Service
AI & LLM Security Testing
Chatbots, copilots and automated AI agents introduce risks that a traditional penetration test doesn't cover. Tested the way an attacker would — structured, reproducible and based on the OWASP LLM Top 10.
Risks
The test scope
AI systems have their own attack profile. Four critical risk categories, fully covered.
Jailbreak & guardrail bypass
Attempts to bypass safety restrictions through creative prompt constructions — including techniques that push the model out of its alignment training.
Prompt injection & agent hijacking
Malicious instructions via user input, documents or tools that take over system behaviour or push an agent into unauthorised actions.
Data & memory leakage
The unintended disclosure of sensitive information from system prompts, training data or sessions belonging to other users.
Bias & harmful output
Structured testing for discriminatory or stereotyping model responses across gender, nationality, and ethnicity.
Methodology
How AI systems are tested
A combination of automated probing and manual validation gives the most reliable results.
Schedule an intakeIntake & objectives
Which AI system is under review, which risks are relevant for your context, and where are the acceptable behavioural boundaries?
System & model analysis
System behaviour, the system prompt (if accessible), the model used and the integration context are fully analysed — tools, memory, external data sources.
Automated probing
Using the internal red-team tool, hundreds of targeted prompts are sent from a curated library, distributed across all relevant attack categories.
Manual escalation & validation
Detected findings are manually validated and extended to establish actual impact — not every automated hit is a genuine vulnerability.
Reporting
Overview of vulnerabilities per OWASP LLM Top 10 category, with concrete prompt/response examples as evidence and mitigations per finding.
Deliverables
What do you receive?
A complete report aligned to the OWASP LLM Top 10 and immediately usable by your development or compliance team.
- Findings per OWASP LLM category — structured overview with severity and impact assessment
- Prompt/response evidence — reproducible examples of each validated finding
- Concrete mitigations — adjustments to system prompt, guardrails, filters and architecture
- Optional retest — verification after implementation of recommended measures
Get started
Want to know how robust your AI system is?
Request a free intake — you'll hear exactly what the risks are for your system.