Web3GPT — agentic threat model
Web3GPT presents a high-risk profile due to its ability to deploy smart contracts and execute wallet-less blockchain transactions, where prompt injection or model compromise could lead to direct financial loss or the deployment of vulnerable/malicious decentralized applications.
OWASP AIVSS score rationale
| Autonomy of Action | 0.70 | |
| Goal-Driven Planning | 0.60 | |
| Self-Modification | 0.10 | |
| Dynamic Tool Use | 0.80 | |
| Persistent Memory | 0.30 | |
| Contextual Awareness | 0.60 | |
| Dynamic Identity | 0.70 | |
| Multi-Agent Interactions | 0.40 | |
| Non-Determinism | 0.70 | |
| Opacity & Reflexivity | 0.50 |
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 — likely relies on external GPT models. Threats include prompt injection causing the model to generate backdoored smart contracts or insecure Web3.js code templates.
Not certain from the listing — likely utilizes RAG or vector databases for Web3.js documentation and EVM smart contract templates. Threats include knowledge-base poisoning that introduces vulnerable coding patterns.
The agent framework orchestrates smart contract deployment and automated testing. Threats include tool misuse (e.g., deploying to unintended networks) and insecure tool integration where malicious inputs trigger unintended blockchain interactions.
Not certain from the listing — requires hosting for the chat interface and backend execution of 'wallet-less' transactions. Threats include exposure of backend private keys used to facilitate wallet-less deployments and lack of sandboxing during automated contract testing.
Not certain from the listing — no mention of real-time guardrails or transaction monitoring. Threats include a lack of audit logs for deployed contracts, making it difficult to trace malicious deployments back to specific sessions.
As an open-source, free tool, there is no evidence of formal security compliance (e.g., SOC2, ISO) or strict identity and access management controls for managing blockchain deployment permissions.
Not certain from the listing — mentions combining LLMs and AI agents, but does not detail a multi-agent ecosystem. Threats include potential trust abuse if the agent interacts with external Web3 oracle agents or decentralized protocols.
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.