Limitus AI — agentic threat model
Limitus AI presents a high-risk profile due to its integration of Web2 and Web3 automation, where compromised agent logic or hijacked credentials could lead to irreversible blockchain transactions and financial loss.
OWASP AIVSS score rationale
| Autonomy of Action | 0.80 | |
| Goal-Driven Planning | 0.60 | |
| Self-Modification | 0.10 | |
| Dynamic Tool Use | 0.80 | |
| Persistent Memory | 0.50 | |
| Contextual Awareness | 0.60 | |
| Dynamic Identity | 0.80 | |
| Multi-Agent Interactions | 0.50 | |
| Non-Determinism | 0.50 | |
| 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 specific foundation models powering LIMITUS are not disclosed. Standard LLM risks like prompt injection, adversarial reprogramming, and misaligned outputs apply, especially if the models are used to interpret natural language commands for Web2/Web3 automation.
Not certain from the listing — No details are provided regarding data storage, vector databases, or RAG pipelines. Risks include data poisoning or exfiltration of sensitive Web2/Web3 credentials or transaction histories if stored insecurely.
Not certain from the listing — While it is described as an automation platform integrating Web2 and Web3, the exact orchestration framework is unspecified. Insecure tool integration and unauthorized execution of blockchain transactions or API calls are major risks.
Not certain from the listing — The platform is described as decentralized, which implies a distributed node infrastructure, but specific sandboxing, secret management for Web2/Web3 keys, and hosting details are not provided.
Not certain from the listing — There is no mention of monitoring, logging, or guardrails to detect anomalous agent behavior or malicious transaction attempts before they are committed to the blockchain.
Not certain from the listing — No compliance certifications (like SOC2) or specific authorization policies are mentioned, which is critical given the integration of Web3 financial transactions and Web2 systems.
Not certain from the listing — The platform's decentralized nature suggests a multi-agent or distributed ecosystem, but specific details on agent-to-agent trust boundaries or marketplace dynamics are absent.
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.