Modelscope-Agent — agentic threat model
Modelscope-Agent is a highly capable open-source framework featuring planning, memory, and powerful tools like a code interpreter and web browser. Its primary security risk stems from potential arbitrary code execution and data exfiltration if these tools are not strictly sandboxed and protected against prompt injection.
OWASP AIVSS score rationale
| Autonomy of Action | 0.70 | |
| Goal-Driven Planning | 0.80 | |
| Self-Modification | 0.30 | |
| Dynamic Tool Use | 0.80 | |
| Persistent Memory | 0.60 | |
| Contextual Awareness | 0.70 | |
| Dynamic Identity | 0.40 | |
| Multi-Agent Interactions | 0.50 | |
| Non-Determinism | 0.70 | |
| Opacity & Reflexivity | 0.60 |
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.
Modelscope-Agent supports multiple foundation models including Dashscope, Modelscope, and OpenAI. It is vulnerable to standard LLM threats such as prompt injection, adversarial manipulation, and system prompt leakage.
Not certain from the listing — while the framework supports memory and knowledge retrieval, details regarding vector database integrations, data poisoning protections, and embedding security are not specified.
The framework orchestrates planning, memory, and tool execution. The inclusion of a code interpreter and web browser introduces severe risks of tool misuse, insecure tool integration, and remote code execution if inputs are not sanitized.
Not certain from the listing — hosting, deployment environments, secrets management for API keys, and sandboxing mechanisms for the code interpreter are not detailed in the description.
Not certain from the listing — observability, execution logging, evaluation metrics, and runtime guardrails are not described in the provided text.
Not certain from the listing — access control policies, user authentication, and compliance frameworks (such as NIST or ISO) are not specified for this open-source framework.
Not certain from the listing — although customizable and scalable, the listing focuses on single-agent capabilities and does not detail multi-agent trust boundaries or ecosystem-level security controls.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).