Browser Use — agentic threat model
Browser Use presents a high-risk profile due to its capability to automate browser interactions directly, exposing host systems to indirect prompt injection, session hijacking, and unauthorized web actions if executed without strict sandboxing.
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 | 0.30 | |
| Contextual Awareness | 0.70 | |
| Dynamic Identity | 0.60 | |
| Multi-Agent Interactions | 0.30 | |
| Non-Determinism | 0.80 | |
| 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.
Not certain from the listing — the library is model-agnostic but relies on LLMs to interpret DOM elements and plan actions, making it highly vulnerable to indirect prompt injection via malicious web page content.
Not certain from the listing — does not detail specific vector stores or training data operations, but handles dynamic web data extraction which could lead to data poisoning if malicious web content is ingested and processed.
As an orchestration framework for browser automation, it is highly vulnerable to tool misuse and insecure tool integration, where malicious web elements can hijack the agent's execution flow and force unintended browser actions.
Not certain from the listing — being a PyPI package, deployment security (such as sandboxing the browser instance or containerization) is left entirely to the user, risking host compromise if the browser is hijacked.
Not certain from the listing — no built-in guardrails, evaluation suites, or logging mechanisms are specified, creating potential blind spots during automated web sessions.
Not certain from the listing — lacks built-in authentication, authorization, or compliance policies, relying on the host application to enforce security boundaries and session limits.
Designed to enable other AI agents to interact with the web, creating a high risk of cascading failures or trust abuse if integrated into multi-agent workflows without strict boundaries.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).