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Securing the AI-Powered Future

AUTOMATED SECURITY TESTING · SHIP WITH CONFIDENCE · FIND VULNS BEFORE ATTACKERS DO

THREAT LANDSCAPE

The Problem

Large language models are everywhere now — customer support chatbots, internal knowledge assistants, code generators, agents with tool access. But security testing hasn't kept up. Most teams have no idea whether their AI deployments are vulnerable to jailbreaks, prompt injection, data exfiltration, or tool abuse.

Manual red teaming is expensive, inconsistent, and can't keep pace with weekly model updates. Traditional application security tools weren't designed for the unique attack surfaces of LLMs — multi-turn conversation exploitation, system prompt extraction, multilingual evasion, and more.

The result? Companies ship AI products hoping they're safe. They're not. Our public leaderboard demonstrates that even the most advanced models from leading AI labs have meaningful security gaps when faced with systematic, automated adversarial testing.

WHAT IT DOES

What We Do

Comprehensive, automated LLM security testing

58,000+ Attack Techniques

From DAN jailbreaks to memory drift and plan injection, our engine tests every angle across 15 categories — including 6 agentic AI threat categories.

15 Security Categories

Jailbreaks, prompt injection, evasion, exfiltration, tool injection, safety testing, agentic threats, and more — mapped to OWASP, MITRE, and NIST.

4 Scan Modes

Test web UIs via browser automation, call API endpoints directly, red-team AI agents with tool access, or benchmark raw models against our full attack suite.

BY THE NUMBERS

By the Numbers

0+

Attack techniques in database

0

Attack categories

0

Compliance frameworks mapped

0

Scan modes (Browser/API/Agent/Model)

TRANSPARENCY

Open Research

We believe security improves with transparency. Our LLM Security Leaderboard is fully public — anyone can see how the top AI models perform against our attack suite. We publish our methodology, share research on our blog, and contribute to the broader AI safety community.

View the Leaderboard
TIMELINE

Our Journey

2024

Founded to make LLM security testable and measurable

2024

Built Browser scan mode — Playwright-based web UI red teaming

2025

Launched API + Agent scan modes; expanded to 58,000+ attack techniques

2025

Advanced attack engine with 8 exploitation strategies and ExploitDepth scoring

2026

Agentic threat testing — 6 categories, adaptive strategies, defense fingerprinting

2026

Public Model Security Leaderboard live at shieldpi.io/leaderboard

TECH STACK

Built With

Backend
Python / FastAPI
Frontend
Next.js / TypeScript
Data
PostgreSQL / Redis
Infrastructure
Docker / Celery
AI Engine
Anthropic Claude
LLM Judge
NVIDIA Qwen
Model Access
OpenRouter
Browser Automation
Playwright

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