BabyAGI — agentic threat model
BabyAGI presents a high agentic risk due to its autonomous task-generation loop and self-building nature, which can easily escalate to arbitrary code execution or resource exhaustion if hijacked via prompt injection.
OWASP AIVSS score rationale
| Autonomy of Action | 0.90 | |
| Goal-Driven Planning | 0.90 | |
| Self-Modification | 0.80 | |
| Dynamic Tool Use | 0.60 | |
| Persistent Memory | 0.80 | |
| Contextual Awareness | 0.70 | |
| Dynamic Identity | 0.10 | |
| Multi-Agent Interactions | 0.20 | |
| Non-Determinism | 0.80 | |
| Opacity & Reflexivity | 0.70 |
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 powerful LLMs like GPT-4. It is highly vulnerable to prompt injection and adversarial inputs that can hijack the autonomous task creation and prioritization loop.
Uses vector database storage for efficient information retrieval. This introduces risks of data/knowledge-base poisoning and embedding inversion, potentially corrupting the agent's long-term context.
Employs an autonomous task creation, execution, and prioritization framework. Vulnerabilities include task-loop hijacking, memory poisoning, and insecure execution of self-generated subtasks.
Not certain from the listing — deployment details are not specified, but as an open-source framework, it typically runs in user-provisioned environments, risking host compromise if executed without strict container sandboxing.
Not certain from the listing — no built-in guardrails, evaluation, or logging mechanisms are described, creating significant blind spots during autonomous execution.
Not certain from the listing — lacks explicit identity, authorization, or compliance controls, relying entirely on the host environment's security posture.
Not certain from the listing — while designed as a single-agent framework, its self-building nature and potential to call external APIs could lead to cascading failures or unauthorized interactions in a broader ecosystem.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).