Most organisations building AI products have a security program — but most security programs weren't built to handle AI systems. Web application pentesting, vulnerability management, and cloud security posture management are mature disciplines with established tooling and processes. AI security testing is not yet mature, the tooling is fragmented, and most security teams don't yet have the specialised skills it requires.
This guide is for security leaders and engineers who need to build an AI security testing capability — either internally or through a combination of internal processes and external expertise — and don't know where to start.
Step 1: Map Your AI Attack Surface
You can't test what you haven't identified. Start with a comprehensive inventory of every AI component in your product and infrastructure:
- LLM API integrations: Which foundation models are you calling? What data do you send in the prompt context? What actions do model responses trigger?
- System prompts: What instructions govern each AI feature? Where are they stored? How are they version-controlled?
- RAG systems: What knowledge bases does the model retrieve from? What is the access control model for retrieval? Who can add documents?
- Agent workflows: Which AI agents have tool access? What tools? What permissions does each tool grant? What decisions do agents make autonomously?
- Fine-tuned models: What data was used for fine-tuning? Is that data still accessible via extraction attacks? Where are model weights stored?
- AI features in non-AI products: Copilots, smart search, recommendation engines, classification features — these all have LLM attack surface even if "AI" isn't in the product name.
Document this in an AI asset register. Use the same format as your existing asset management — classification, owner, data sensitivity, exposure (internal/external) — with AI-specific fields added for model type, tool access, and retrieval scope.
Step 2: Build Your Threat Model
For each AI system in your inventory, identify the realistic threat scenarios:
Who are the threat actors?
- External users (authenticated or unauthenticated) interacting with customer-facing AI features.
- Internal users who can access AI admin functions, system prompts, or training data.
- Third parties who can write to data sources your AI retrieves from (indirect injection vector).
- Compromised upstream providers — model providers, embedding APIs, vector database vendors.
What are the high-value targets?
- Customer data accessible via RAG retrieval.
- System prompt intellectual property and business logic.
- Agent capabilities that could be redirected (email, file access, API calls).
- Model weights and training data.
- The model's trust relationship with users.
Map to OWASP LLM Top 10
For each system and each threat actor, identify which OWASP LLM Top 10 categories are in scope. This gives you a structured testing agenda and a compliance-friendly framework for reporting findings.
Step 3: Define Your Testing Cadence
Different AI components warrant different testing frequency:
| Trigger | Test Type | Scope |
|---|---|---|
| Before initial launch | Full AI security pentest | All in-scope AI systems |
| New agent with tool access | Agent security assessment | Agent + all tools + integration points |
| New RAG knowledge source added | Targeted RAG testing | Retrieval scope, access controls, injection vectors |
| System prompt changes | Internal checklist review | Modified prompts + related functionality |
| Foundation model upgrade | Regression testing | Known attack patterns + behavioural comparison |
| Annual | Full AI red team | All AI systems, novel attack research |
Step 4: Build Internal Capabilities
Tooling
The AI security testing toolchain is still evolving rapidly, but a functional starting set includes:
- Garak: Open-source LLM vulnerability scanner covering prompt injection, jailbreaks, data extraction, and more. Good for automated regression testing.
- Burp Suite: For testing the API layer of your AI application — authentication, IDOR, injection into API parameters that reach the model.
- PromptBench: Adversarial robustness evaluation framework for LLMs.
- Custom prompt libraries: Maintain a library of known-good and known-bad prompts specific to your system. Update it with every new finding.
Skills Development
AI security requires a combination of traditional application security skills and AI/ML literacy. Invest in training your security team on LLM fundamentals, prompt engineering, and AI-specific attack techniques. OWASP's LLM Top 10 project, Anthropic's responsible scaling policy documents, and published AI security research papers are good starting points.
Internal Red Team vs. External Assessment
Internal teams are best positioned for continuous, regression-focused testing against your specific system using your maintained prompt library. External AI security teams provide independence, novel attack research, and specialised expertise that's impractical to maintain internally. The most effective programs use both: internal continuous testing supplemented by periodic external assessments.
Step 5: Build Feedback Loops
An AI security testing program that doesn't feed back into development is a compliance exercise, not a security program. Build feedback loops at every level:
- Finding → remediation tracking: Every AI security finding should have an owner, a remediation timeline, and a verification test. Track this in the same system as your other security findings.
- Testing → threat model update: Every engagement should produce new attack patterns added to your internal test library and new threat scenarios added to your threat model.
- Incident → program improvement: Every AI security incident — jailbreak, prompt injection, data leakage — should trigger a program review. What did we miss? What test would have caught this? Add it to the test suite.
- Research → proactive testing: Monitor AI security research publications (arXiv, Black Hat, DEF CON AI Village proceedings). When a new attack technique is published against systems similar to yours, test for it within 30 days.
Starting Small: The 90-Day Plan
If you're starting from zero, here is a practical 90-day plan:
- Days 1-30: Complete AI asset inventory. Build threat model for your highest-risk AI system. Run OWASP LLM Top 10 checklist internally against your top-priority system.
- Days 31-60: Commission external AI security assessment on your highest-risk system. Implement tooling (Garak or equivalent) for automated regression testing. Draft AI security policy.
- Days 61-90: Remediate critical and high findings from external assessment. Implement continuous automated testing in CI/CD pipeline. Train security team on AI attack techniques. Define testing cadence for all AI systems in inventory.
By day 90, you will have a documented attack surface, a tested high-risk system, an operational automated testing baseline, and a cadence for ongoing security assessment. That is a foundation you can build on.
Key Takeaways
- This post covers practical, actionable guidance for security and engineering teams.
- All findings and techniques are mapped to recognised frameworks (OWASP, NIST, ISO).
- Contact Vynox Security to test your systems against the vulnerabilities described here.