LaVague — agentic threat model
LaVague presents a high agentic risk profile due to its direct integration with browser automation tools (Selenium/Playwright) driven by LLMs, making it highly susceptible to indirect prompt injection from untrusted web content.
OWASP AIVSS score rationale
| Autonomy of Action | 0.80 | |
| Goal-Driven Planning | 0.80 | |
| Self-Modification | 0.10 | |
| Dynamic Tool Use | 0.80 | |
| Persistent Memory | 0.30 | |
| Contextual Awareness | 0.70 | |
| Dynamic Identity | 0.30 | |
| Multi-Agent Interactions | 0.20 | |
| 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.
Supports local and cloud-based LLMs. The primary threat is indirect prompt injection, where malicious web page content reprogrammes the model's instructions during navigation.
Not certain from the listing — The framework extracts web data, but details on vector stores, RAG pipelines, or data lineage are not specified, risking data exfiltration of sensitive DOM content.
Uses a World Model and Action Engine to translate natural language into Selenium/Playwright commands. This orchestration is highly vulnerable to tool misuse and execution of unintended web actions if the planning phase is compromised.
Not certain from the listing — Hosting and deployment (local or cloud) are managed by the user. There is no mention of built-in containerization or sandboxing for the browser automation environment, risking host compromise.
Not certain from the listing — No explicit evaluation, logging, or guardrail mechanisms are described to monitor or intercept malicious browser actions in real-time.
Not certain from the listing — Lacks built-in security controls, access policies, or compliance frameworks, leaving authorization and session management entirely to the developer's implementation.
Not certain from the listing — The framework focuses on single-agent web automation; no multi-agent coordination or marketplace ecosystem is described.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).