Idea
A guided reasoning platform for automated penetration testing that improves accuracy and efficiency for cybersecurity teams.
Research Paper
Core Innovation
This paper introduces a deterministic task tree based on the MITRE ATT&CK Matrix to guide LLM reasoning in penetration testing. This structured approach constrains the LLM to proven tactics and techniques, reducing hallucinations and cyclical errors common in self-guided methods. The result is a more accurate and efficient automated penetration testing process.
Market Size (TAM)
$2–10B TAM, $1–2B SAM; assumption: growing demand for automated cybersecurity tools and AI-driven penetration testing platforms.
Potential Customers & Pain Points
- Enterprise Cybersecurity Teams Needing Faster Vulnerability Assessments
- Security Automation Providers Seeking Reliable LLM Integration
- Penetration Testers Facing Inconsistent AI-Driven Tools
Business Model
Subscription-based SaaS platform offering API access and enterprise integrations for automated penetration testing workflows.
Competitive Landscape
- Cobalt
- Synack
- Pentera
Implementation Challenges
- Integration with diverse enterprise security environments
- Ensuring up-to-date attack tree mappings
- Managing LLM model costs and query efficiency
Validation Strategy
- Pilot deployments with cybersecurity teams on real-world penetration tests
- Benchmarking against existing LLM penetration testing tools
- Iterative refinement of task trees based on user feedback and attack evolution
Research Paper Overview
Guided Reasoning in LLM-Driven Penetration Testing Using Structured Attack Trees
Summary
This paper proposes a guided reasoning pipeline for LLM-based penetration testing agents that uses a deterministic task tree derived from the MITRE ATT&CK Matrix to constrain and improve the accuracy of attack procedures. By anchoring the LLM's reasoning in proven penetration testing methodologies, the approach reduces ineffective or hallucinated actions and enhances task completion rates. Evaluations on 10 HackTheBox exercises with three LLMs show significant improvements in subtask completion and query efficiency compared to self-guided reasoning methods.