ConnectOnion — agentic threat model
ConnectOnion is a highly capable, open-source agent framework with powerful built-in tools (shell, browser, email) that present severe security risks if deployed without strict sandboxing and human-in-the-loop approvals.
OWASP AIVSS score rationale
| Autonomy of Action | 0.80 | |
| Goal-Driven Planning | 0.80 | |
| Self-Modification | 0.30 | |
| Dynamic Tool Use | 0.90 | |
| Persistent Memory | 0.70 | |
| Contextual Awareness | 0.70 | |
| Dynamic Identity | 0.50 | |
| Multi-Agent Interactions | 0.80 | |
| Non-Determinism | 0.70 | |
| Opacity & Reflexivity | 0.60 |
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.
Integrates with external foundation models (OpenAI, Anthropic, Gemini). It is susceptible to standard LLM risks such as prompt injection, which could be leveraged to abuse the framework's powerful tool-calling capabilities.
Not certain from the listing — details on vector databases or RAG pipelines are not explicitly detailed, though the framework supports 'memory' and 'file tools' which could be targets for data poisoning or exfiltration.
High risk at the framework layer due to built-in execution tools (shell, file tools, browser automation, Gmail, Outlook). Insecure tool integration or prompt injection could lead to arbitrary code execution or unauthorized API actions.
Not certain from the listing — as a Python library installed via pip, deployment and sandboxing are left entirely to the developer. Executing shell and browser tools without containerized sandboxing poses extreme host compromise risks.
Includes built-in logging, debugging, TUI components, and an 'Eval' plugin. These features aid in observability, but developers must actively configure them to detect anomalous agent behavior or drift.
Provides an 'approvals' plugin which can act as a critical human-in-the-loop gate for sensitive actions. However, standard enterprise compliance, identity management, and fine-grained authorization policies are not built-in.
Supports multi-agent workflows and subagents. This introduces risks of cascading failures, agent-to-agent trust abuse, and complex delegation paths where a compromised subagent could escalate privileges.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).