Pentest Checklist — agentic threat model
This agent acts as a static planning and methodology guide with low autonomy, presenting minimal direct operational risk unless integrated into an automated execution pipeline.
OWASP AIVSS score rationale
| Autonomy of Action | 0.10 | |
| Goal-Driven Planning | 0.40 | |
| Self-Modification | 0.00 | |
| Dynamic Tool Use | 0.00 | |
| Persistent Memory | 0.10 | |
| Contextual Awareness | 0.30 | |
| Dynamic Identity | 0.00 | |
| Multi-Agent Interactions | 0.00 | |
| Non-Determinism | 0.30 | |
| Opacity & Reflexivity | 0.20 |
Scored with the canonical OWASP AIVSS formula (AIVSS calculator reference); agentic risk factors estimated from the agent’s described capabilities.
MAESTRO 7-layer threat model
Per-layer threats for this agent. Layers tagged “not certain from listing” are general, caveated commentary where the public description didn’t pin that layer.
Not certain from the listing — relies on the host platform's underlying LLM, which is susceptible to prompt injection that could alter the generated pentest checklist or methodology.
Not certain from the listing — the agent likely uses static best-practice templates, but if it ingests scoping documents, there is a risk of sensitive data exposure or data poisoning via malicious input files.
The agent's core function is planning and scoping. It does not appear to execute tools directly, minimizing the risk of insecure tool integration or arbitrary code execution at this layer.
Not certain from the listing — deployment details are unspecified, but as a free/open-source skill, it relies entirely on the security and sandboxing of the runner environment.
Not certain from the listing — there are no mentioned guardrails or logging mechanisms to detect if the agent is being used to plan malicious attacks rather than authorized pentests.
Not certain from the listing — lacks explicit compliance controls, access policies, or authorization mechanisms to restrict scoping of unauthorized IP addresses.
As an 'Agent Skill', this is designed to be imported into other agents, creating a potential risk of cascading failures or malicious planning if integrated into a rogue multi-agent orchestrator.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).