MetaGPT — agentic threat model
MetaGPT presents a high agentic risk profile due to its multi-agent collaboration and automated code generation capabilities, which can be exploited to execute unauthorized code or inject supply-chain vulnerabilities if deployed without strict sandboxing.
OWASP AIVSS score rationale
| Autonomy of Action | 0.80 | |
| Goal-Driven Planning | 1.00 | |
| Self-Modification | 0.30 | |
| Dynamic Tool Use | 0.80 | |
| Persistent Memory | 0.60 | |
| Contextual Awareness | 0.80 | |
| Dynamic Identity | 0.50 | |
| Multi-Agent Interactions | 1.00 | |
| 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.
Not certain from the listing — relies on external foundation LLMs which are inherently susceptible to prompt injection, adversarial reprogramming, and generating insecure or malicious code snippets.
Not certain from the listing — handles sensitive software design documents, SOPs, and source code. Insecure data handling could lead to codebase exfiltration or poisoning of the context used by the agents.
MetaGPT's core framework orchestrates complex agent workflows. Vulnerabilities here include insecure tool execution (e.g., running generated code or compilers) and prompt injection that bypasses the defined SOP constraints.
Not certain from the listing — as an open-source framework, deployment is user-managed. Running MetaGPT without strict containerization or sandboxing poses severe risks of host compromise during automated code execution.
Not certain from the listing — does not specify built-in guardrails, real-time monitoring, or anomaly detection to identify when agents deviate from SOPs or generate malicious payloads.
Not certain from the listing — lacks native enterprise security controls, access policies, or compliance auditing, requiring organizations to wrap the framework in their own security boundaries.
Highly relevant. MetaGPT relies heavily on multi-agent interactions. A compromise in one agent (e.g., the Product Manager or Architect) can propagate trust and cause cascading failures or malicious code injection across the entire agent chain.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).