Agentspace — agentic threat model
Agentspace presents a high-impact risk profile due to its deep integration with enterprise-wide data silos and application connectors. While its 'secure by design' infrastructure and granular access controls mitigate some risk, the ability to create custom agents and execute complex tasks across multiple platforms elevates the potential for data exfiltration and unauthorized tool execution.
OWASP AIVSS score rationale
| Autonomy of Action | 0.60 | |
| Goal-Driven Planning | 0.70 | |
| Self-Modification | 0.20 | |
| Dynamic Tool Use | 0.80 | |
| Persistent Memory | 0.60 | |
| Contextual Awareness | 0.80 | |
| Dynamic Identity | 0.50 | |
| Multi-Agent Interactions | 0.70 | |
| 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.
Integrates Gemini AI for advanced reasoning. Primary threats include prompt injection, adversarial manipulation of multimodal inputs, and model-level data leakage of sensitive enterprise context.
Utilizes enterprise-wide search, NotebookLM integration, and pre-built connectors. Highly vulnerable to knowledge-base poisoning, unauthorized data discovery across silos, and embedding inversion attacks.
Orchestrates tasks and custom agent creation. Risks include insecure tool integration via enterprise connectors, prompt injection leading to unauthorized task execution, and state manipulation within NotebookLM.
Not certain from the listing — likely hosted on Google Cloud Platform with enterprise-grade infrastructure, but specific sandboxing mechanisms for custom-created agents and third-party connectors are unverified.
Not certain from the listing — no explicit mention of real-time guardrails, evaluation frameworks, or observability logging, which are critical for detecting anomalous agent behavior across enterprise apps.
Features 'secure by design' infrastructure with granular access controls. Key threats involve misconfiguration of these access controls, privilege escalation, and compliance gaps regarding cross-border data translation.
Supports custom AI agent creation and management. Vulnerable to rogue or compromised custom agents, cascading failures across multi-agent workflows, and unauthorized agent-to-agent trust abuse.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).