Langroid — agentic threat model
Langroid is a highly collaborative multi-agent framework whose primary risks stem from agent-to-agent trust abuse, insecure tool execution, and the complexity of orchestrating multiple autonomous entities.
OWASP AIVSS score rationale
| Autonomy of Action | 0.70 | |
| Goal-Driven Planning | 0.80 | |
| Self-Modification | 0.30 | |
| Dynamic Tool Use | 0.80 | |
| Persistent Memory | 0.60 | |
| Contextual Awareness | 0.70 | |
| Dynamic Identity | 0.40 | |
| Multi-Agent Interactions | 1.00 | |
| 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.
Integrates with external LLMs (both local/open and remote/proprietary). It is vulnerable to prompt injection, adversarial inputs, and misaligned outputs originating from these underlying models.
Supports vector databases for RAG. This introduces risks of knowledge-base poisoning, unauthorized data retrieval, and potential embedding inversion attacks.
As an orchestration framework supporting function calling and tools, it is highly susceptible to insecure tool integration, tool misuse, and framework-level vulnerabilities in task delegation.
Not certain from the listing — as an open-source Python library, deployment security (such as container sandboxing, network isolation, and secrets management for API keys) is entirely dependent on the developer's implementation.
Features built-in observability, logging, and caching. While these aid in monitoring, risks remain regarding the logging of sensitive data or cache poisoning attacks.
Not certain from the listing — no explicit security policies, access control mechanisms, or compliance alignments (like SOC2 or NIST) are detailed in the framework's description.
Designed specifically for multi-agent collaboration and task delegation. This paradigm is highly vulnerable to agent-to-agent trust abuse, cascading failures, and rogue agent behavior during message exchange.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).