MiniAGI — agentic threat model
MiniAGI presents a moderate-to-high risk profile due to its autonomous task execution and tool-use capabilities running locally without built-in sandboxing. While its chain-of-thought visibility provides some observability, the lack of native security controls means users must carefully isolate its execution environment.
OWASP AIVSS score rationale
| Autonomy of Action | 0.70 | |
| Goal-Driven Planning | 0.80 | |
| Self-Modification | 0.40 | |
| Dynamic Tool Use | 0.60 | |
| Persistent Memory | 0.30 | |
| Contextual Awareness | 0.60 | |
| Dynamic Identity | 0.10 | |
| Multi-Agent Interactions | 0.10 | |
| Non-Determinism | 0.70 | |
| Opacity & Reflexivity | 0.50 |
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.
Utilizes GPT-3.5-Turbo and GPT-4 foundation models. Highly susceptible to prompt injection, adversarial manipulation, and jailbreaks that can bypass the agent's internal self-criticism prompts.
Not certain from the listing — likely relies on in-memory context windows and basic text summarization rather than a dedicated vector database, limiting the risk of persistent vector store poisoning but leaving short-term context vulnerable to injection.
Features an orchestration framework supporting inner monologue, self-criticism, and tool execution. Vulnerabilities include tool misuse and the potential for malicious inputs to hijack the planning and self-criticism loops.
Not certain from the listing — as an open-source tool run locally by developers, infrastructure security depends entirely on the user's local environment; there is no mention of built-in containerization or sandboxing for tool execution.
Provides good basic observability by showing its reasoning process (chain-of-thought) and inner monologue, though it lacks enterprise-grade guardrails, automated drift detection, or tamper-proof logging.
Not certain from the listing — being a lightweight open-source developer tool, it does not appear to include native compliance frameworks, role-based access control (RBAC), or formal security policies.
Not certain from the listing — appears to operate as a standalone single-agent system with no native multi-agent orchestration, marketplace integrations, or complex ecosystem trust boundaries described.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).
These scores are auto-generated from public information (the agent's own listing, docs, and repository) using the canonical OWASP AIVSS formula and the MAESTRO framework — an estimate for guidance, not a penetration test, audit, or certification. See the scoring methodology. Are you the vendor? Factual corrections are free.