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Wallet Finder AI — agentic threat model

9.1AIVSS 9.1 · Critical

Wallet Finder AI presents a high-risk profile due to its integration with real-time DeFi transaction tracking and potential copy-trading execution. A compromise of its signal generation or tool integrations could lead to direct financial losses for users mirroring poisoned trading strategies.

OWASP AIVSS score rationale

AIVSS = (CVSS_Base + AARS) × Mitigation_Factor, where AARS = (10 − CVSS_Base) × (Factor_Sum / 10) × ThM
CVSS base 8.5AARS uplift 0.57Factor sum 3.8/10Threat ×1.0Mitigation ×1.0
Autonomy of Action
0.60
Goal-Driven Planning
0.40
Self-Modification
0.10
Dynamic Tool Use
0.50
Persistent Memory
0.40
Contextual Awareness
0.70
Dynamic Identity
0.20
Multi-Agent Interactions
0.20
Non-Determinism
0.30
Opacity & Reflexivity
0.40

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.

L1 · Foundation Models⚠ not certain from listing

Not certain from the listing — The specific foundation models used for analyzing wallet histories and generating alerts are not disclosed. Threats include adversarial prompt injection designed to misclassify malicious transactions as profitable whale moves.

L2 · Data Operations✓ mapped

Ingests real-time blockchain transaction data and historical wallet records. Threats include data poisoning of the underlying blockchain indexers or RAG data sources, leading the agent to generate false buy/sell signals.

L3 · Agent Frameworks✓ mapped

Orchestrates wallet tracking, history analysis, and alert generation. If the 'mirroring' feature involves automated execution, insecure tool integration with web3 wallets or exchange APIs poses a severe risk of unauthorized fund drainage.

L4 · Deployment & Infrastructure⚠ not certain from listing

Not certain from the listing — The hosting environment and sandboxing mechanisms are unspecified. Threats include the compromise of API keys used to query blockchain nodes or user-provided exchange credentials stored in the infrastructure.

L5 · Evaluation & Observability⚠ not certain from listing

Not certain from the listing — No observability or guardrail frameworks are mentioned. The agent is vulnerable to blind spots where flash loan attacks or wash trading volume are misidentified as legitimate whale activity.

L6 · Security & Compliance (cross-cutting)⚠ not certain from listing

Not certain from the listing — There is no mention of compliance frameworks (e.g., SOC2, GDPR) or financial regulations (KYC/AML). Lack of secure credential management for copy-trading APIs represents a major compliance and security gap.

L7 · Agent Ecosystem⚠ not certain from listing

Not certain from the listing — While it operates in a multi-actor web3 ecosystem, direct agent-to-agent interactions are not detailed. Threats include cascading failures if upstream data oracles or downstream execution bots are compromised.

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.