Mocha — agentic threat model
Mocha presents a high-risk profile due to its ability to generate, deploy, and host full-stack applications with database and payment integrations from natural language prompts, making it a prime target for prompt injection leading to malicious code execution or data exfiltration.
OWASP AIVSS score rationale
| Autonomy of Action | 0.70 | |
| Goal-Driven Planning | 0.80 | |
| Self-Modification | 0.20 | |
| Dynamic Tool Use | 0.70 | |
| Persistent Memory | 0.40 | |
| Contextual Awareness | 0.60 | |
| Dynamic Identity | 0.30 | |
| Multi-Agent Interactions | 0.10 | |
| Non-Determinism | 0.70 | |
| Opacity & Reflexivity | 0.60 |
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 commercial LLMs for code generation, vulnerable to prompt injection leading to malicious code generation or model reprogramming.
Not certain from the listing — handles user-provided app requirements and database schemas, risking data leakage or poisoning of generated database structures.
Orchestrates multi-step code generation, database schema design, and payment setup. Vulnerable to prompt injection that hijacks tool execution to generate backdoored code or unauthorized payment configurations.
Deploys full-stack apps with databases and authentication. Requires strict sandboxing of generated code to prevent container escape, lateral movement, or host compromise on the hosting infrastructure.
Not certain from the listing — lacks visible guardrails or automated security scanning for generated code, potentially deploying vulnerable apps (e.g., SQLi, XSS) to production.
Manages sensitive operations including user authentication and payment integrations. Requires robust access controls and compliance (e.g., PCI-DSS) to prevent unauthorized billing or credential theft.
Not certain from the listing — primarily functions as a standalone builder; no explicit multi-agent or marketplace interactions mentioned.
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.