Large language model security testing
Q9 Continuous tests your LLM the way an attacker would: prompt injection, jailbreak attempts, system prompt extraction, hallucination triggers, unsafe model behaviour and output manipulation. You get prioritised, human-reviewed findings with clear remediation steps, not a list of theoretical risks.
01What it does
What it does
Large language model security testing is adversarial testing of an LLM deployment: the model as your application uses it, its system prompt and guardrails, and the interfaces, data and integrations around it.
Q9 Continuous runs AI-driven tests, tailored to how your deployment actually works, and every finding is reviewed by a person before it reaches you. Coverage is confirmed during scoping.
02Risk register
Risks we evaluate
What Q9 Continuous tests for within your agreed scope. Coverage is confirmed during scoping; not every item applies to every deployment.
- 01.01Prompt injectionDirect and indirect attempts to override your instructions, including through documents, web pages or other content the model reads.
- 01.02Jailbreak attemptsTechniques that push the model past the behaviour and safety boundaries you intended.
- 01.03System prompt extractionWhether confidential instructions, configuration or business logic can be drawn out of the model.
- 01.04Sensitive data leakageExposure of personal, customer or internal data the model can reach.
- 01.05Hallucination triggersInputs that reliably provoke confident but false output where your users depend on accuracy.
- 01.06Output manipulationOutput shaped to mislead users or to carry unsafe content into downstream systems.
- 01.07Model inversionAttempts to infer private or training-derived information from the model’s responses.
- 01.08OWASP LLM categoriesTesting mapped to the OWASP Top 10 for LLM Applications categories that apply to your deployment.
03Who needs it
Who needs it
- Teams launching or operating chatbots, assistants, copilots or other LLM-powered features.
- Startups whose investors, clients or regulators are starting to ask hard questions about AI security.
- Organisations in regulated environments that need evidence of how their AI behaves under attack.
04Approach
How Q9 Continuous tests it
Every test follows the same path, agreed in writing before any testing starts.
Discovery
We map your AI architecture, understand your threat model, and define what’s in scope based on your risk appetite and compliance obligations.
Agreed scope & written authorisation
Scope, environments, accounts and testing windows are agreed in writing. Nothing is tested without written authorisation from the people responsible for the system.
Testing
Q9 Continuous runs AI-driven tests against the authorised system, tailored to your deployment.
Human-reviewed findings
Every finding is reviewed by a person before it reaches you, with severity, context and stated limitations.
Reporting & remediation
Severity ratings, reproduction steps and remediation guidance your engineering team can act on, plus an executive summary for board-level reporting.
Re-testing where agreed
Where agreed, fixes are re-tested within the authorised scope through Continuous AI Pen Testing.
Testing runs on Q9 Continuous: fixes and significant changes can be re-tested within the authorised scope, and Q9 Driftmark (early access) can show when a checked system changes. Automated checks complement, and do not replace, independent penetration testing.
05Deliverables
What you receive
- A detailed findings report with severity ratings and reproduction steps
- Prioritised remediation guidance your engineering team can act on
- An executive summary for board-level reporting
- Findings mapped to the OWASP Top 10 for LLM Applications
- A clear statement of what was in scope, and of any testing limitations
Each test is a point-in-time check of an agreed scope. It is not a certification and does not guarantee that every risk has been found.
What We Test
Other things we test
- 02
Retrieval-augmented generation (RAG)
Retrieval pipelines, vector stores and knowledge-base isolation.
- 03
Agentic AI and tool execution
Autonomous agents, the tools they call and the permissions they hold.
- 04
APIs and application endpoints
The APIs and model endpoints that connect AI to your business.
- 05
AI application security controls
Model access, data flows, trust boundaries, secrets and monitoring.
- 06
AI usage discovery
Finding AI systems and integrations so they can be brought into testing scope.