Fomo.fund — agentic threat model
Fomo.fund presents a high-risk profile due to the intersection of autonomous AI agents, social media integration (Twitter/Telegram), and financial operations (cryptocurrency token creation). The combination of public-facing communication tools and financial incentives makes it a prime target for market manipulation, unauthorized token transactions, and social engineering attacks.
OWASP AIVSS score rationale
| Autonomy of Action | 0.80 | |
| Goal-Driven Planning | 0.60 | |
| Self-Modification | 0.40 | |
| Dynamic Tool Use | 0.80 | |
| Persistent Memory | 0.50 | |
| Contextual Awareness | 0.80 | |
| Dynamic Identity | 0.80 | |
| Multi-Agent Interactions | 0.50 | |
| Non-Determinism | 0.80 | |
| 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.
Not certain from the listing — The specific foundation models powering the interactive agents and voice chat are not disclosed. Potential risks include model reprogramming to promote fraudulent tokens or adversarial prompt injection via public social media channels.
Not certain from the listing — The data ingestion pipelines, RAG sources, and vector databases used to keep agents contextually aware of market trends are unspecified, leaving them vulnerable to data poisoning and market-feed manipulation.
The agent framework orchestrates social media posting, two-way voice chat, and token-related actions. Insecure tool integration or prompt injection could allow attackers to hijack the agent's communication channels to broadcast malicious links or execute unauthorized financial transactions.
Not certain from the listing — The hosting infrastructure, sandboxing of agent execution environments, and secret management for social media API keys and crypto private keys are not detailed, presenting risks of credential theft and infrastructure compromise.
Not certain from the listing — There is no mention of real-time monitoring, guardrails for financial advice, or anomaly detection to prevent agents from engaging in market manipulation or generating toxic content.
While community governance is highlighted as a mechanism to influence agent evolution, traditional security compliance frameworks, identity management, and access controls for token deployment are not specified.
The platform hosts an ecosystem of multiple token-associated agents. This creates a high risk of cascading failures, where compromised agents collude to manipulate token prices, exploit other agents, or abuse trust across the platform's community.
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.