gptme — agentic threat model
gptme presents an extremely high-risk profile due to its unconstrained local execution capabilities, including arbitrary terminal commands and file modifications, combined with web browsing and a complete lack of default sandboxing.
OWASP AIVSS score rationale
| Autonomy of Action | 0.80 | |
| Goal-Driven Planning | 0.70 | |
| Self-Modification | 0.40 | |
| Dynamic Tool Use | 1.00 | |
| Persistent Memory | 0.30 | |
| Contextual Awareness | 0.80 | |
| Dynamic Identity | 0.20 | |
| Multi-Agent Interactions | 0.10 | |
| Non-Determinism | 0.70 | |
| 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.
Not certain from the listing — supports several LLM providers (both local and cloud), making its vulnerability to prompt injection, adversarial reprogramming, or model-specific exploits highly dependent on the user's chosen backend model.
Not certain from the listing — primarily operates on local files and web search rather than a dedicated vector database, but its file read/write capabilities expose local data to exfiltration if the agent is compromised.
High risk of tool misuse and insecure tool integration; the framework allows arbitrary terminal execution, file modification, and web browsing, which can be chained maliciously via prompt injection from untrusted web content or files.
Runs locally in the user's terminal without default sandboxing ('unconstrained local alternative'), meaning any compromised execution has direct access to the host system, local network, and user privileges.
Not certain from the listing — provides a CLI and optional Web UI for interaction, but lacks built-in security guardrails, automated policy enforcement, or anomaly detection to monitor malicious tool usage.
Lacks built-in authentication or authorization controls, relying entirely on the host operating system's user permissions, presenting significant compliance and access control risks if run in enterprise environments.
Not certain from the listing — designed as a standalone personal assistant rather than a multi-agent orchestrator, minimizing direct agent-to-agent ecosystem risks unless manually integrated into larger workflows.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).