AI Legion — agentic threat model
AI Legion presents a high agentic risk profile due to its focus on autonomous, multi-agent collaboration and dynamic tool use (such as web search) without any documented built-in sandboxing, guardrails, or security controls.
OWASP AIVSS score rationale
| Autonomy of Action | 0.80 | |
| Goal-Driven Planning | 0.80 | |
| Self-Modification | 0.30 | |
| Dynamic Tool Use | 0.60 | |
| Persistent Memory | 0.50 | |
| Contextual Awareness | 0.70 | |
| Dynamic Identity | 0.20 | |
| Multi-Agent Interactions | 0.90 | |
| Non-Determinism | 0.70 | |
| Opacity & Reflexivity | 0.70 |
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.
Leverages GPT-3.5 and GPT-4 foundation models, making it susceptible to prompt injection, adversarial manipulation, and misaligned outputs that can propagate across the multi-agent system.
Not certain from the listing — no explicit details on vector databases, RAG pipelines, or data storage mechanisms are provided, though agents likely process context dynamically from web searches.
The TypeScript-based orchestration framework manages planning, memory, and tool execution (like web search). Vulnerabilities here include insecure tool integration, memory poisoning across agent sessions, and framework-level execution bugs.
Not certain from the listing — the platform is described as console-based and configurable, but details regarding containerization, sandboxing of executed code, or network isolation are not specified.
Not certain from the listing — there is no mention of built-in evaluation frameworks, real-time monitoring, logging, or guardrails to detect anomalous agent behavior.
Not certain from the listing — as an open-source framework, it does not specify built-in identity management, access control policies, or compliance alignments.
Designed specifically for multi-agent collaboration, creating a high risk of agent-to-agent trust abuse, cascading failures, and the potential for a single compromised agent to corrupt the entire collaborative workflow.
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.