I run adversarial tests on LLM systems to identify prompt injection, data leakage, and behavioural risks before they become real problems.
Teams are integrating AI into products and internal workflows, but very few test how these systems behave under adversarial input.
This creates real risks:
If it hasn't been tested, it's an assumption — not a control.
Untested AI systems are a liability. Adversarial testing turns assumptions into evidence.
I simulate real-world attacks against AI systems to identify how they can be manipulated, bypassed, or misused.
Prompt Injection Testing
Structured adversarial inputs designed to hijack model behaviour, bypass system prompts, and execute unintended instructions — testing both direct and indirect injection vectors.
Hallucination & Reliability Testing
Systematic testing of model output accuracy under edge-case and high-stakes inputs — identifying where your system produces confidently wrong or fabricated responses.
Misuse & Edge-Case Scenarios
Testing how your AI system responds to abuse patterns, policy bypass attempts, and adversarial inputs that fall outside normal usage — before real users find them.
Behavioural Analysis
Analysis of model behaviour under adversarial conditions — identifying inconsistencies, guardrail failures, and outputs that deviate from intended system design.
Structured testing of your LLM system with a written report covering every vulnerability discovered, how it was exploited, and what to do about it.
Fixed-Scope EngagementWhat's Included
Outcome
Evidence, Not Assumptions.
A clear picture of how your AI system behaves under attack — with specific vulnerabilities, exploitation evidence, and a concrete remediation plan.
Week 1
Scoping, access setup, and adversarial test execution
Week 2
Findings report, remediation roadmap, and live readout
Three steps. Fixed scope. No open-ended retainers.
Free AI Vulnerability Check
15 Min · No CommitmentA quick conversation to understand your AI use case and identify potential risk areas. I'll tell you upfront whether structured testing makes sense for your system — and what to focus on if it does.
AI Vulnerability Assessment
Week 1 · RemoteStructured adversarial testing of your LLM system against the OWASP LLM Top 10 — covering prompt injection, data leakage, misuse scenarios, and behavioural analysis under attack conditions.
Report & Recommendations
Week 2 · Written + LiveWritten findings report with severity ratings, exploitation evidence, and a prioritised remediation roadmap — plus a live readout session so your team can act on findings immediately.
This engagement is right for you if:
If you are deploying AI and haven't tested it under attack, you don't know what it will do when someone tries to break it.
AI Security Consultant with 12+ years in IT — including hands-on DevOps engineering and AWS cloud architecture experience, now applied to adversarial testing of AI systems. I bring a practical engineering background to vulnerability assessments: I understand how LLM systems are built, deployed, and where they break.
I work with engineering teams and product organisations to identify real vulnerabilities in production AI systems — not theoretical risks from a checklist.
View LinkedIn ProfileTechnical Focus Areas
Practical AI security analysis for CTOs, engineering managers, and technical leaders — covering adversarial risks, LLM vulnerabilities, and production deployment controls.
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A 15-minute conversation to understand your AI use case and identify where your system is most likely to be vulnerable. No commitment required — I'll tell you honestly whether adversarial testing makes sense for your situation.
Connect on LinkedInFixed-scope engagements. Written findings you can act on. No open-ended retainers or vague advisory reports.