AI & LLM Penetration Testing

Primary Focus

Security risks unique to AI systems, large language models, and AI-driven applications

Security of traditional web applications and backend systems

Scope

Prompt injection, model manipulation, data leakage, output abuse, AI integrations

Authentication flows, input validation, business logic, APIs, and server-side logic

Attack Surface

User prompts, training data exposure, embeddings, plugins, third-party model integrations

Web forms, session handling, APIs, client-side and server-side components

Common Vulnerabilities

Prompt injection, data exfiltration through model output, insecure model APIs, model jailbreaks

SQL injection, XSS, CSRF, broken access controls, logic flaws

Testing Approach

Simulates malicious prompt crafting, model manipulation, and abuse of AI outputs

Simulates real-world attackers exploiting application vulnerabilities

Data Exposure Risk

Sensitive training data leakage, unintended data disclosure via responses

Database exposure, session hijacking, unauthorized data access

Business Logic Abuse

Manipulating AI outputs to bypass controls or generate harmful results

Exploiting flawed workflows or authorization checks

Human-in-the-Loop Risk

Tests how users can socially engineer AI systems into unsafe responses

Typically does not evaluate AI behavioral manipulation

Impact if Compromised

Brand damage, misinformation, regulatory risk, data exposure, unsafe automated decisions

Data breach, account takeover, service disruption

Ideal For

Organizations deploying AI chatbots, copilots, AI-powered SaaS features, or LLM integrations

Organizations operating traditional web applications and SaaS platforms