Review AI app security before users and data are exposed.
AI-generated code and LLM features should be treated as untrusted until reviewed. The risk is not only bad output. It is user data leakage, prompt injection, tool overreach, weak permissions, unbounded cost, and production behavior nobody is monitoring.
Expected outcomes
- An AI security risk map tied to the product's real workflows.
- Recommendations for prompt, model, tool, data, auth, and deployment controls.
- A review of generated code paths that touch users, payments, private data, or external systems.
- A prioritized hardening plan before launch or before deeper AI rollout.
Best fit
Founders launching AI features
Your app uses LLMs, agents, RAG, automations, uploads, private records, or third-party tools and you need to understand the risk before launch.
Teams using AI-generated code
You need code, auth, API, and deployment review before generated implementation details become customer exposure.
Product leaders adding agent workflows
The product now takes actions, reads business data, or talks to external systems, and permissions need to be designed rather than assumed.
AI security pitfalls
Prompt injection and indirect instruction attacks
User content, documents, websites, emails, or retrieved context can instruct the model to ignore the intended workflow.
Sensitive data disclosure
Logs, prompts, embeddings, tools, support transcripts, and model responses can expose information users never meant to share.
Excessive agency and tool access
Agents that can read, write, email, update records, or call APIs need scoped permissions, audit trails, and human checkpoints.
How LOJI helps
Review AI and app boundaries
We examine what the model can see, what it can do, what data flows through it, and where user trust depends on hidden assumptions.
Map practical threat scenarios
We focus on the attacks and failures most relevant to the product: injection, leakage, auth bypass, unsafe tools, cost spikes, and unreliable outputs.
Harden the launch path
LOJI can help implement safer permissions, output controls, tests, logging, deployment guardrails, and escalation paths.
Common questions before the first call.
Is this only for LLM products?
No. It is useful for any app that uses AI-generated code, LLM features, agents, RAG, automation, private data, or user-facing AI output.
Can LOJI test prompt injection risk?
Yes. LOJI can review prompt injection exposure as part of the broader app, data, tool, and permission model.
Should this happen before or after launch?
Before launch is better. If the app is already live, the review can prioritize the most exposed production paths first.
Keep moving through the AI launch system.
AI prototype to production
Turn a working AI-built prototype into a product that can handle real users.
Vibe-coded app cleanup
Clean up technical debt, brittle workflows, and rushed architecture before the rewrite starts.
MVP has users. Now what?
Move from feature shipping to product maturity, support, analytics, and repeatable adoption.
Treat AI launch risk as part of product delivery.
Bring the app, AI workflow, model/tool usage, data paths, and launch timeline. LOJI will help identify the security controls that matter first.