OpenAgents — agentic threat model
OpenAgents is an open-source platform designed to run language agents with tool-use capabilities in the wild, presenting high risks of tool abuse, insecure code execution, and data exposure if hosted without robust sandboxing.
OWASP AIVSS score rationale
| Autonomy of Action | 0.60 | |
| Goal-Driven Planning | 0.70 | |
| Self-Modification | 0.20 | |
| Dynamic Tool Use | 0.80 | |
| Persistent Memory | 0.50 | |
| Contextual Awareness | 0.70 | |
| Dynamic Identity | 0.20 | |
| Multi-Agent Interactions | 0.40 | |
| 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.
As an open platform for language agents, it relies on various foundation models (LLMs) which are inherently vulnerable to prompt injection, adversarial reprogramming, and generating mis-aligned or toxic outputs.
Not certain from the listing — The data operations, vector stores, and RAG pipelines are not detailed in the brief description, leaving risks of data poisoning or exfiltration unconfirmed but highly plausible given its ChatGPT-like nature.
The platform orchestrates language agents, making it highly susceptible to framework-level vulnerabilities such as insecure tool integration, prompt injection bypassing agent logic, and malicious tool execution.
Not certain from the listing — The hosting, sandboxing, and secrets management infrastructure are not specified, raising concerns about potential host compromise or privilege escalation if agents execute arbitrary code.
Not certain from the listing — There is no mention of built-in guardrails, logging, or real-time monitoring to detect anomalous agent behavior or drift.
Not certain from the listing — No compliance certifications, identity management, or access control policies are detailed for this open-source platform.
Not certain from the listing — While designed as a platform for 'agents in the wild', the specific multi-agent interaction protocols, trust boundaries, and marketplace risks are not described.
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.