when-stuck — agentic threat model
The 'when-stuck' agent is a low-risk cognitive meta-router designed to diagnose problem-solving impasses. Its primary security risks are prompt injection leading to routing manipulation and cascading failures within its skill ecosystem, rather than direct data exfiltration or system compromise.
OWASP AIVSS score rationale
| Autonomy of Action | 0.30 | |
| Goal-Driven Planning | 0.40 | |
| Self-Modification | 0.10 | |
| Dynamic Tool Use | 0.20 | |
| Persistent Memory | 0.10 | |
| Contextual Awareness | 0.40 | |
| Dynamic Identity | 0.00 | |
| Multi-Agent Interactions | 0.40 | |
| Non-Determinism | 0.50 | |
| Opacity & Reflexivity | 0.30 |
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 — likely relies on a general-purpose LLM for instruction-driven meta-routing, making it vulnerable to prompt injection that misdirects the diagnosis or causes routing loops.
Not certain from the listing — does not explicitly mention a vector database or RAG, but if it stores past impasses, it could be vulnerable to data poisoning.
The agent acts as a meta-router over a 'skill family'. Vulnerabilities include routing hijacking or infinite loops if an attacker manipulates the impasse description to trigger recursive routing.
Not certain from the listing — as an open-source community skill, deployment is likely local or self-hosted, meaning infrastructure security depends entirely on the user's environment.
Not certain from the listing — no built-in logging or evaluation guardrails are mentioned, which could lead to silent failures or unmonitored routing errors.
Not certain from the listing — lacks explicit authentication, authorization, or policy enforcement mechanisms, relying on the host framework for compliance.
Designed to route to other skills in the 'problem-solving skill family'. This creates a risk of cascading failures or trust abuse if a downstream skill is compromised or if the router is tricked into calling malicious skills.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).