Langbase — agentic threat model
Langbase is a highly composable, serverless AI agent platform managing RAG memory and API keys across 100+ LLMs. Its primary risk posture is defined by its multi-tenant infrastructure, where a compromise could lead to widespread credential theft, data exfiltration from managed vector stores, and unauthorized LLM consumption.
OWASP AIVSS score rationale
| Autonomy of Action | 0.50 | |
| Goal-Driven Planning | 0.40 | |
| Self-Modification | 0.20 | |
| Dynamic Tool Use | 0.70 | |
| Persistent Memory | 0.80 | |
| Contextual Awareness | 0.70 | |
| Dynamic Identity | 0.30 | |
| Multi-Agent Interactions | 0.50 | |
| 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.
Supports over 100+ LLMs via a unified API. Risks include prompt injection bypassing the 'Pipe' abstraction, model alignment drift across different third-party providers, and potential API key exposure for the connected foundation models.
Features a 'Managed search engine API with RAG tools' (Memory). Risks include vector database poisoning, unauthorized data exfiltration via manipulated RAG queries, and embedding inversion exposing sensitive training/contextual data.
Utilizes 'Pipes' to hook LLMs to data and tools. Vulnerabilities include insecure tool integration, prompt injection manipulating the orchestration logic, and state manipulation within the serverless execution flow.
Operates on a serverless, pay-as-you-go infrastructure. Key threats include multi-tenant isolation failure, serverless container escape, denial-of-wallet attacks via automated loops, and insecure storage of LLM API keys.
Provides 'LLMOps' and 'smart cost prediction'. Risks include blind spots in logging malicious agent behaviors, evasion of cost anomaly detection, and lack of real-time semantic guardrails on inputs/outputs.
Not certain from the listing — while it mentions a collaborative GitHub-like Studio, specific enterprise security controls, RBAC, compliance certifications (such as SOC2), or data residency policies are not explicitly detailed.
Not certain from the listing — although it supports composable AI and collaboration, explicit multi-agent orchestration protocols, agent-to-agent trust boundaries, or marketplace security controls are not fully defined.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).