GPT Computer Assistant(GCA) — agentic threat model
GPT Computer Assistant presents a high-risk profile due to its core capability of direct computer use and OS-level interaction. While Dockerization offers some containment, arbitrary tool execution via MCP poses significant host compromise risks if malicious prompts bypass LLM alignment.
OWASP AIVSS score rationale
| Autonomy of Action | 0.80 | |
| Goal-Driven Planning | 0.70 | |
| Self-Modification | 0.20 | |
| Dynamic Tool Use | 0.90 | |
| Persistent Memory | 0.40 | |
| Contextual Awareness | 0.70 | |
| Dynamic Identity | 0.30 | |
| Multi-Agent Interactions | 0.60 | |
| 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.
Not certain from the listing — GCA is a framework supporting various LLMs. Foundation model threats (adversarial prompt injection, model misalignment) depend heavily on the specific LLM integrated by the developer.
Not certain from the listing — GCA focuses on computer use and MCP, but specific RAG or vector store implementations for data operations are not detailed in the brief description.
GCA is an orchestration framework enabling computer use and MCP (Model Context Protocol). High risk of tool misuse, insecure tool integration, and prompt injection translating directly into OS-level commands.
Features 'Dockerized agents' and 'desktop app'. Dockerization provides some container-level sandboxing, but desktop execution risks host compromise, privilege escalation, and exposed local services if not properly isolated.
Not certain from the listing — The directory listing does not mention built-in evaluation, logging, or guardrail mechanisms to monitor agent actions on the host system.
Not certain from the listing — No explicit security, compliance, or access control policies are mentioned for managing permissions of the computer-use agents.
Supports 'Vertical AI agents' and 'MCP' (Model Context Protocol), indicating an ecosystem of interoperable tools and agents. Risks include cascading failures and trust abuse between connected MCP servers/agents.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).