VacAIgent — agentic threat model
VacAIgent presents a low-to-moderate risk profile as a local travel planner, but its multi-agent architecture (CrewAI) introduces risks of agent-to-agent trust abuse and prompt injection, compounded by a lack of sandboxing in its Streamlit deployment.
OWASP AIVSS score rationale
| Autonomy of Action | 0.50 | |
| Goal-Driven Planning | 0.70 | |
| Self-Modification | 0.10 | |
| Dynamic Tool Use | 0.30 | |
| Persistent Memory | 0.30 | |
| Contextual Awareness | 0.50 | |
| Dynamic Identity | 0.10 | |
| Multi-Agent Interactions | 0.80 | |
| Non-Determinism | 0.60 | |
| 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.
Uses GPT-4, GPT-3.5, and local models via Ollama. Primary threats include prompt injection leading to hijacked itineraries, or model manipulation if using untrusted local Ollama models.
Not certain from the listing — the data operations layer is not detailed, but likely involves processing user-provided preferences and potentially caching destination data. Risks include injection of malicious payloads via user inputs.
Uses the CrewAI framework for orchestration. Threats include insecure tool execution, prompt injection bypassing agent boundaries, and cascading failures in agent planning.
Not certain from the listing — deployment is likely self-hosted via Streamlit (local or Streamlit Community Cloud). Risks include exposed Streamlit ports, lack of sandboxing for local execution, and insecure storage of OpenAI API keys.
Not certain from the listing — no built-in evaluation or observability framework is mentioned. Lack of monitoring could lead to undetected prompt injections or agent loop failures.
Not certain from the listing — as an open-source travel planning tool, it lacks formal identity, authorization, or compliance controls, relying entirely on the host environment's security.
Uses CrewAI multi-agent collaboration. Threats include agent-to-agent trust abuse, where a compromised 'destination researcher' agent feeds malicious data to the 'itinerary planner' agent, leading to downstream exploitation.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).