Runtime Defensefor Autonomous AI Agents

In private beta

From Detection to Prevention

We're building a pluggable, production-grade security layer to ensure agents are safe, shifting the industry from detection-first to prevention-first guarantees. Our product combines self-evolving runtime guardrails, a cryptographic capability-and-provenance layer, and a risk engine that enforces least-privilege at call time.

How It Works

  • ▪Self-Evolving Guardrails: Guardrails evolve continuously through automated red-teaming and incorporation of the latest AI-security research
  • ▪Cryptographic Capability Layer: Issues attenuable, auditable tokens and cryptographic verification so every tool invocation and side-effect is verifiably authorized and traceable
  • ▪Risk Engine: Enforces least-privilege at call time with deterministic, auditable agent behavior

Key Benefits

  • ✓Minimal integration overhead
  • ✓High autonomy with verifiable authority
  • ✓No UX compromises
  • ✓Prevents privilege escalation
  • ✓Enables safe delegation between agents and subagents
  • ✓Integration with enterprise tools

The Problem We Solve

We've jailbroken every state-of-the-art LLM. We've poisoned agentic frameworks integrated with payroll, billing, and financial systems. We've demonstrated how a single prompt-injected PDF can hijack bank accounts.

AI agents are about to control payment rails, crypto transfers, and financial operations. Without runtime defense, we're building an open playground for attackers.

The Bounty Platform

As a complement to our core product, we're launching a Kaggle-style platform for AI-security bounties and competitive games. Researchers and practitioners hack realistic scenarios including prompt injections, jailbreaks, RAG attacks, and context escalation across difficulty levels.

Custom Challenges: Create custom challenge scenarios using privacy-preserving building blocks that mimic real systems without revealing proprietary logic.

Continuous Red-Teaming: This marketplace accelerates continuous red-teaming and builds a corpus of exploitable patterns.

Feedback Loop: Practical adversarial data feeds back into our guardrails and threat models, creating a virtuous cycle of security improvement.