Cyber Agents — Autonomous AI-Powered Penetration Testing Platform
58 specialized agents. Intelligent Attack Tree with backtracking. Built for organizations where security data cannot leave the network.
The Problem
Traditional penetration testing can’t keep up with today’s threat landscape.
Manual pentests cost between $10,000 and $100,000 per engagement, take weeks to schedule and execute, and deliver a point-in-time snapshot that’s outdated before the report is finished. Automated vulnerability scanners find individual weaknesses but can’t chain them into real attack paths.
The Solution: Cyber Agents
CyberAgents is an autonomous penetration testing framework. It combines 58 specialized AI agents with an intelligent Attack Tree architecture that dynamically explores, evaluates, and chains attack paths across your infrastructure.
How It Works
CyberAgents operates through a structured 9-phase methodology that mirrors the approach of an expert red team:
Phase 1: Pre-Engagement & Scoping — Define targets, rules of engagement, and attack surface boundaries.
Phase 2: Passive Reconnaissance — Gather intelligence from publicly available sources without touching the target.
Phase 3: Active Reconnaissance — Probe the target to map live hosts, open ports, and exposed services.
Phase 4: Enumeration — Deep-dive into discovered services to extract versions, configurations, users, and shares.
Phase 5: Vulnerability Analysis — Correlate findings against known vulnerabilities, misconfigurations, and attack patterns.
Phase 6: Exploitation — Execute validated exploits to gain initial access, chaining vulnerabilities where necessary.
Phase 7: Post-Exploitation — Escalate privileges, extract credentials, pivot laterally, and establish persistence.
Phase 8: Lateral Movement — Traverse the network to reach high-value targets and demonstrate full compromise paths.
Phase 9: Reporting & Evidence — Generate comprehensive reports with full attack chains, evidence, and remediation guidance.
At every step, agents share findings through a shared Blackboard, coordinated by a Moderator Agent that allocates tasks, resolves conflicts, and ensures no avenue is left unexplored.
Intelligent Attack Tree with Dynamic Backtracking
Most automated security tools follow linear playbooks. When a step fails, they stop or skip ahead, missing critical attack paths that a human pentester would explore.
Cyber Agents takes a fundamentally different approach. It constructs an intelligent Attack Tree — a structured representation of all potential attack paths through your infrastructure. As agents execute and gather results, the tree expands dynamically, branching into new possibilities based on real findings.
When an attack path reaches a dead end, Cyber Agents doesn’t stop. It backtracks automatically to the last viable decision point and explores alternative branches. This process continues until all promising paths have been exhausted, ensuring the kind of thorough, creative exploration that distinguishes expert pentesters from automated scanners.
The result: CyberAgents discovers multi-step attack chains that linear tools miss, while automatically adapting its strategy based on what it finds in your environment.
58 Specialized Agents
CyberAgents doesn’t rely on a single monolithic AI model. Instead, it deploys 58 purpose-built AI agents, each with deep expertise in a specific security domain.
Reconnaissance agents map your attack surface using passive OSINT and active probing techniques. Enumeration agents specialize in specific protocols and services: SMB, LDAP, DNS, HTTP, databases, and more. Exploitation agents validate and execute exploits with precision, avoiding false positives. Post-exploitation agents focus on privilege escalation, credential harvesting, and persistence. Lateral movement agents traverse network segments to demonstrate full compromise paths. And a Moderator Agent orchestrates the entire operation, allocating tasks, managing priorities, and ensuring comprehensive coverage.
Agents communicate through a shared Blackboard architecture — a common knowledge space where findings, hypotheses, and intermediate results are posted and consumed by other agents in real time. This enables emergent collaboration: an enumeration agent’s discovery can immediately trigger a specialized exploitation agent, without manual intervention.