The question we're answering
“How do we test prompts, data exposure, access paths and model integrations?” LLM integrations create attack surface traditional application security testing wasn't built for: prompt injection, unintended data retention, and integrations that quietly expand what a model can access.
Who this is for
Organizations integrating LLMs or generative AI into products or internal workflows, who need those integrations tested with LLM-specific methodology, not a generic web-app scan.
What this covers
This engagement tests the LLM integration specifically, not just the surrounding application:
- Prompt and data retention: what's logged, retained, and for how long
- Prompt injection and manipulation testing: can the model be steered off its intended behavior
- Data exposure: what training or context data a model can be induced to reveal
- Access paths: how the model connects to your data and systems, and what that connection permits
- Model and provider boundaries: what the underlying provider's own terms and security posture allow
Outcomes & deliverables
- Prioritized findings from LLM-specific testing, not a generic scan report
- Clear guidance on prompt/data retention policy
- Remediation for integration-level exposure, not just model-level issues
- A retest once fixes are in place
How we approach it
Document prompts, data paths, and integration points across the system.
Prompt injection, data-leakage and manipulation testing against real scenarios.
Guardrails, retention policy fixes, and boundary hardening.
Regulatory readiness
Governance work here feeds directly into regulatory evidence, not just internal policy.
Industries & use cases
Proof
50 active enterprise clients, 100+ SMB clients, and 1,000+ assessments delivered per year: this isn't our first engagement like yours.
Expert reviewer
