owasp_analyzer.py

from flask import Blueprint, request, jsonify
import os # For accessing environment variables
import json # For parsing API responses

# Create a Blueprint for OWASP analysis related routes
owasp_bp = Blueprint('owasp', __name__)

# Placeholder for AI model interaction
# In a real application, this would be a more sophisticated AI pipeline.
async def analyze_with_ai(data_to_analyze, vulnerability_type, api_key):
    """
    Conceptual function to send data to an AI model for vulnerability analysis.
    `data_to_analyze` could be HTML, network traffic, user input, etc.
    `vulnerability_type` helps guide the AI's focus (e.g., 'XSS', 'SQLi', 'CSRF').
    """
    prompt = f"Analyze the following data for {vulnerability_type} vulnerabilities. Provide a severity, a brief explanation, and remediation suggestions. Data: \n\n{data_to_analyze}"

    chat_history = []
    chat_history.append({"role": "user", "parts": [{"text": prompt}]})
    payload = {
        "contents": chat_history,
        "generationConfig": {
            "responseMimeType": "application/json", # Request structured JSON response
            "responseSchema": {
                "type": "OBJECT",
                "properties": {
                    "vulnerability_type": {"type": "STRING"},
                    "severity": {"type": "STRING", "enum": ["Critical", "High", "Medium", "Low", "Informational"]},
                    "explanation": {"type": "STRING"},
                    "remediation_suggestions": {
                        "type": "ARRAY",
                        "items": {"type": "STRING"}
                    }
                },
                "required": ["vulnerability_type", "severity", "explanation", "remediation_suggestions"]
            }
        }
    }
    api_url = f"https://generativelanguage.googleapis.com/v1beta/models/gemini-2.0-flash:generateContent?key={api_key}"

    # In a real Flask app, you'd use the 'requests' library for this.
    # For this example, we'll return a mock structured response.
    # try:
    #     response = requests.post(api_url, headers={'Content-Type': 'application/json'}, json=payload)
    #     response.raise_for_status()
    #     result = response.json()
    #     if result.get('candidates') and result['candidates'][0].get('content') and \
    #        result['candidates'][0]['content'].get('parts'):
    #         # The API returns a string that needs to be parsed as JSON
    #         json_string = result['candidates'][0]['content']['parts'][0]['text']
    #         return json.loads(json_string)
    #     else:
    #         print("AI analysis response structure unexpected:", result)
    #         return {"error": "AI analysis failed: Unexpected response."}
    # except requests.exceptions.RequestException as e:
    #     print(f"Error calling AI model: {e}")
    #     return {"error": f"AI analysis failed: Could not connect to API: {e}"}
    # except json.JSONDecodeError as e:
    #     print(f"Error decoding AI response JSON: {e}")
    #     return {"error": f"AI analysis failed: Invalid JSON response: {e}"}

    # Mock structured response for demonstration
    mock_response = {
        "vulnerability_type": vulnerability_type,
        "severity": "Medium",
        "explanation": f"The provided data shows potential patterns indicative of {vulnerability_type}. Further manual verification is recommended.",
        "remediation_suggestions": [
            f"Implement strict input validation for all user-supplied data.",
            f"Ensure proper output encoding based on the context (e.g., HTML entity encoding for XSS).",
            f"Use security headers and frameworks that mitigate {vulnerability_type}."
        ]
    }
    return mock_response

@owasp_bp.route('/xss', methods=['POST'])
async def analyze_xss():
    """
    Analyzes provided web content or input for XSS vulnerabilities using AI.
    """
    content = request.json.get('content') # Could be a URL, HTML snippet, or user input example
    if not content:
        return jsonify({"error": "Content to analyze is required"}), 400

    api_key = os.getenv("GEMINI_API_KEY", "") # Get API key from environment

    print(f"Analyzing content for XSS with AI: {content[:100]}...") # Log for debugging
    analysis_result = await analyze_with_ai(content, "Cross-Site Scripting (XSS)", api_key)

    return jsonify(analysis_result)

@owasp_bp.route('/csrf', methods=['POST'])
async def analyze_csrf():
    """
    Analyzes provided form data or page structure for CSRF vulnerabilities using AI.
    """
    form_data = request.json.get('form_data') # Could be HTML form, or details about a request
    if not form_data:
        return jsonify({"error": "Form data to analyze is required"}), 400

    api_key = os.getenv("GEMINI_API_KEY", "")

    print(f"Analyzing form data for CSRF with AI: {form_data[:100]}...")
    analysis_result = await analyze_with_ai(form_data, "Cross-Site Request Forgery (CSRF)", api_key)

    return jsonify(analysis_result)

# You would add more routes here for other OWASP Top 10 vulnerabilities
# (e.g., broken access control, security misconfigurations, etc.)
# Each would call analyze_with_ai with relevant data and vulnerability type.