Agentic pentesting: AI agents that prove exploitability
Agentic pentesting replaces scripted scanning with autonomous agents that plan, act, observe and re-plan. Prancer's SwarmHack™ combines a deterministic execution core with optional frontier-model exploration and grades every finding by the evidence it actually captured.
Deterministic core, optional model assistance
Customers can run without a model or enable approved frontier, private, open-weight or sovereign models for adaptive exploration. The model may expand what gets tried, but deterministic execution and captured target output decide what is published as proven.
What makes a pentest agentic?
A scanner executes a list; an agent pursues a goal. Each AI agent selects its own tooling, reads the target's real response and re-plans from what it learned, sharing state with the rest of the swarm so a credential found on a web host becomes an Active Directory pivot seconds later.
The agentic kill chain
- PortDiscovery — map reachable surface before any payload is planned.
- Recon — service, stack and identity fingerprinting into shared swarm state.
- Discovery — candidate attack paths ranked by reachability, not CVSS.
- Exploitation — safe, bounded exploitation capturing the target's own output.
- PostExploit — credential reuse, pivots and lateral movement chained into real paths.
- Collection — evidence graded, deduplicated and written into the report.
How agentic findings stay honest
Findings are graded Exploited (captured target output), Observed, AttackPathIdentified or Simulated. Only Exploited findings can carry Critical severity, and an Exploited label without a captured artifact is downgraded automatically.
Agentic pentesting FAQ
What is agentic pentesting? Penetration testing performed by autonomous agents that plan, act, observe and re-plan. Prancer combines deterministic execution with optional model-assisted exploration.
How is it different from AI penetration testing tools? Prancer does not treat generated suggestions as findings. Specialist agents execute candidate paths, and only captured target evidence passes the reporting gate.
Can it replace human pentesters? No — it absorbs continuous, repeatable validation so humans focus on business-logic abuse and adversary emulation.
Go deeper on agentic pentesting
- SwarmHack architecture documentation — how the swarm, planner and intelligence bus fit together.
- Agent capability reference — every registered plugin grouped by attack surface.
- Authorization envelope and safety gates — signed scope, kill switch, default-deny classes.
- Tutorial: run your first SwarmHack scan — authorization to graded evidence, step by step.
- Tutorial: AD attacks and internal pentesting — where agents pivot inside the domain.
- Why hybrid pentesting needs an evidence gate — deterministic execution and optional model exploration working together.
- Inside the 200-IP AWS lab — 18 surfaces, 45 findings, 19 Exploited against ground truth.
Where agentic pentesting runs
- Active Directory and Windows identity — 21+ capabilities from Kerberoasting to Diamond tickets.
- Cloud, containers and CI/CD — IMDSv2, IAM pivots, Docker escape, kubelet, Jenkins.
- SSE and ZTNA validation — 23 capabilities proving whether the edge really blocks.
- API pentesting — REST and GraphQL abuse chained into real data exposure.
- RoboSec and IoT — MQTT, ROS2/DDS, firmware and radio attack surface.
- Unified white-box and black-box testing — code and IaC correlated with runtime proof.
- The SwarmHack engine — how the swarm coordinates 85 plugins in one run.