The autonomous
red team for
AI systems.
AI agents that continuously attack your LLMs, RAG pipelines, MCP servers, and AI agents — finding what humans and scanners miss.
npm Downloads
Detection Rules
Skills Scanned
Attack Types
Built by former Google engineers — contributors to Garak & Promptfoo
Your AI systems have an attack surface
your security team has never seen.
Every LLM integration, every MCP server, every AI agent is a new attack surface that didn't exist 12 months ago. Traditional pentests happen quarterly. Attackers move in seconds.
Prompt Injection
Attackers manipulate LLM inputs to bypass instructions, exfiltrate data, and take control of your AI system's behavior.
MCP / Tool Poisoning
Malicious tool responses hijack agent behavior and execute unintended actions. Most MCP servers have zero protection.
AI Agent Exploits
Autonomous agents are tricked into running harmful code, leaking secrets, or pivoting into your enterprise network.
Autonomous Attack Loop
Deploy once. AI agents continuously discover, exploit, validate, and evolve — then loop back to find what changed.
Recon Agent
Maps your entire AI attack surface — models, MCP tools, RAG pipelines, agent chains, and data flows.
Attack Agent
Chains multi-step exploits across your AI stack. Prompt injection, tool poisoning, agent hijacking — all automated.
Exploit Validation
Proves exploitability with real proof-of-concept attacks. No false positives — every finding is verified.
Self-Evolve
Learns from each engagement. Mutates successful attacks, generates new variants, and adapts to your defenses.
Recon Agent
Maps your entire AI attack surface — models, MCP tools, RAG pipelines, agent chains, and data flows.
Attack Agent
Chains multi-step exploits across your AI stack. Prompt injection, tool poisoning, agent hijacking — all automated.
Exploit Validation
Proves exploitability with real proof-of-concept attacks. No false positives — every finding is verified.
Self-Evolve
Learns from each engagement. Mutates successful attacks, generates new variants, and adapts to your defenses.
Why security teams
choose ProofLayer.
| ProofLayer | Manual Pentests | Static AI Scanners | Legacy Automated Pentests | |
|---|---|---|---|---|
Testing Frequency How often vulnerabilities are discovered | 24/7 continuous | Quarterly | On each scan | Scheduled runs |
AI Threat Coverage Prompt injection, MCP poisoning, agent hijacking, RAG attacks | 25+ attack types | Depends on tester | Rule-based only | Not supported |
MCP Server Testing Security validation for Model Context Protocol integrations | Full coverage | Not supported | Partial | Not supported |
Attack Adaptation Ability to evolve attacks based on target defenses | Self-evolving AI | Human expertise | Static rules | Fixed playbooks |
Proof of Exploit Verified, reproducible attack chains — not just CVE lists | Full kill chain | Manual PoC | Risk scores only | Partial validation |
Time to First Finding How quickly actionable results are delivered | < 60 seconds | 2-4 weeks | Minutes | Hours |
Deployment How it integrates into your environment | npm install, MIT | SOW + scheduling | SaaS / API | Agent install |
Cost at Scale Economics of continuous security testing | Open core + platform | $20K-100K/engagement | $5K-15K/yr | $40K-200K/yr |
We ship. Here's what we
built in 60 days.
Our team contributed to the tools that defined AI security. Now we're making them autonomous.
agent-security-scanner-mcp
Open-source MCP security scanner. Detect prompt injection, tool poisoning, and agent exploits in any MCP server.
ClawHub Security Dashboard
MCP skill security intelligence platform. Real-time vulnerability scanning and threat grading for the MCP ecosystem.
We built the tools. Now we're building the autonomous system that replaces manual security workflows entirely.
Start red-teaming
your AI.
See what attackers see — before they do.