The autonomous
red team for
AI systems.
Autonomous AI agents that continuously red-team your AI systems — finding the vulnerabilities that manual pentests and static scanners miss.
Built by former Google engineers — contributors to Garak & Promptfoo
AI created a massive new attack surface.
Traditional security can't keep up.
The Threat
Prompt Injection
OWASP #1Attackers manipulate LLM inputs to bypass instructions and exfiltrate data.
MCP / Tool Poisoning
Malicious tool responses hijack agent behavior and execute unintended actions.
AI Agent Exploits
Autonomous agents are tricked into running harmful code or leaking secrets.
The Solution
Autonomous AI Attack Agents
Deploy AI agents that think like attackers — no manual scripting required.
Continuous 24/7 Red-Teaming
Your AI changes daily. ProofLayer tests it around the clock.
Self-Evolving Attacks
Attack agents learn from each run and generate new exploit variants.
AI-Native Threat Coverage
Purpose-built for prompt injection, MCP exploits, RAG poisoning, and agent hijacking.
Autonomous Attack Loop
A continuous cycle that discovers, exploits, validates, and evolves — then loops 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 | Traditional Pentest | Acquired / Vendor-Locked | Static AI Scanners | |
|---|---|---|---|---|
| Finds vulnerabilities without manual scripting | ||||
| Catches new risks as your AI changes | ||||
| Adapts to your defenses automatically | ||||
| Covers AI-specific threats (injection, MCP, agents) | ||||
| Tests MCP servers and tool integrations | ||||
| Open-source, deploy anywhere | ||||
| Proves exploitability with real PoCs | ||||
| Deploys in under 30 seconds |
Frequently asked questions
What does ProofLayer do?+
Autonomous AI agents that continuously red-team your AI systems — LLMs, RAG pipelines, MCP servers, AI agents. Finds vulnerabilities like prompt injection, tool poisoning, and agent hijacking without manual scripting.
How is this different from a traditional pentest?+
Traditional pentests are manual, periodic, and not designed for AI-specific threats. ProofLayer runs autonomous attack agents 24/7 that evolve their techniques.
What AI systems does it cover?+
Any system using LLMs, RAG pipelines, MCP tools, or autonomous AI agents. Model-agnostic — works with OpenAI, Anthropic, Google, and open-source models.
Is this open source?+
The core scanner is MIT licensed with 1,700+ detection rules. Open-source ensures transparency and community-driven security coverage.
How long does deployment take?+
Under 30 seconds. One command, no configuration required.
Who built ProofLayer?+
Former Google engineers (Safe Browsing, Google Cloud) who contributed to Garak and Promptfoo — the frameworks that defined the AI security category.