Random Labs — agentic threat model
Random Labs provides open-source frameworks for building AI agents, shifting the primary security responsibility to the implementing developers. The main risks stem from insecure default configurations, tool integration vulnerabilities, and the lack of built-in guardrails.
OWASP AIVSS score rationale
| Autonomy of Action | 0.50 | |
| Goal-Driven Planning | 0.60 | |
| Self-Modification | 0.30 | |
| Dynamic Tool Use | 0.50 | |
| Persistent Memory | 0.50 | |
| Contextual Awareness | 0.50 | |
| Dynamic Identity | 0.20 | |
| Multi-Agent Interactions | 0.60 | |
| Non-Determinism | 0.70 | |
| Opacity & Reflexivity | 0.50 |
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 listing does not specify which foundation models are supported or integrated by Random Labs' frameworks.
Not certain from the listing — No details are provided regarding RAG, vector databases, or training data operations managed by the framework.
As an open-source agent framework, the primary threats lie in framework-level vulnerabilities, insecure tool integration patterns, and how developers configure planning or memory structures.
Not certain from the listing — Since this is an open-source framework for developers, deployment, hosting, and sandboxing configurations are entirely up to the end-user.
Not certain from the listing — The listing does not mention built-in evaluation, logging, observability, or guardrail features.
Not certain from the listing — No compliance certifications (e.g., SOC2, ISO) or built-in policy enforcement mechanisms are mentioned.
As a framework for building software agents, it likely supports multi-agent orchestration, introducing risks of cascading failures or agent-to-agent trust abuse if not properly isolated.
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.