Great Wave AI — agentic threat model
Great Wave AI presents a moderate-to-high agentic risk profile as an enterprise multi-agent orchestration platform with API integrations and RAG capabilities. While its inclusion of human-in-the-loop evaluation and governance features mitigates some operational risks, the potential for cascading failures in chained agents and unauthorized API execution remains a key concern.
OWASP AIVSS score rationale
| Autonomy of Action | 0.60 | |
| Goal-Driven Planning | 0.70 | |
| Self-Modification | 0.20 | |
| Dynamic Tool Use | 0.70 | |
| Persistent Memory | 0.50 | |
| Contextual Awareness | 0.80 | |
| Dynamic Identity | 0.40 | |
| 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 platform is model-agnostic or supports multiple LLMs, but specific foundation models, alignment techniques, or protections against adversarial prompt injection are not detailed.
Features configurable RAG-as-a-service. This introduces risks of knowledge-base poisoning, unauthorized data access via vector search, and data exfiltration if the retrieval mechanism is manipulated by malicious prompts.
The no-code agent builder supports API integrations. Insecure tool integration, lack of input sanitization before API calls, and prompt injection leading to unauthorized tool execution are primary threats at this layer.
Not certain from the listing — Details regarding containerization, execution sandboxing for API integrations, and secrets management for enterprise credentials are not specified.
Explicitly includes automated and human-in-the-loop evaluation. This significantly mitigates evaluation gaming and blind spots, though real-time drift and anomaly detection capabilities during live deployment remain unverified.
Claims robust governance features, control, and security for enterprise adoption. However, specific compliance certifications (e.g., SOC2, ISO 27001) or fine-grained access control policies are not explicitly detailed.
Features multi-agent chaining. This introduces risks of cascading failures, agent-to-agent trust abuse, and payload propagation where a compromise in one agent compromises the entire chain.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).