Devyan — agentic threat model
Devyan presents a high agentic risk profile due to its multi-agent orchestration of software development tasks, which likely involves executing generated code (e.g., during testing) and accessing sensitive code repositories without built-in sandboxing or verification controls.
OWASP AIVSS score rationale
| Autonomy of Action | 0.80 | |
| Goal-Driven Planning | 0.90 | |
| Self-Modification | 0.20 | |
| Dynamic Tool Use | 0.80 | |
| Persistent Memory | 0.50 | |
| Contextual Awareness | 0.70 | |
| Dynamic Identity | 0.30 | |
| Multi-Agent Interactions | 1.00 | |
| 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.
Uses OpenAI's GPT-based models. Vulnerable to prompt injection that could hijack the developer, architect, or tester roles to inject malicious code or bypass design constraints.
Not certain from the listing — likely reads local codebases or repositories, posing risks of data exfiltration or processing malicious source code (data poisoning) if the repository contains untrusted inputs.
Orchestrates multiple agents for architecture, implementation, testing, and reviewing. Vulnerable to insecure tool integration (e.g., executing test suites that run arbitrary code) and orchestration logic bypasses.
Not certain from the listing — as an open-source coding agent, it likely runs locally or in user-provisioned environments. If not strictly sandboxed, code execution during the 'testing' phase poses severe host compromise risks.
Not certain from the listing — no explicit mention of logging, guardrails, or evaluation frameworks to monitor agent-to-agent communication or code generation safety.
Not certain from the listing — being open-source, compliance and access controls (authN/authZ) depend entirely on the user's deployment environment and API key management.
Employs a multi-agent ecosystem (architecture, implementation, testing, reviewing). Vulnerable to agent-to-agent trust abuse, where a compromised implementation agent fools the reviewer agent, leading to cascading security failures in the generated software.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).