Orchestrate (Cursor plugin) — agentic threat model
Orchestrate presents a high-risk profile due to its multi-agent parallel execution model with read/write access to local codebases. The lack of explicit sandboxing or human-in-the-loop verification gates for generated code increases the potential for automated, cascading security compromises.
OWASP AIVSS score rationale
| Autonomy of Action | 0.80 | |
| Goal-Driven Planning | 0.90 | |
| Self-Modification | 0.40 | |
| Dynamic Tool Use | 0.80 | |
| Persistent Memory | 0.50 | |
| Contextual Awareness | 0.80 | |
| Dynamic Identity | 0.30 | |
| Multi-Agent Interactions | 1.00 | |
| 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 — likely relies on Cursor's underlying models (e.g., GPT-4, Claude). Threats include prompt injection hijacking the planner or verifier roles to generate malicious code.
Not certain from the listing — reads and writes the local codebase. Threats include codebase exfiltration, poisoning of context via malicious files in the repository, and lack of data lineage for generated artifacts.
The framework uses a custom planner/worker/verifier orchestration. Threats include insecure handoffs, state manipulation between sub-agents, and logic bypass where a worker bypasses the verifier to execute unauthorized commands.
Not certain from the listing — runs as a Cursor plugin spawning cloud agents. Threats include insecure communication channels between the local Cursor instance and cloud agents, and lack of sandboxing for executed code on the host machine.
Not certain from the listing — while the verifier role provides functional evaluation, security observability, audit logging of agent actions, and guardrails against malicious code generation are unspecified.
Not certain from the listing — no mention of compliance, authorization boundaries, or user-approval gates before writing to the codebase or executing commands.
Highly vulnerable to agent-to-agent trust abuse. A compromised worker agent could exploit weaknesses in the verifier agent's evaluation logic to inject backdoors into the codebase, leading to cascading multi-agent failures.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).