📰 Originally published on Securityelites — AI Red Team Education — the canonical, fully-updated version of this article.
🤖 AI/LLM HACKING COURSE
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Part of the AI/LLM Hacking Course — 90 Days
AI Security Automation – Day 35 of 90 · 38.9% complete
⚠️ Authorised Targets Only: Automated security test suites send structured attack payloads to AI endpoints. Ensure test suites run only against authorised environments — dedicated test endpoints or staging deployments, never production systems. Coordinate with the development team before integrating automated security tests into a shared CI/CD pipeline.
I once assessed a company that had done almost everything right after our initial security engagement. They spent twelve weeks fixing every finding we had reported. The system prompt was rewritten, injection filters were tightened, agent permissions were hardened, and rate limits were finally enforced properly. On paper, the system looked significantly safer.
Six months later, I ran a quick spot check. Three of those twelve vulnerabilities had quietly come back.
One was particularly frustrating. A system prompt change made around week fourteen had accidentally reintroduced the extraction weakness we had fixed back in week two. Then, around week twenty, a framework upgrade changed the way the agent validated tool permissions and quietly reopened an LLM06 attack path. Nobody had caught either regression because nobody was checking. There was no automated security test running after each change to make sure the protections were still working.
That’s the problem AI security automation is designed to solve.
Traditional security testing is often treated as a point-in-time activity: test the system, fix the findings, write the report, and move on. But AI systems don’t stay still. Prompts change. Models are upgraded. Agent frameworks are updated. RAG pipelines receive new documents. Tools and permissions evolve. Even a small configuration change can alter the security behavior of the entire system.
Every one of those changes creates a new opportunity for an old vulnerability to return.
Without automated testing running continuously, those regressions can sit unnoticed for weeks or months until the next security assessment finds them. By then, the vulnerable configuration may already have been running in production for a long time.
That’s the gap we’ll close in Day 35. We’ll look at how to build an AI security test suite that runs automatically, how to test for prompt injection and other security regressions, how to connect those tests to a CI/CD pipeline, and how continuous monitoring can extend security checks into production.
The goal isn’t to test an AI system once and declare it secure. The goal is to make sure every change has to prove that it hasn’t made the system less secure.
🎯 What You’ll Master in Day 35
Design a comprehensive AI security test inventory covering all attack families
Build a modality-agnostic test harness that handles text, image, and document test cases
Write injection regression tests that block deployment on vulnerability re-emergence
Write safety boundary tests that verify consistent refusal and acceptance behaviour
Integrate the test suite into a CI/CD pipeline as a deployment gate
Add runtime monitoring that evaluates production traffic against security criteria
⏱️ Day 35 · 3 exercises · Kali Terminal + Kali Terminal + Think Like Hacker ### ✅ Prerequisites - Day 16 — Automated Prompt Injection Testing — the Day 16 scanner is the foundation for Day 35’s regression test layer; Day 35 wraps it in a test harness and CI/CD integration - Day 27 — AI Red Team Operations — the engagement findings from Day 27 become the regression test cases in Day 35; findings without regression tests will silently re-emerge - Python with pytest and GitHub Actions or equivalent CI/CD — Exercise 2 builds the full pipeline integration ### 📋 AI Security Automation — Day 35 Contents 1. Designing the Security Test Inventory 2. Building the Test Harness 3. Injection Regression Tests 4. Safety Boundary Tests 5. CI/CD Pipeline Integration 6. Production Runtime Monitoring In Day 34 you covered multimodal injection — image, PDF, and OCR-based attacks. Day 35 wraps the full Days 4–34 attack library into a continuously running test suite. Day 36 covers advanced agentic AI security — multi-agent architectures, autonomous agent assessment, and the emerging attack surfaces in AI orchestration systems that coordinate multiple specialised agents.
Designing the Security Test Inventory
The test inventory is the spec before the code. Writing tests without an inventory produces a suite that covers whatever the author thought of on the day they wrote it. Writing tests from an inventory produces a suite with deliberate coverage of every attack family. The inventory has three columns: test category, test type (regression or boundary), and pass criteria (what a passing test looks like — what the AI must NOT produce for an injection test, what it must produce for a boundary acceptance test).
Coverage gaps in the inventory become production blind spots. An inventory that covers prompt injection but not indirect injection misses the most common real-world attack vector. An inventory that covers text injection but not image injection misses the multimodal surface. Build the inventory from the attack families in this course — one category per day covered — and prioritise based on which attack families the specific deployment is most exposed to.
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