prompt-optimizer — agentic threat model
The prompt-optimizer agent presents low inherent risk due to its narrow, instructional focus on text transformation using EARS syntax. Its primary threat vector is downstream impact if compromised specifications are executed blindly by other agents or developers.
OWASP AIVSS score rationale
| Autonomy of Action | 0.10 | |
| Goal-Driven Planning | 0.20 | |
| Self-Modification | 0.00 | |
| Dynamic Tool Use | 0.00 | |
| Persistent Memory | 0.10 | |
| Contextual Awareness | 0.20 | |
| Dynamic Identity | 0.00 | |
| Multi-Agent Interactions | 0.10 | |
| Non-Determinism | 0.40 | |
| 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 — likely relies on a standard foundation LLM to perform the EARS rewriting. Vulnerable to prompt injection that could bypass the EARS structuring or output malicious instructions.
Not certain from the listing — does not appear to use a vector database or persistent RAG, operating purely on user-provided text inputs. Main risk is data exposure if sensitive requirements are processed.
The agent is a single-purpose skill focused on text transformation (EARS syntax). It lacks complex planning, memory, or tool-calling frameworks, minimizing orchestration-level vulnerabilities.
Not certain from the listing — deployment details are unspecified. As an open-source skill, infrastructure security depends entirely on the host environment running the agent.
Not certain from the listing — no built-in guardrails or evaluation metrics are mentioned. Output quality and safety rely on the user's manual review of the generated EARS specifications.
Not certain from the listing — lacks explicit authentication, authorization, or compliance controls. Users must ensure compliance with data privacy policies when inputting proprietary requirements.
Designed as a modular 'Agent Skill' to be injected into other workflows. If integrated into a larger multi-agent system, a compromised optimizer could inject malicious specifications downstream.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).