AG2 — agentic threat model
AG2 is a powerful multi-agent orchestration framework whose primary security risks stem from automated agent-to-agent trust abuse, tool execution vulnerabilities, and the complexity of securing decentralized swarm interactions.
OWASP AIVSS score rationale
| Autonomy of Action | 0.70 | |
| Goal-Driven Planning | 0.80 | |
| Self-Modification | 0.30 | |
| Dynamic Tool Use | 0.80 | |
| Persistent Memory | 0.60 | |
| Contextual Awareness | 0.70 | |
| 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 models like GPT-4, making it susceptible to adversarial prompt injection, model misalignment, or API-level vulnerabilities depending on the chosen provider.
FalkorDB integration introduces risks of knowledge graph poisoning, unauthorized data exfiltration, or graph injection attacks if inputs to the database are not properly sanitized.
As an agent framework supporting tool integration and customizable agents, it is highly vulnerable to tool misuse, insecure tool execution, and framework-level orchestration vulnerabilities if agent-to-agent messages are manipulated.
Not certain from the listing — the deployment environment, sandboxing of tool execution, and secrets management are left to the developer implementing the AG2 framework.
Not certain from the listing — does not explicitly detail built-in evaluation, logging, or guardrail mechanisms, creating potential blind spots in agent execution monitoring.
Not certain from the listing — access control, authentication, and compliance frameworks are not detailed in the listing and must be implemented externally by the developer.
Specifically designed for automated agent-to-agent communication and swarm-based orchestration, making it highly susceptible to cascading failures, rogue agent behavior, and trust abuse within the multi-agent ecosystem.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).