Latitude — agentic threat model
Latitude is a highly capable meta-agent and orchestration platform with a massive attack surface due to its integration with over 2,800 tools and multi-agent generation capabilities. While its strong observability and evaluation features aid in monitoring, the lack of explicit sandboxing and security controls for deployed agents presents significant operational and systemic risks.
OWASP AIVSS score rationale
| Autonomy of Action | 0.80 | |
| Goal-Driven Planning | 0.70 | |
| Self-Modification | 0.60 | |
| Dynamic Tool Use | 0.90 | |
| Persistent Memory | 0.50 | |
| Contextual Awareness | 0.60 | |
| Dynamic Identity | 0.40 | |
| Multi-Agent Interactions | 0.80 | |
| Non-Determinism | 0.70 | |
| 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 platform likely supports multiple external foundation models (e.g., OpenAI, Anthropic) to power its meta-agent 'Latte' and generated agents, exposing them to prompt injection, model misalignment, and adversarial manipulation.
Not certain from the listing — while synthetic dataset generation is supported, the specific vector stores, RAG pipelines, or data lineage controls for training and grounding the generated agents are not detailed.
Latitude acts as an orchestration framework allowing meta-agents to generate sub-agents and connect to 2,800+ tools. This massive tool integration surface introduces severe risks of tool misuse, insecure tool execution, and prompt injection leading to unauthorized API calls.
Not certain from the listing — the deployment infrastructure (cloud hosting, containerization, sandboxing of tool executions, and secrets management for the 2,800+ integrations) is not specified.
Strong focus on observability with step-by-step behavior tracking, sub-agent evaluation, and A/B testing of prompt strategies, which helps mitigate evaluation gaming and drift, though runtime guardrails are not explicitly detailed.
Not certain from the listing — there is no mention of enterprise security controls, role-based access control (RBAC), data privacy compliance, or audit logging for the platform's administrative actions.
Supports a multi-agent ecosystem where a meta-agent ('Latte') generates and orchestrates sub-agents. This introduces risks of cascading failures, delegation of unauthorized tasks between agents, and complex trust boundaries.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).