GHSA-XCRH-WPHV-74Q4

Vulnerability from github – Published: 2026-08-13 12:31 – Updated: 2026-09-03 21:31
VLAI
Details

Flowise before 3.1.3 contains a code injection vulnerability in the CSV Agent node's customReadCSV parameter that allows authenticated attackers to execute arbitrary Python code. The validator uses a static regex blocklist that can be bypassed through obfuscation techniques, enabling attackers to execute code in the unsandboxed pyodide environment with full system access.

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{
  "affected": [],
  "aliases": [
    "CVE-2026-73486"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-94"
    ],
    "github_reviewed": false,
    "github_reviewed_at": null,
    "nvd_published_at": "2026-08-13T12:17:23Z",
    "severity": "CRITICAL"
  },
  "details": "Flowise before 3.1.3 contains a code injection vulnerability in the CSV Agent node\u0027s customReadCSV parameter that allows authenticated attackers to execute arbitrary Python code. The validator uses a static regex blocklist that can be bypassed through obfuscation techniques, enabling attackers to execute code in the unsandboxed pyodide environment with full system access.",
  "id": "GHSA-xcrh-wphv-74q4",
  "modified": "2026-09-03T21:31:14Z",
  "published": "2026-08-13T12:31:09Z",
  "references": [
    {
      "type": "WEB",
      "url": "https://github.com/FlowiseAI/Flowise/security/advisories/GHSA-4878-cqgq-j53v"
    },
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2026-73486"
    },
    {
      "type": "WEB",
      "url": "https://www.vulncheck.com/advisories/flowise-before-code-injection-via-csv-agent-customreadcsv"
    }
  ],
  "schema_version": "1.4.0",
  "severity": [
    {
      "score": "CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H",
      "type": "CVSS_V3"
    },
    {
      "score": "CVSS:4.0/AV:N/AC:H/AT:N/PR:L/UI:N/VC:H/VI:H/VA:H/SC:H/SI:H/SA:H/E:X/CR:X/IR:X/AR:X/MAV:X/MAC:X/MAT:X/MPR:X/MUI:X/MVC:X/MVI:X/MVA:X/MSC:X/MSI:X/MSA:X/S:X/AU:X/R:X/V:X/RE:X/U:X",
      "type": "CVSS_V4"
    }
  ]
}



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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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