Dynamiq — agentic threat model
Dynamiq is a powerful end-to-end agentic platform whose primary risks stem from its deep integration with internal APIs, multi-agent orchestration, and model fine-tuning capabilities. While its built-in guardrails and observability features provide strong mitigation vectors, a compromise of the orchestration layer could lead to widespread internal API abuse and data exfiltration.
OWASP AIVSS score rationale
| Autonomy of Action | 0.80 | |
| Goal-Driven Planning | 0.80 | |
| Self-Modification | 0.30 | |
| Dynamic Tool Use | 0.80 | |
| Persistent Memory | 0.70 | |
| Contextual Awareness | 0.80 | |
| Dynamic Identity | 0.20 | |
| Multi-Agent Interactions | 0.90 | |
| 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.
Supports fine-tuning of proprietary LLM models and custom LLM agents. Key threats include model poisoning during fine-tuning, model stealing of proprietary weights, and adversarial prompt injection bypassing alignment.
Provides custom RAG knowledge bases and vector DB deployments. Primary threats involve knowledge-base poisoning, embedding inversion, and unauthorized data exfiltration via RAG retrieval mechanisms.
Orchestrates custom LLM agents and connects them to internal APIs. Threats include insecure tool integration, privilege escalation via API execution, and malicious tool manipulation by compromised agents.
Not certain from the listing — details regarding container isolation, secure credential storage for internal APIs, and network sandboxing are not specified in the public directory.
Features robust observability, interaction logging, LLM quality evaluations, and guardrails (validators, sensitive content detection, data leak prevention). Threats include guardrail evasion and evaluation gaming.
Not certain from the listing — while data leak prevention and guardrails are mentioned, specific identity management, role-based access control (RBAC), and regulatory compliance standards are not detailed.
Supports multi-agent orchestration. Key threats include agent-to-agent trust abuse, cascading failures across orchestrated workflows, and rogue agent behavior within the ecosystem.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).