Agentic Pre-Flight
Intercepts AI agent tool calls before execution. An IT Helpdesk AI Agent receives tickets; before executing actions (reset password, grant access, run script), every tool call passes through an LFM2-350M pre-flight validator that classifies it as allow, deny, or hold_for_approval in ~15ms.
The Problem
AI agents execute tool calls in production: resetting passwords, granting access. Keyword filters block everything or nothing. Cloud LLM validation adds 500ms+. Most agents execute unchecked.
How LFM Compares
Keyword filters over-block or under-block. Cloud validation adds 500ms+ per tool call. LFM validates every AI agent action at 15ms with semantic understanding of intent vs. risk.
What LFM Unlocks
Every tool call validated at 15ms, faster than the call itself. Semantic distinction: 'reset my password' (safe) vs 'reset admin password and email externally' (attack).
Agentic Pre-Flight
Your AI agent is helpful, fast, and completely unsupervised. That's terrifying. LFM adds a semantic safety layer at 15ms — faster than the tool call itself.
Select an attack scenario to begin
Agent activity will appear here
Why Rules-Based Systems Fail Here
Rules-based firewalls check keywords — they'd block reset_password everywhere or allow it everywhere. They can't distinguish “reset my own password” from “reset admin password and email credentials externally.”
- ❌ Blocks ALL password resets (false positive)
- ❌ Can't detect social engineering tone
- ❌ Misses embedded prompt injections
- ❌ No concept of “blast radius”
- ✅ Allows self-service resets, blocks exfiltration
- ✅ Detects urgency pressure & impersonation
- ✅ Catches embedded [SYSTEM OVERRIDE] injections
- ✅ Evaluates scope × duration × target risk
LFM adds semantic understanding at 15ms — the safety check completes faster than the tool call it's protecting. Rules can't match this: they're either too broad (everything blocked) or too narrow (threats slip through).
This demo is fine-tuned on sample data. Results improve with your data.