Cerberus

Product Introduction

1. Definition: Cerberus is an autonomous, AI-driven penetration testing (Pentesting) platform and offensive security agent. It is categorized as an "Auto PT" (Automated Penetration Testing) solution that utilizes a proprietary programming language, Cerberus Lang, built on formal verification and type theory to execute safe, multi-stage cyberattacks for vulnerability discovery.

2. Core Value Proposition: Cerberus exists to bridge the gap between infrequent, high-cost manual penetration tests and passive vulnerability scanners. Its primary value proposition is "Safety by Construction," providing a production-safe AI hacker that guarantees mathematical adherence to a defined scope. By replacing prompt-engineering-based guardrails with machine-checked proofs, Cerberus allows enterprises to conduct continuous, autonomous offensive security operations without the risk of system downtime or data destruction.

Main Features

1. Cerberus Lang & Proof-Carrying Execution: The core technical engine is Cerberus Lang, a programming language designed specifically for offensive security. Unlike traditional AI agents that rely on LLM (Large Language Model) output directly, Cerberus translates natural language objectives into this specialized code. Every action within Cerberus Lang must be accompanied by a mathematical proof that it resides within the user-defined scope. If the compiler cannot verify the safety proof (e.g., a command that might trigger a DDoS or move laterally into a restricted subnet), the execution is physically blocked at the compiler level.

2. Autonomous Attack Tree & Hypothesis Testing: Cerberus operates beyond simple scanning by generating live attack trees. It formulates hypotheses about an application's architecture and potential vulnerabilities, then writes and executes real exploit code to validate them. This process includes automated External Attack Surface Management (EASM), DAST (Dynamic Application Security Testing), and SAST (Static Application Security Testing) with the delivery of working Proof-of-Concepts (PoCs) rather than mere lists of potential issues.

3. Enterprise-Grade Collaborative Workspace: The platform features a collaborative environment where security teams can monitor the AI's logic in real-time. This includes an "attestable" execution trace, allowing auditors to see every command and why it was permitted. The system integrates into existing developer workflows via a VSCode extension for "write-time" vulnerability remediation and CI/CD integrations to ensure every deployment is pentested before reaching production.

Problems Solved

1. The "Snapshot" Security Problem: Traditional manual pentests cost between $80k and $200k, take weeks to complete, and only offer a snapshot of security at a specific point in time. Cerberus solves the "blind spot" between audits by providing continuous, 24/7 autonomous testing at 1% of the cost of human consultancies.

2. Target Audience:

   • CISOs and Security Directors: Seeking to reduce organizational risk and meet compliance requirements (SOC2, PCI-DSS) through continuous monitoring.
   • Penetration Testers and Red Teamers: Looking to automate the "grunt work" of asset discovery and CVE monitoring to focus on complex exploit chains.
   • DevSecOps Engineers: Requiring automated, production-safe offensive testing integrated into CI/CD pipelines.
   • Regulated Enterprises: Finance, healthcare, and government sectors that require on-premise, air-gapped security tools to protect sensitive data.

3. Use Cases:

   • Production Vulnerability Validation: Safely running exploits against live production environments to ensure defenses are active.
   • Continuous Asset Discovery: Automatically mapping the external attack surface as new cloud resources are deployed.
   • Vulnerability Remediation Verification: Using the AI agent to re-test fixed vulnerabilities to ensure patches are effective.

Unique Advantages

1. Differentiation from LLM-Based Pentesters: Most AI pentesting tools rely on "prompt engineering" to prevent the AI from causing damage (e.g., "Don't drop the database"). Cerberus eliminates the "hallucination" risk inherent in LLMs. By using formal methods and type theory, it moves safety from a "promise" to a mathematical "certainty." If an action is out of scope, the code literally cannot run.

2. Key Innovation: Attestable Offensive Security: Cerberus is the first tool to ship a machine-checked proof with every action. This makes the AI's behavior fully transparent and auditor-ready. Furthermore, its ability to run on-premise with local models (Zero Cloud Dependency) ensures that proprietary architecture data and secrets never leave the client's network, a critical requirement for high-security environments.

Frequently Asked Questions (FAQ)

1. Is it safe to run an AI hacker on a production database? Yes, Cerberus is specifically engineered for production safety. Unlike other tools that use "guardrail prompts" which can be bypassed by LLM hallucinations, Cerberus uses Cerberus Lang. This language requires every network call or execution effect to be validated against a formal specification at compile time. If the AI attempts a destructive action like a DROP TABLE or an unauthorized lateral move, the compiler rejects the action before a single packet is sent.

2. How does Cerberus compare to traditional DAST or vulnerability scanners? Traditional scanners (like Nessus or Burp Suite) identify potential vulnerabilities but often produce high false-positive rates and do not demonstrate exploitability. Cerberus is an autonomous agent that acts like a human hacker; it doesn't just flag a "maybe" vulnerability—it writes exploit code, attempts to safely verify the flaw, and provides a working Proof-of-Concept (PoC). It covers the entire lifecycle from discovery to exploitation.

3. Can Cerberus be deployed in air-gapped or high-privacy environments? Yes. Cerberus supports Enterprise-tier deployments that are fully on-premise and air-gapped. It supports local LLM infrastructure, meaning your application’s code, secrets, and vulnerability data are processed locally and never transmitted to the cloud. This provides a level of data sovereignty that typical SaaS-based AI security tools cannot match.