AI Security & Red Teaming
I test LLM & RAG systems for prompt injection, data leakage, and misuse risk.
Helping SaaS teams ship safer AI
Building OpsChecker
Most “AI vulnerabilities” aren’t model bugs.
They’re control failures.
If user input can override system intent, you don’t have an AI feature, you have an untrusted code path with a natural language interface.
A lot of people don’t realise how many “helpful” AI layers are quietly being added to everyday UI.
Search, summaries, recommendations are all new attack surfaces if they’re not constrained properly.
Curious what guardrails it had, if any, once you pushed it off the happy path.
Taking a break from my main project to have some fun,
@simonecanciello what do you think? Inspo taken from your scrolling fitness app.
Would you scroll this rather than Netflix endlessly?
Extra features you could add:
>TV Series
>I have watched button
>Watch later
>Watcher comments section.
✨ Spawnbeam Spotlight #2 ✨
When I think of an up-and-coming designer in the founder space, one name comes to mind.
Meet @uxmateja.
We hit him with some rapid-fire questions: 🧵
Back from Christmas and straight back into AI red teaming.
Reminder:
If your system prompt is your only control surface, you don’t have controls.
Prompt injection, instruction override, RAG leakage these show up fast when tested properly.
First night back after Christmas. Hope everyone had a good one!
Most AI issues I see aren’t model failures, they’re control failures.
Prompt injection, role confusion, silent data leakage.
Not bugs. Design gaps.
If you’re shipping AI features, reliability is security.