PYSEC-2026-66

Vulnerability from pysec - Published: 2026-02-27 22:16 - Updated: 2026-05-20 09:19
VLAI
Details

Gradio is an open-source Python package designed for quick prototyping. Prior to version 6.6.0, a Server-Side Request Forgery (SSRF) vulnerability in Gradio allows an attacker to make arbitrary HTTP requests from a victim's server by hosting a malicious Gradio Space. When a victim application uses gr.load() to load an attacker-controlled Space, the malicious proxy_url from the config is trusted and added to the allowlist, enabling the attacker to access internal services, cloud metadata endpoints, and private networks through the victim's infrastructure. Version 6.6.0 fixes the issue.

Impacted products
Name purl
gradio pkg:pypi/gradio

{
  "affected": [
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "gradio",
        "purl": "pkg:pypi/gradio"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "0"
            },
            {
              "fixed": "6.6.0"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ],
      "versions": [
        "0.1.0",
        "0.1.1",
        "0.1.2",
        "0.1.3",
        "0.1.4",
        "0.1.5",
        "0.1.6",
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    }
  ],
  "aliases": [
    "CVE-2026-28416",
    "GHSA-jmh7-g254-2cq9"
  ],
  "details": "Gradio is an open-source Python package designed for quick prototyping. Prior to version 6.6.0, a Server-Side Request Forgery (SSRF) vulnerability in Gradio allows an attacker to make arbitrary HTTP requests from a victim\u0027s server by hosting a malicious Gradio Space. When a victim application uses `gr.load()` to load an attacker-controlled Space, the malicious `proxy_url` from the config is trusted and added to the allowlist, enabling the attacker to access internal services, cloud metadata endpoints, and private networks through the victim\u0027s infrastructure. Version 6.6.0 fixes the issue.",
  "id": "PYSEC-2026-66",
  "modified": "2026-05-20T09:19:02.232478Z",
  "published": "2026-02-27T22:16:24.667Z",
  "references": [
    {
      "type": "ADVISORY",
      "url": "https://github.com/gradio-app/gradio/security/advisories/GHSA-jmh7-g254-2cq9"
    }
  ],
  "severity": [
    {
      "score": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:C/C:H/I:N/A:N",
      "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.

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