SocialScan — agentic threat model
SocialScan presents a high-risk profile due to its integration with Web3 protocols, financial trading analytics, and customizable agent capabilities, where compromise could lead to direct financial loss or manipulation of on-chain intelligence.
OWASP AIVSS score rationale
| Autonomy of Action | 0.40 | |
| Goal-Driven Planning | 0.50 | |
| Self-Modification | 0.10 | |
| Dynamic Tool Use | 0.60 | |
| Persistent Memory | 0.40 | |
| Contextual Awareness | 0.80 | |
| Dynamic Identity | 0.30 | |
| Multi-Agent Interactions | 0.40 | |
| 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.
Not certain from the listing — the underlying foundation models are not specified. Standard threats include adversarial prompt injection to manipulate sentiment analysis or trading analytics, and model reprogramming.
Not certain from the listing — details on how on-chain data and sentiment data are ingested, indexed, or stored are omitted. Threats include data poisoning of sentiment sources or on-chain indexers.
The platform orchestrates customizable AI agents for trading analytics and data mining. Threats include insecure tool integration with Web3 protocols and malicious prompt injection leading to unauthorized dApp interactions.
Not certain from the listing — hosting, sandboxing, and execution environment details for these customizable agents are not provided. Threats include container compromise or private key exposure if the infrastructure manages wallets.
Not certain from the listing — no mention of real-time guardrails, monitoring, or evaluation frameworks for the customizable agents. Gaps could lead to undetected drift in trading analytics.
Not certain from the listing — the closed-source nature and lack of explicit compliance certifications make the security posture opaque. Identity and authorization controls for Web3 wallets represent a critical threat vector.
The platform supports customizable AI agents interacting within the Web3 ecosystem. Threats include rogue agents providing manipulated on-chain intelligence or cascading failures across multi-agent interactions.
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.