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Security Testing Guide for AI/LLM Applications

Comprehensive security testing methodologies, tools, and best practices to ensure your AI infrastructure is protected against emerging threats and vulnerabilities.

Security Testing Coverage Analysis
Current security testing coverage across different vulnerability categories
Penetration Testing Framework
Systematic approach to identifying and exploiting vulnerabilities in LLM applications

Testing Phases

Key Testing Areas

  • Prompt injection vulnerabilities
  • Model extraction attempts
  • API security and authentication
  • Data leakage prevention
  • Supply chain security
  • Access control mechanisms

Testing Tools

  • LLMFuzzer for prompt testing
  • Burp Suite for API testing
  • Custom exploitation scripts
  • OWASP ZAP for web security
  • PyRIT for red teaming
  • Metasploit modules
Quick Security Assessment
Run a basic security assessment on your LLM endpoint
Essential Security Testing Tools

LLMFuzzer

Open-source fuzzing tool specifically designed for LLM security testing

Fuzzing
Open Source

PyRIT

Microsoft's Python Risk Identification Tool for AI red teaming

Red Team
Microsoft

Garak

LLM vulnerability scanner with extensive test suites

Scanner
Automated

TextAttack

Framework for adversarial attacks on NLP models

Adversarial
Research

AI Safety Benchmark

Comprehensive benchmark suite for AI safety testing

Benchmark
Safety

MLSec Toolkit

Security testing toolkit for machine learning systems

ML Security
Toolkit
Security Testing Best Practices

Testing Strategy

  • Test early and often in the development lifecycle
  • Combine automated and manual testing approaches
  • Include AI-specific test cases in your suite
  • Regularly update test scenarios for new threats
  • Integrate security testing into CI/CD pipelines

Common Pitfalls

  • Testing only happy path scenarios
  • Ignoring supply chain vulnerabilities
  • Insufficient coverage of AI-specific threats
  • Not testing model behavior under stress
  • Skipping compliance verification
Compliance & Standards

OWASP LLM Top 10

Essential security risks for LLM applications

Industry Standard

NIST AI RMF

AI Risk Management Framework guidelines

Government

ISO/IEC 23053

Framework for AI system trustworthiness

International

Testing Requirements by Standard

GDPR Compliance
  • • Data minimization testing
  • • Right to erasure verification
  • • Consent mechanism testing
SOC2 Requirements
  • • Access control testing
  • • Audit trail verification
  • • Availability testing

Secure Your AI Applications with ParrotRouter

Enterprise-grade security testing and continuous monitoring for your LLM deployments

Automated testing • Expert penetration testing • Compliance verification

References
  1. [1] OWASP. "OWASP Top 10 for LLM Applications" (2024)
  2. [2] NIST. "AI Risk Management Framework" (2024)
  3. [3] Microsoft. "LLM Security Best Practices" (2024)