LLMStack — agentic threat model
LLMStack is a powerful multi-tenant AI agent platform that presents significant risk due to its ability to orchestrate complex workflows, integrate multiple LLMs, and access external APIs and vector databases. A compromise of the platform could lead to tenant isolation breaches, unauthorized API execution, and sensitive data exfiltration across hosted applications.
OWASP AIVSS score rationale
| Autonomy of Action | 0.60 | |
| Goal-Driven Planning | 0.50 | |
| Self-Modification | 0.20 | |
| Dynamic Tool Use | 0.70 | |
| Persistent Memory | 0.60 | |
| Contextual Awareness | 0.70 | |
| Dynamic Identity | 0.40 | |
| Multi-Agent Interactions | 0.60 | |
| 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 and chains multiple foundation LLMs. Vulnerable to prompt injection, adversarial inputs, and model alignment bypasses that could disrupt downstream chained workflows.
Utilizes vector databases and data management tools for processing application data. Risks include vector database poisoning, unauthorized data exfiltration, and embedding inversion.
Provides a no-code builder to construct complex AI workflows and automate tasks. Vulnerable to insecure workflow logic, tool/API misuse, and framework-level orchestration vulnerabilities.
Supports deployment on cloud or on-premise infrastructure with API access. Risks include container escape, insecure API endpoints, and host compromise if deployment environments are not properly sandboxed.
Not certain from the listing — There is no explicit mention of built-in evaluation, monitoring, logging, or guardrail systems to detect drift, anomalies, or malicious inputs.
Features multi-tenant support, which is critical for security. However, tenant isolation failures, weak API authentication, and insufficient access controls could lead to cross-tenant data leaks.
Acts as an agent ecosystem platform by chaining multiple LLMs and workflows. Vulnerable to cascading failures across chained components and trust abuse between interconnected AI applications.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).