Zeta Labs — agentic threat model
Zeta Labs is positioned as a closed-source digital worker and AI assistant, but the extremely sparse directory listing provides no details on its architecture, integrations, or security controls, resulting in a highly uncertain risk profile.
OWASP AIVSS score rationale
| Autonomy of Action | 0.30 | |
| Goal-Driven Planning | 0.20 | |
| Self-Modification | 0.00 | |
| Dynamic Tool Use | 0.20 | |
| Persistent Memory | 0.10 | |
| Contextual Awareness | 0.20 | |
| Dynamic Identity | 0.00 | |
| Multi-Agent Interactions | 0.00 | |
| Non-Determinism | 0.30 | |
| Opacity & Reflexivity | 0.40 |
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 underlying foundation models are not specified. Standard LLM risks like prompt injection, adversarial examples, and misaligned outputs apply.
Not certain from the listing — No details are provided regarding data ingestion, RAG, vector databases, or training data pipelines. Standard risks include data exfiltration and knowledge poisoning.
Not certain from the listing — The orchestration framework, memory architecture, and tool-calling mechanisms are undisclosed. Risks include insecure tool execution and memory poisoning.
Not certain from the listing — The hosting environment, sandboxing capabilities, and network security controls are unknown. Standard risks include container escape or unauthorized access to the closed-source hosting infrastructure.
Not certain from the listing — There is no mention of guardrails, monitoring, logging, or evaluation frameworks to detect drift or malicious inputs.
Not certain from the listing — No compliance certifications (e.g., SOC2, ISO 27001), identity management, or access control policies are mentioned.
Not certain from the listing — It is unclear if this digital worker interacts with other agents or operates within a multi-agent marketplace.
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.