QA Engineer Skills 2026QA-2026Security Testing for AI Applications

Security Testing for AI Applications

AI applications introduce an entirely new attack surface that traditional security testing does not address. Prompt injection, data leakage, retrieval poisoning, and jailbreaks are not covered by OWASP's classic Top 10 or by conventional SAST/DAST tools. A QA architect building AI-powered products must understand both the traditional web security landscape and the emerging AI-specific threats, and must be able to design testing strategies that cover both.


Chapter Contents

1. OWASP LLM Top 1001-owasp-llm-top-10/

2. AI-Specific Attacks02-ai-specific-attacks/

3. Traditional Security03-traditional-security/

4. Security Program04-security-program/


Why This Matters

AI security testing requires a dual focus. First, the classic web security fundamentals -- OWASP Top 10, SAST, DAST, SCA in CI -- because an AI app is still a web app. Second, the AI-specific attack surface: prompt injection, jailbreaks, data leakage, and RAG poisoning. New jailbreak techniques emerge weekly, so the test suite must evolve as fast as the attack surface.

Core principle: Security testing for AI is not a one-time activity. It is a continuous practice.