LLM Security Assessments
Security assessment of LLM applications: prompt injection, data leakage and model abuse.
What it is
LLM applications introduce new vulnerability classes: prompt injection, insecure output handling, training-data poisoning, excessive agency and sensitive-information disclosure — catalogued in the OWASP Top 10 for LLM Applications. We assess your LLM integrations, RAG pipelines and agents against these risks with adversarial testing.
Coverage
- Prompt injection and jailbreak testing
- Data leakage and memorisation checks
- Excessive agency and tool-permission review
- RAG pipeline security review
- Output handling and downstream impact
How we deliver
- Scope — models, integrations and data agreed.
- Map — architecture and trust boundaries.
- Test — adversarial assessment against LLM risks.
- Report — risk-rated findings with mitigations.
- Retest — verify guardrails and fixes.
Outcomes
- Known LLM risks measured in your app
- Guardrail and architecture recommendations
- Evidence for AI governance reviews
- Safer path to production