OWASP Generative AI Red Team Guide

Gen AI Security Testing Approach

Red Team Methodology

Simulate real-world attacks against generative AI systems to identify vulnerabilities. Our red team exercises test prompt injection resilience, output validation, access controls, and plugin security through adversarial scenarios mirroring attacker techniques.

Prompt Security Assessment

Evaluate system prompt design, input validation, and instruction hierarchy to prevent prompt injection. Test whether attackers can override system instructions, extract prompts, or manipulate LLM behavior through crafted inputs.

Output Validation Testing

Assess how applications handle LLM-generated content before using it in databases, APIs, or user interfaces. Test for code injection, XSS, SQL injection, and command execution through AI outputs.

Data Leakage Analysis

Probe models for memorized training data, PII disclosure, and sensitive information leakage. Test whether LLMs reveal confidential data, system details, or proprietary information through carefully crafted queries.

Plugin Security Review

Evaluate security of LLM extensions, tool integrations, and API connectors. Test authentication, authorization, input validation, and privilege boundaries of plugins that extend LLM capabilities.

Supply Chain Verification

Assess security of third-party models, APIs, datasets, and dependencies. Review provenance, security posture, and data handling practices of upstream AI services and components.

Why Gen AI Security Matters to Your Business

Rapid Adoption Risks

Organizations deploy LLMs faster than security controls mature, creating exposure to novel attack vectors

Brand Protection

Security failures in customer-facing AI systems cause reputation damage and erode trust in AI-powered services.

Competitive Advantage

Secure AI implementation differentiates offerings and wins enterprise customers conducting AI security assessments.

Intellectual Property

Protect proprietary models, training data, and AI innovations from theft through model extraction and data poisoning.

Why This Framework is Critical

ISO 42001 is the world's first international standard for AI management systems, providing comprehensive governance for responsible AI development and deployment. As AI regulations emerge globally, this certification demonstrates proactive compliance, reduces liability, and builds stakeholder trust in AI systems making consequential decisions.

Financial Services

Customer Service AI: Banking chatbots handling account inquiries must prevent prompt injection attacks extracting customer financial data or executing unauthorized transactions
Fraud Detection Manipulation: LLMs analyzing transaction patterns vulnerable to adversarial inputs causing false negatives that allow fraudulent transactions through detection systems
Compliance Violations: AI systems generating financial advice or credit decisions must prevent hallucinations and ensure regulatory compliance with SEC, FINRA requirements

Healthcare

Medical Information Accuracy: Healthcare LLMs providing patient information or clinical guidance must prevent hallucinations that could cause medical errors with patient safety consequences
PHI Protection: AI systems processing patient data must prevent sensitive information disclosure violating HIPAA through memorized training data or prompt injection attacks
Diagnostic AI Security: LLMs assisting diagnosis must prevent manipulation through adversarial inputs that could cause misdiagnosis affecting patient treatment and outcomes

Government

Citizen Service Security: Government chatbots must prevent prompt injection accessing restricted information, bypassing access controls, or providing incorrect guidance on regulations
Information Accuracy: AI systems providing regulatory guidance, legal information, or policy details must prevent hallucinations that could mislead citizens or cause compliance failures
Data Sovereignty: Government LLMs processing classified or sensitive information must ensure data doesn't leak through third-party API services or model memorization

Legal Services

Confidentiality Breaches: Legal AI tools processing confidential client information must prevent data leakage that violates attorney-client privilege and professional responsibility rules
Document Hallucination: LLMs generating legal documents, contracts, or research must prevent fabricated case citations, incorrect legal analysis causing malpractice liability
E-Discovery Security: AI systems processing privileged documents must implement strict access controls preventing unauthorized disclosure through prompt injection or excessive agency

Technology & SaaS

Code Generation Security: GitHub Copilot-style tools must prevent generation of vulnerable code, hardcoded credentials, or insecure patterns introduced into production systems
API Security: LLM-powered APIs require rate limiting, authentication, and input validation preventing abuse, denial of service, and unauthorized model access
Multi-Tenant Isolation: SaaS platforms using shared LLMs must prevent data leakage between customers, ensuring tenant isolation and preventing prompt injection accessing other users' data

E-Commerce

Product Recommendations: LLM-powered shopping assistants must prevent manipulation through prompt injection directing users to unauthorized products or affiliate links
Review Analysis: AI systems processing customer reviews must detect and prevent adversarial inputs attempting to manipulate sentiment analysis or product rankings
Price Manipulation: Dynamic pricing AI must prevent adversarial attacks manipulating algorithms to display incorrect prices causing revenue loss or regulatory scrutiny