ATLAN TEAM
Why LLMs need their own testing approach
LLM-based systems have unique vulnerabilities that classic application testing does not capture. OWASP’s GenAI Top 10 highlights risks like prompt injection, insecure output handling, and excessive agency.
Key reasons LLM pentesting is essential
- Novel exploit paths: Prompt injection and model manipulation have no direct analogue in traditional apps.
- Sensitive outputs: Models can inadvertently leak confidential data without strict output validation.
- RAG pipeline exposure: Search indices and context prompts can be poisoned or tampered with.
- Governance and safety: Model parameters, system prompts, and classifiers need integrity checks.
Checklist for LLM pentesting
- Test known prompt injection techniques and safety filter bypasses.
- Simulate tool and API abuse in agent-style workflows.
- Attempt data exfiltration through crafted prompts and outputs.
- Review system prompts and RAG context for tampering.
- Stress test models for denial-of-service or poisoning scenarios.
LLM security testing turns AI features into first-class citizens within the security program, with controls that match their risk profile.
How to scope an LLM pentest
Effective LLM testing starts with architecture clarity: data sources, prompts, integrations, and model permissions. Scope should include RAG indices, tool APIs, and any automated workflows that the model can trigger. This is where most security gaps appear.
- Data flow review: Map every source of context and validate access boundaries.
- Prompt and policy testing: Evaluate whether system prompts and guardrails can be overridden.
- Tool misuse scenarios: Simulate unauthorized actions through agent workflows.
What leaders should expect in outputs
Beyond technical findings, strong LLM pentests deliver impact mapping and prioritized remediation. The output should answer: What data can be exposed? Which workflows can be abused? What controls materially reduce risk?
Explore LLM Penetration Testing and view our methodology here. If LLM features are embedded in a broader product, you can pair this with Web Application Testing to cover the full application surface.
Specialist testing turns AI risk into actionable engineering and governance decisions.