AI security represents one of the most critical challenges as organizations race to deploy LLM-powered systems in production. This article introduces our open-source OWASP AI Security Labs repository, focusing on how to protect your systems against parameter pollution vulnerabilities that can lead to costly resource exhaustion and financial impact.
The Problem: Vulnerable AI Function Calls
Modern AI assistants connect to external tools and APIs, passing parameters extracted from natural language to function calls. Without proper validation, attackers can exploit these systems by requesting excessive resource allocation:
User: "Book 500 seats for the concert."
AI: "Booking 500 seats at $75 each. Total: $37,500."This represents an example of OWASP AI Top 10 vulnerability #2: Insecure Plugin Design. In production environments, this can lead to:
- Resource exhaustion
- Unauthorized financial commitments
- Denial of service for legitimate users
Our Solution: Interactive Security Labs
We've created the OWASP AI Security Labs repository to provide hands-on training for developers building AI systems. Each lab demonstrates a vulnerability from the OWASP AI Top 10, with working code showing both:
- The vulnerable implementation
- The secure, properly validated version
The Parameter Pollution lab presents a real-world scenario of an event booking system where an attacker can exploit the unvalidated num_seats parameter to book all available seats at once.
Implementation Details
The lab compares two implementations:
Vulnerable code:
def book_seats(num_seats: int = 1, customer_name: str = None, customer_email: str = None) -> str:
"""
🔥 VULNERABLE: No validation on num_seats parameter!
Attackers can book any number of seats, potentially all of them.
"""
# Basic input validation, but missing limits on num_seats!
if not customer_name or not customer_email:
return "Customer name and email are required."
# Check if enough seats are available, but no upper bound check
if EVENTS["E001"]["available_seats"] < num_seats:
return f"Not enough seats available. Only {EVENTS['E001']['available_seats']} seats left."Secure code:
def book_seats(num_seats: int = 1, customer_name: str = None, customer_email: str = None) -> str:
"""
✅ SECURE: Properly validated parameters with reasonable limits
"""
# Input validation with maximum limit on seats
if not customer_name or not customer_email:
return "Customer name and email are required."
# Validate reasonable booking limits
if num_seats > MAX_SEATS_PER_BOOKING:
return f"Cannot book more than {MAX_SEATS_PER_BOOKING} seats in a single transaction."The difference is straightforward but critical: the secure implementation enforces a reasonable upper bound on the number of seats that can be booked in a single transaction.
Why This Matters
As AI assistants gain the ability to execute actions on behalf of users, these systems handle increasingly sensitive operations including:
- Financial transactions
- Resource allocation
- Data access
- Account management
Without proper parameter validation, malicious actors can exploit these systems in ways that:
- Cause financial damage
- Exhaust service capacity
- Lead to account lockouts
- Compromise user trust
Building Secure AI Systems
Organizations building AI-powered applications need to:
- Validate all parameters passed from LLMs to functions
- Implement rate limiting and reasonable upper bounds
- Build monitoring systems that can detect unusual patterns
- Test AI systems against known attack patterns
Our lab provides a hands-on environment for developers to understand these vulnerabilities and implement effective mitigations. The demo script allows teams to experience both the attack and defense scenarios with running code.
Get Expert Help
Building production-grade AI systems requires deep expertise in both machine learning and security engineering. As organizations scale their AI capabilities, this intersection becomes increasingly critical.
Our team provides specialized consulting services to help you:
- Audit existing AI applications for security vulnerabilities
- Design secure architectures for new AI initiatives
- Train your engineering teams on AI security best practices
- Implement secure deployment pipelines for LLM-powered applications
We've helped organizations cut security vulnerabilities while accelerating their AI development velocity through secure-by-design patterns and tools.
Ready to build AI systems that ship, scale, and protect your users? Contact us to schedule a security assessment of your AI applications.