Voyager — agentic threat model
Voyager's reliance on dynamic code execution (GPT-4 generated code) combined with autonomous self-improvement and a persistent skill library presents a high risk of arbitrary code execution if the execution environment is not strictly sandboxed.
OWASP AIVSS score rationale
| Autonomy of Action | 1.00 | |
| Goal-Driven Planning | 0.90 | |
| Self-Modification | 0.80 | |
| Dynamic Tool Use | 0.80 | |
| 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.
Utilizes GPT-4 for in-context learning and code generation. Primary threats include prompt injection and model reprogramming, which could lead to the generation of malicious code designed to escape the application context.
Maintains an 'ever-growing skill library' to store complex behaviors. If an attacker can poison this library or inject malicious skills, the agent will persistently execute compromised behaviors across sessions.
Features an automatic curriculum and an iterative prompting mechanism that executes generated code based on environmental feedback. The framework's core mechanism of direct code execution represents a critical vulnerability if input validation or code sandboxing is absent.
Not certain from the listing — requires a runtime environment to execute GPT-4 generated code and interface with Minecraft. If this environment lacks strict containerization or sandboxing, code execution vulnerabilities could lead to host system compromise.
Not certain from the listing — mentions using execution errors and environmental feedback for self-improvement, but there is no indication of security-focused logging, anomaly detection, or guardrails to intercept malicious payloads.
Not certain from the listing — as an open-source gaming and research agent, it likely lacks enterprise-grade access controls, authentication mechanisms, or compliance alignments.
Not certain from the listing — primarily operates as a single-agent system within Minecraft, but could face multi-agent or player-to-agent threats if deployed on public multiplayer servers where external entities can influence its environment.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).
These scores are auto-generated from public information (the agent's own listing, docs, and repository) using the canonical OWASP AIVSS formula and the MAESTRO framework — an estimate for guidance, not a penetration test, audit, or certification. See the scoring methodology. Are you the vendor? Factual corrections are free.