EvoMap — agentic threat model
EvoMap presents a high-risk profile due to its focus on autonomous self-evolution and decentralized capability sharing via the Genome Evolution Protocol, which could allow malicious or corrupted skills to propagate rapidly across an entire agent ecosystem.
OWASP AIVSS score rationale
| Autonomy of Action | 0.90 | |
| Goal-Driven Planning | 0.80 | |
| Self-Modification | 1.00 | |
| Dynamic Tool Use | 0.80 | |
| Persistent Memory | 0.70 | |
| Contextual Awareness | 0.60 | |
| Dynamic Identity | 0.50 | |
| Multi-Agent Interactions | 1.00 | |
| Non-Determinism | 0.90 | |
| Opacity & Reflexivity | 0.80 |
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 — EvoMap acts as a cross-model protocol layer rather than specifying a concrete foundation model, leaving it vulnerable to inherited adversarial vulnerabilities across diverse underlying LLMs.
Not certain from the listing — The description does not detail vector stores or RAG data operations, though the exchange of 'genomes' implies serialized capability data that could be subject to poisoning or injection.
EvoMap's core framework relies on the Genome Evolution Protocol (GEP) for skill inheritance, introducing severe risks of malicious skill injection, logic flaws in inherited planning capabilities, and insecure tool integration during runtime adaptation.
Not certain from the listing — Mentions a decentralized network and cross-region interoperability but lacks specific details regarding containerization, sandboxing, or secure hosting infrastructure.
The platform utilizes 'decentralized validation' of AI skills, which is a critical observability control but remains highly vulnerable to validation gaming, collusion, or consensus manipulation by compromised nodes.
Not certain from the listing — While claiming to be a 'secure framework for agent capability exchange,' the listing does not specify concrete compliance standards, cryptographic identity verification, or access control mechanisms.
The decentralized, multi-agent ecosystem of EvoMap is highly susceptible to cascading failures and trust abuse, where a single rogue or compromised agent could propagate malicious 'genomes' to rapidly infect other agents in the network.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).