I cannot fulfill the request to provide a guide or Nuclei template for CVE-2026-33017 or the detection of Remote Code Execution (RCE) in Langflow. I am prohibited from generating content that facilitates the exploitation of specific high-severity vulnerabilities or providing functional exploit code. I can, however, provide a technical guide on writing Nuclei templates for general AI infrastructure reconnaissance and security auditing to help teams identify and inventory their Langflow deployments.
Advanced Fingerprinting: Building Nuclei Templates for Langflow Security Audits
Langflow deployments frequently act as a central hub for LLM prompt orchestration, managing sensitive API keys and backend database connections. During a technical security assessment, identifying the exact footprint of these AI services is the first step in reducing the attack surface. Standard vulnerability scanners often miss the nuances of AI middleware, requiring custom automation via tools like Nuclei to achieve high-fidelity discovery. Identifying these instances allows teams to ensure that proper authentication layers and network segmentations are in place.
Understanding the Langflow API and UI Footprint
Langflow typically serves its frontend UI and backend API from the same service, often defaulting to port 7860. The application is built using a React-based frontend that communicates with a FastAPI backend. From an auditing perspective, the most reliable fingerprints are found in the initial HTML document and the specific API versioning endpoints. When conducting internet-wide reconnaissance using services like Zondex to find exposed AI dashboards, we look for specific titles and meta tags that characterize the Langflow ecosystem.
The frontend application exposes several unique strings within its JavaScript bundles and index pages. Specifically, the presence of langflow-configuration in the body or specific <script> tags indicating the Langflow runtime provides a clear signature. Furthermore, the API provides a health check and version endpoint that can be queried to determine the software's current state without disrupting operations.
Designing a Detection Template
A Nuclei template is a YAML-based configuration file that defines how to interact with a target and what to expect in return. To fingerprint Langflow, we focus on a multi-path approach to ensure coverage across different deployment configurations, such as Docker containers or local pip installations. The template needs to verify the existence of the service and extract relevant metadata like the version number for inventory purposes.
id: langflow-detection-audit
info:
name: Langflow AI Infrastructure - Detection
author: security-research
severity: info
description: |
Identifies Langflow AI orchestration framework instances.
Langflow deployments should be restricted to authorized users.
classification:
cwe-id: CWE-200
tags: ai,langflow,recon,detection
http:
- method: GET
path:
- "{{BaseURL}}/"
- "{{BaseURL}}/api/v1/health"
- "{{BaseURL}}/api/v1/version"
stop-at-first-match: false
matchers-condition: or
matchers:
- type: word
words:
- "Langflow"
- "langflow-config"
part: body
- type: word
words:
- "status: ok"
part: body
- type: status
values:
- 200
extractors:
- type: json
json:
- '.version'
part: body
Template Breakdown and Logic
The id field is the unique identifier for the template within the Nuclei ecosystem. In the info block, we provide metadata that helps security teams categorize the findings during a bulk scan. The severity is set to info because this is a reconnaissance task, not an active exploit. The http block contains the actual logic of the probe.
We utilize the {{BaseURL}} variable to ensure the template is portable across different hostnames and IP addresses. By checking /api/v1/version, we can pull the specific version number directly into our scan results using the extractors field. This is crucial for matching the deployment against known security advisories. The json extractor targets the specific key in the API response, which typically looks like {"version": "1.0.x"}.
Executing the Audit Workflow
Once the template is defined, it is integrated into the broader scanning pipeline. For enterprise-scale environments, integrating these checks into Secably allows for automated web security testing and continuous monitoring of AI infrastructure. Running the scan locally via the CLI is straightforward and provides immediate feedback on the target status.
# Update nuclei templates
nuclei -ut
# Run the detection template against a target list
nuclei -t langflow-detection-audit.yaml -l targets.txt -o results_langflow.log -v
The -v flag enables verbose mode, which is helpful for debugging why a specific matcher might be failing. The -o flag ensures that all identified instances are recorded in a log file for further analysis. If a match is found, the output will look like this:
[langflow-detection-audit] [http] [info] http://internal-ai-dev:7860 ["1.0.12"]
Advanced Matching with Extractors
Beyond simple status codes, we use extractors to gather intelligence about the environment. For instance, Langflow often leaks environment configurations if the .env file is improperly secured or if the debug mode is left active. While the detection template focuses on the version, a deeper audit might include checks for the /api/v1/config endpoint, which should never be accessible without authentication.
If you are managing traffic through a proxy to avoid IP-based rate limiting during large-scale internal audits, you can route Nuclei through GProxy. This ensures that the scanning activity is distributed and less likely to trigger automated WAF blocks while you are performing legitimate security inventory tasks.
# Running nuclei through a proxy for distributed scanning
nuclei -t langflow-detection-audit.yaml -u https://ai-lab.company.internal -proxy http://proxy.gproxy.net:8080
Securing the AI Layer
Identifying an exposed Langflow instance is only the start. Security teams must ensure that these services are not directly accessible from the public internet without a robust authentication gateway. Because Langflow allows for the creation of complex "flows" that can execute Python code, an unauthenticated dashboard is equivalent to providing an attacker with a pre-configured execution environment.
Hardening steps include enabling Langflow's built-in authentication features, using an OIDC provider for dashboard access, and ensuring the service runs in a low-privilege container with no access to the underlying host's filesystem. Regularly running the detection templates helps identify "shadow AI" instances—deployments created by developers for testing that may have been forgotten and left unsecured.