LAgent — agentic threat model
LAgent is an open-source agent development and training framework, presenting a moderate-to-high risk profile primarily driven by supply chain vulnerabilities, insecure default configurations, and the potential for arbitrary code execution during model training or tool integration.
OWASP AIVSS score rationale
| Autonomy of Action | 0.50 | |
| Goal-Driven Planning | 0.60 | |
| Self-Modification | 0.20 | |
| Dynamic Tool Use | 0.50 | |
| Persistent Memory | 0.40 | |
| Contextual Awareness | 0.50 | |
| Dynamic Identity | 0.20 | |
| Multi-Agent Interactions | 0.40 | |
| 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.
Not certain from the listing — The framework supports training and deploying various models, but the specific foundation models used and their susceptibility to adversarial attacks or alignment issues depend entirely on the developer's choice of integration.
Not certain from the listing — While 'model training support' is highlighted, the framework's specific data ingestion pipelines, vector database integrations, and protections against training data poisoning are not detailed.
As an open-source agent framework, the primary risks reside in how it orchestrates tool execution and manages state. Insecure tool integration or vulnerabilities within the framework's core libraries could allow malicious inputs to trigger unauthorized actions or remote code execution.
Not certain from the listing — The framework provides 'deployment tools' for production environments, but the underlying infrastructure security, container sandboxing, and secrets management are left to the implementing developer.
Not certain from the listing — There is no mention of built-in evaluation, logging, or guardrail mechanisms to monitor agent behavior and detect drift or anomalous tool calls in production.
Not certain from the listing — The directory listing does not indicate any built-in enterprise security controls, role-based access control (RBAC), or compliance certifications (e.g., SOC2, ISO 27001).
Not certain from the listing — Although it supports 'various integrations' to handle complex tasks, it is unclear if the framework natively orchestrates multi-agent ecosystems or relies on external marketplaces, which would introduce agent-to-agent trust risks.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).