Securing the AI-Powered Future
We build autonomous security testing tools so organizations can deploy AI systems with confidence. If your LLM has a vulnerability, ShieldPi finds it before an adversary does.
The Problem
Large language models are being deployed everywhere — customer support chatbots, internal knowledge assistants, code generators, autonomous agents with tool access. But the security testing hasn't kept up. Most organizations 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 We Do
Comprehensive, automated LLM security testing
230+ Attack Techniques
From DAN jailbreaks to multilingual evasion, our autonomous agents test every angle an adversary would try — and many they wouldn't think of.
15 Security Categories
Jailbreaks, prompt injection, evasion, exfiltration, tool injection, safety testing, attack chaining, 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
Attack techniques in database
Models tested publicly
Attack vectors per scan
Scan modes available
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.
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