doubt-driven-development — agentic threat model
This agent acts as a high-stakes decision gatekeeper by spawning adversarial subagents, introducing risks of subagent orchestration hijacking and cascading failures if the verification loop is bypassed or manipulated.
OWASP AIVSS score rationale
| Autonomy of Action | 0.60 | |
| Goal-Driven Planning | 0.70 | |
| Self-Modification | 0.20 | |
| Dynamic Tool Use | 0.40 | |
| Persistent Memory | 0.30 | |
| Contextual Awareness | 0.80 | |
| Dynamic Identity | 0.30 | |
| Multi-Agent Interactions | 0.80 | |
| 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.
Relies heavily on LLMs for adversarial reasoning and verification. Vulnerable to prompt injection that could trick the 'doubt' agent into approving unsafe or incorrect decisions by framing them as safe.
Not certain from the listing — The agent reviews code and decisions in context, but the exact mechanism of data ingestion, RAG, or repository access is not specified.
High risk in orchestration logic. Spawning adversarial verification passes introduces subagent-orchestration surface where a compromised subagent could return false negatives or hijack the parent agent's decision flow.
Not certain from the listing — The execution environment for running these verification passes (e.g., whether it executes code in a secure sandbox) is not detailed.
The agent itself acts as an evaluation/observability gate. However, if the adversarial review process lacks independent logging, its internal 'doubts' and decision-gating logic could be silently bypassed.
Not certain from the listing — No explicit compliance frameworks, access control policies, or human-in-the-loop authorization gates are defined for the final decision output.
Exhibits multi-agent risk through subagent orchestration. A compromised parent agent could spawn malicious subagents, or subagents could collude to validate incorrect high-stakes decisions.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).