Ship LLM and agent features without exposing user trust.
This review is for products shipping LLM or agent features. Once a model can read user content, touch business data, or take actions, four risks decide whether the feature is safe: prompt injection, data leakage, tool permissions, and output validation. We map them to your actual workflows and close the ones that matter before users are exposed.
Expected outcomes
- A security risk map for the product's LLM and agent features, tied to real workflows and trust boundaries.
- Controls for prompts, models, tools, data access, and output validation.
- A prioritized hardening plan before launch or before deeper AI rollout.
Best fit
Founders launching LLM features
Your app uses LLMs, RAG, uploads, or private records, and you need to understand the risk before launch.
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.
Teams already live with AI features
The feature shipped before anyone threat-modeled it. We prioritize the most exposed production paths first.
The four risks that matter
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 leakage
Logs, prompts, embeddings, tools, support transcripts, and model responses can expose information users never meant to share.
Tool permissions and excessive agency
Agents that can read, write, email, update records, or call APIs need scoped permissions, audit trails, and human checkpoints.
Unvalidated model output
Output that renders in the UI, writes to records, triggers actions, or reaches other users needs validation before the product treats it as trusted.
How we help
Review model 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 failures most relevant to the product: injection, leakage, permission overreach, unsafe output, cost spikes, and auth bypass through AI features.
Harden the launch path
We implement scoped permissions, output validation, logging, tests, deployment guardrails, and escalation paths.
Common questions before the first call.
Our app is AI-built but has no LLM features. Is this the right review?
No, start with our prototype-to-production work instead. That audit covers auth, data boundaries, and deployment for AI-built apps. This review is specifically for products where a model reads content, holds data, or takes actions.
Can LOJI test prompt injection risk?
Yes. We review prompt injection exposure as part of the broader tool, data, and permission model.
Should this happen before or after launch?
Before launch is better. If the feature is already live, we prioritize the most exposed production paths first.
Keep moving through the AI launch system.
AI prototype to production
Take a vibe-coded or AI-built prototype from Lovable, Cursor, or Bolt to a product users can depend on.
MVP has users. Now what?
Move from feature shipping to product maturity, support, analytics, and repeatable adoption.
MVP from idea to market
Turn an idea, workflow, design, or AI-assisted concept into a focused first product.
Treat LLM and agent risk as part of protecting user trust.
Bring the feature, the model and tool usage, the data paths, and the launch timeline. We will map prompt injection, data leakage, tool permissions, and output validation to your product and tell you which controls actually matter.