Fixr — agentic threat model
Fixr presents a high-risk profile due to its autonomous ability to generate and deploy smart contracts and APIs combined with anonymous, API-keyless access via x402 micropayments, making it a prime target for automated exploitation and malicious code propagation.
OWASP AIVSS score rationale
| Autonomy of Action | 0.80 | |
| Goal-Driven Planning | 0.70 | |
| Self-Modification | 0.20 | |
| Dynamic Tool Use | 0.80 | |
| Persistent Memory | 0.40 | |
| Contextual Awareness | 0.70 | |
| Dynamic Identity | 0.50 | |
| Multi-Agent Interactions | 0.80 | |
| Non-Determinism | 0.60 | |
| 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 — The specific foundation models powering the smart contract auditing and code generation are not disclosed. Threats include prompt injection that could bypass security audit logic or force the model to generate backdoored smart contracts.
Not certain from the listing — The mechanisms for storing and retrieving wallet intelligence, token directories, and audit histories are unspecified. Threats include data poisoning of the token analysis database to artificially inflate rug pull or honeypot scores.
The agent orchestrates complex workflows including code generation, auditing, and deployment across multiple chains. Threats include tool misuse where the agent is manipulated into executing unauthorized on-chain transactions or deploying malicious contracts.
The agent deploys x402 APIs, Farcaster mini apps, and smart contracts (EVM/Solana). Threats include insecure deployment environments, lack of sandboxing during contract compilation/testing, and exposure of deployment private keys.
Not certain from the listing — There is no mention of real-time monitoring, guardrails, or evaluation frameworks for the generated code or audit outputs. Threats include undetected drift in audit accuracy and lack of logging for malicious exploit attempts.
The agent uses x402 micropayments on Base which bypasses traditional API key authentication. While lowering friction, this introduces significant authorization and rate-limiting challenges, potentially allowing anonymous actors to abuse the auditing and deployment endpoints.
The agent is highly integrated into the multi-agent ecosystem, offering 120+ endpoints for other agents to call via x402 and hosting a directory for ERC-8004 verified agent tokens. Threats include cascading failures where compromised peer agents exploit Fixr's deployment capabilities to launch malicious sub-agents.
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.