Proof Layer offers a continuous red‑team platform that automatically attacks large language model integrations, autonomous agents, and model‑centered programming servers to uncover AI‑specific vulnerabilities such as prompt injection, tool poisoning, and agent chain exploits. Each finding is mapped in real time to the OWASP LLM Top 10 and MITRE ATLAS frameworks, giving security teams provable coverage metrics and actionable remediation guidance.
Funding
Funding not disclosed
Founders
Product
Problem
Integrating large language models, autonomous agents, and model‑centered programming (MCP) servers creates novel attack surfaces that traditional security scanners and penetration‑testing services do not cover. Existing tools lack awareness of prompt injection, tool poisoning, agent chain exploits, and memory/context poisoning, leaving organizations vulnerable to emerging AI‑specific threats.
Solution
Proof Layer provides a continuous red‑team platform that automatically probes LLM integrations, agents, and MCP servers for AI‑specific vulnerabilities. The system executes realistic attack campaigns that simulate prompt injection, tool poisoning, agent chaining, and memory poisoning, displaying the progression of each exploit in real time. Findings are automatically mapped to the OWASP LLM Top 10 and MITRE ATLAS frameworks, delivering provable coverage metrics rather than generic scan reports. Security teams can monitor live attack timelines, review detailed methodology breakdowns, and assess coverage across all relevant threat categories. The platform’s data‑driven approach enables organizations to prioritize remediation based on concrete exploit evidence and framework alignment.
Target Audience
Primary customers are security and DevSecOps teams at enterprises deploying AI‑enabled applications, as well as AI product developers and platform providers that need to validate the safety of their LLM integrations and autonomous agents.
Features
- Automated continuous red‑team campaigns that target LLMs, autonomous agents, and MCP servers
- Real‑time visualization of multi‑phase attack timelines (NEXUS) with per‑expert performance metrics
- Mapping of every detected finding to OWASP LLM Top 10 and MITRE ATLAS categories for standardized reporting
- Coverage gauges that quantify provable framework coverage across all attack families
- Detection of 13 known memory and context poisoning techniques that persist across sessions
- Identification of tool and agent chain exploits that can trigger harmful code execution or data exfiltration