Play 30

AI Security Hardening

High✅ Ready

LLM defense — prompt injection, jailbreak detection, OWASP LLM Top 10.

A comprehensive security platform for production AI workloads. Multi-layer defense: prompt injection detection (pattern matching + classifier), jailbreak attempt blocking, content safety enforcement (Azure AI Content Safety), automated red teaming (generates attack prompts and tests defenses), and OWASP LLM Top 10 compliance scanning. Covers all 10 categories: prompt injection, insecure output handling, training data poisoning, model DoS, supply chain attacks, sensitive info disclosure, insecure plugin design, excessive agency, overreliance, and model theft.

Architecture Pattern

LLM security: multi-layer defense, red teaming, OWASP Top 10 compliance

Azure Services

Azure AI Content SafetyAzure OpenAI (gpt-4o)Container AppsKey VaultAzure Monitor

DevKit (.github Agentic OS)

agent.md — root orchestrator with builder→reviewer→tuner handoffs
3 agents — Security Builder (gpt-4o), Reviewer (gpt-4o-mini), Tuner (gpt-4o-mini)
3 skills — deploy (104 lines), evaluate (101 lines), tune (101 lines)
4 prompts — /deploy, /test, /review, /evaluate with agent routing
.vscode/mcp.json — FrootAI MCP with Content Safety + OpenAI inputs + envFile

TuneKit (AI Config)

config/openai.json — classifier model for injection detection
config/security.json — severity thresholds, blocklists, allow patterns
config/guardrails.json — content safety levels, red team scenarios
evaluation/eval.py — Injection detection >99%, False positive <5%

Tuning Parameters

Severity thresholds (0→4 scale)Custom blocklistsRed team scenario libraryContent safety tolerance levelsOWASP compliance rulesIncident response triggers

Estimated Cost

Dev/Test

$100–250/mo

Production

$1.5K–5K/mo

User Guide Open in VS Code View on GitHub Setup Guide Configurator Ask Agent FAI Back to FrootAI