pkg:pypi/urllib3"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "2.6.0"
            },
            {
              "fixed": "2.7.0"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ],
      "versions": [
        "2.6.0",
        "2.6.1",
        "2.6.2",
        "2.6.3"
      ]
    }
  ],
  "aliases": [
    "CVE-2026-44432",
    "GHSA-mf9v-mfxr-j63j"
  ],
  "details": "urllib3 is an HTTP client library for Python. From 2.6.0 to before 2.7.0, urllib3 could decompress the whole response instead of the requested portion (1) during the second HTTPResponse.read(amt=N) call when the response was decompressed using the official Brotli library or (2) when HTTPResponse.drain_conn() was called after the response had been read and decompressed partially (compression algorithm did not matter here). These issues could cause urllib3 to fully decode a small amount of highly compressed data in a single operation. This could result in excessive resource consumption (high CPU usage and massive memory allocation for the decompressed data) on the client side. This vulnerability is fixed in 2.7.0.",
  "id": "PYSEC-2026-142",
  "modified": "2026-05-20T09:19:21.038869Z",
  "published": "2026-05-13T16:16:57.303Z",
  "references": [
    {
      "type": "ADVISORY",
      "url": "https://github.com/urllib3/urllib3/security/advisories/GHSA-mf9v-mfxr-j63j"
    }
  ],
  "severity": [
    {
      "score": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H",
      "type": "CVSS_V3"
    }
  ]
}


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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.

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.


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