How a 2-Hour AI Project Exposed Romania’s Public API Security Gaps (And How You Can Test Yours) + Video

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Introduction:

The rapid development of public-facing applications using AI tools and open APIs has introduced a new frontier in cybersecurity. A recent project, demoanaf.ro, aggregated sensitive Romanian fiscal data from official ANAF and BNR APIs into a modern, responsive interface in just two hours. While showcasing efficiency, this raises critical questions about API security, data scraping, and the risks of unauthenticated data aggregation.

Learning Objectives:

  • Understand how to perform reconnaissance on public APIs to identify exposed data and security misconfigurations.
  • Learn to audit and secure API endpoints against unauthorized scraping and mass data retrieval.
  • Implement defensive coding and infrastructure hardening techniques to protect public data portals.

You Should Know:

1. Reconnaissance and API Discovery: Identifying Public Endpoints

The first step in understanding the security posture of a public data portal is mapping its external attack surface. The demoanaf.ro project explicitly states it pulls data from official ANAF (Agenția Națională de Administrare Fiscală) and BNR (Banca Națională a României) APIs. This is a classic example of client-side data consumption, where the frontend acts as a proxy to multiple backend sources.
Step‑by‑step guide: To audit an application like this, you would begin by using browser developer tools (F12) to monitor network traffic. Look for XHR/Fetch requests that directly call the target APIs. In this case, you might find endpoints like `api.anaf.ro/…` or bnr.ro/.... Once identified, you can use `curl` to test these endpoints directly.
– Linux/macOS Command:

curl -X GET "https://api.anaf.ro/endpoint?param=value" -H "User-Agent: Mozilla/5.0" -v

– Windows PowerShell Equivalent:

Invoke-WebRequest -Uri "https://api.anaf.ro/endpoint?param=value" -Headers @{"User-Agent"="Mozilla/5.0"}

This helps you see if the API requires authentication, rate limiting, or any other form of protection. A lack of these controls suggests a high risk of data scraping and potential DDoS.

  1. Building a Secure Data Aggregator: The Python Backend
    To replicate or secure such a portal, a backend proxy is often used to hide the original API keys and implement security controls. A poorly configured backend could expose sensitive keys or become an open proxy for attackers. Below is an example of a minimal, secure proxy using Python Flask that fetches data from a third-party API without exposing credentials client-side.
    Step‑by‑step guide: This code sets up a proxy endpoint `/api/proxy` that accepts requests, adds a secure API key from environment variables, and forwards the request. It also includes basic error handling and rate limiting headers.

    import os
import requests
from flask import Flask, request, jsonify
from flask_limiter import Limiter
from flask_limiter.util import get_remote_address</li>
</ol>

app = Flask(<strong>name</strong>)
limiter = Limiter(get_remote_address, app=app, default_limits=["200 per day", "50 per hour"])

API_KEY = os.environ.get('API_KEY')
BASE_URL = "https://api.anaf.ro/v1"

@app.route('/api/proxy')
@limiter.limit("10 per minute")  Enforce rate limiting
def proxy():
endpoint = request.args.get('endpoint')
if not endpoint:
return jsonify({"error": "Missing endpoint"}), 400

headers = {'Authorization': f'Bearer {API_KEY}'}
try:
response = requests.get(f"{BASE_URL}/{endpoint}", headers=headers, params=request.args)
return jsonify(response.json()), response.status_code
except requests.exceptions.RequestException as e:
return jsonify({"error": str(e)}), 500

if <strong>name</strong> == '<strong>main</strong>':
app.run(host='0.0.0.0', port=5000, ssl_context='adhoc')  Force HTTPS

    This example demonstrates using environment variables for secrets, rate limiting, and HTTPS enforcement—key mitigations against the risks inherent in public data portals.

    1. Auditing API Endpoints for Mass Data Retrieval Vulnerabilities
      A primary concern with portals like demoanaf.ro is the potential for mass data retrieval (scraping) if the underlying official APIs lack proper authentication or rate limiting. An attacker could enumerate all 4,000,000+ firms mentioned in the post by iterating through CUI (Unique Identification Code) numbers.
      Step‑by‑step guide: To test for this vulnerability, you would attempt to perform automated, sequential queries against the API endpoint. Using a simple bash script or Python, you can check if the API returns data for any given ID without blocking or challenging the requester.

    – Linux/macOS Bash Script (Test for mass enumeration):

    for i in {1..1000}; do
curl -s "https://api.anaf.ro/company?cui=$i" | grep -o "company_name"
echo "Checked $i"
sleep 0.1  Minimal delay to test basic rate limiting
done

    If the API returns a response for every `cui` without any captcha, API key, or IP-based blocking, it is vulnerable to mass data extraction. Defenders must implement strict rate limiting, request validation (e.g., requiring a nonce), and possibly behavioral analytics to detect scraping patterns.

    1. Hardening the Client-Side with Content Security Policy (CSP)
      Modern web applications like demoanaf.ro rely heavily on client-side JavaScript to fetch and render data. Without a strict Content Security Policy, these applications are susceptible to XSS (Cross-Site Scripting) attacks, which could allow an attacker to steal data or hijack user sessions. A well-configured CSP restricts which sources scripts can load and prevents inline execution.
      Step‑by‑step guide: Deploy a strict CSP header in your web server configuration (e.g., Nginx or Apache) to lock down the client-side environment.

    – Nginx Configuration Example:

    add_header Content-Security-Policy "default-src 'self'; script-src 'self' https://trusted.cdn.com; style-src 'self' 'unsafe-inline'; img-src 'self' data:; connect-src 'self' https://api.anaf.ro; frame-ancestors 'none'; base-uri 'self'; form-action 'self';" always;

    This policy ensures that scripts can only be loaded from the same origin and a trusted CDN, while API calls (connect-src) are explicitly allowed only to the official ANAF domain. This prevents malicious scripts from injecting fake UI elements or exfiltrating data to attacker-controlled domains.

    1. Cloud Hardening: Securing the Infrastructure Behind the Portal
      Assuming demoanaf.ro is hosted on a cloud platform (like AWS, Azure, or a VPS), its infrastructure must be hardened. The post mentions “raspuns instant” and “sub 2 secunde,” which implies a performant setup that could be vulnerable to DDoS or resource exhaustion attacks if not properly configured.
      Step‑by‑step guide: Implement a Web Application Firewall (WAF) and configure network-level controls to filter malicious traffic. Below is an example of using iptables on Linux to rate-limit incoming connections to prevent simple DoS attacks.

    – Linux iptables Rules for Rate Limiting:

     Limit new connections to 10 per second per IP
sudo iptables -A INPUT -p tcp --dport 443 -m state --state NEW -m recent --set
sudo iptables -A INPUT -p tcp --dport 443 -m state --state NEW -m recent --update --seconds 60 --hitcount 20 -j DROP

    Additionally, cloud-specific services like AWS WAF or Azure Front Door should be used to create custom rules that block requests with unusual user-agents, high request rates, or requests that match patterns of known enumeration attempts.

    6. Applying AI-Assisted Development Securely

    The core narrative of the post is building the portal “cu ajutorul AI.” While AI accelerates development, it can also introduce insecure code if not guided properly. AI models may generate code with hardcoded secrets, outdated libraries, or insecure patterns if the prompts are not security-focused.
    Step‑by‑step guide: When using AI to generate code for APIs or portals, always include security requirements in the prompt. For example, you could prompt: “Write a Flask proxy for a REST API. Include environment variable handling for secrets, implement rate limiting with Flask-Limiter, and ensure all responses have security headers like CSP and X-Frame-Options.” After generation, manually audit the code for vulnerabilities using tools like `bandit` for Python or `npm audit` for Node.js.
    – Python Security Audit Command:

    bandit -r your_project_directory/ -f html -o security_report.html

    – Node.js Audit Command:

    npm audit

    This ensures that the speed of AI development does not come at the expense of security.

    What Undercode Say:

    • Public API Exposure is a Double-Edged Sword: While open data initiatives are beneficial, they must be secured against mass scraping and abuse. The demoanaf.ro project highlights how easily aggregated data can be repurposed, which should be a wake-up call for government and private entities to audit their public APIs.
    • Defense in Depth is Non-Negotiable: Relying solely on the obscurity of an API endpoint is insufficient. Effective security requires a combination of rate limiting, authentication, WAFs, CSP, and secure coding practices, as demonstrated in the step-by-step guides.
    • AI Accelerates Both Development and Risk: The use of AI to build applications in hours is a testament to its power, but it also underscores the need for security-aware development practices. Developers must treat AI as a co-pilot that requires human oversight, particularly in the areas of secret management, input validation, and output encoding.

    Prediction:

    The trend of AI-generated portals that aggregate public data will grow exponentially, putting pressure on governments and enterprises to implement API security as a core component of their digital strategy. We will likely see a rise in regulations mandating strict API security controls, as well as an increase in automated bug bounty programs focused specifically on data leakage through public endpoints. Organizations that fail to secure their APIs will face not only reputational damage but also regulatory penalties and the risk of their data being commoditized by third-party portals without consent.

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