BaseAI.dev — agentic threat model
BaseAI.dev is an open-source serverless AI agent framework that introduces notable risk through its support for autonomous agents, self-healing tools, and deep reasoning memory deployed directly to cloud environments without built-in security guardrails mentioned in the listing.
OWASP AIVSS score rationale
| Autonomy of Action | 0.70 | |
| Goal-Driven Planning | 0.60 | |
| Self-Modification | 0.50 | |
| Dynamic Tool Use | 0.70 | |
| Persistent Memory | 0.80 | |
| Contextual Awareness | 0.70 | |
| Dynamic Identity | 0.20 | |
| 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.
Not certain from the listing — BaseAI is a framework and does not specify a default foundation model, meaning model-level threats (adversarial examples, data poisoning) depend entirely on the developer's choice of LLM.
Explicitly supports 'memory (RAG)' and 'agentic deep reasoning memory'. This introduces risks of knowledge-base poisoning, embedding inversion, and unauthorized data exfiltration if the memory store is compromised.
As an orchestration framework supporting 'composable AI pipes' and 'self-healing tools', vulnerabilities in tool integration or logic could allow attackers to manipulate tool execution paths or exploit self-healing mechanisms to run malicious code.
Deploys to serverless environments via 'npx baseai deploy' to Langbase. Threats include serverless container escape, insecure handling of API keys/secrets during deployment, and lateral movement within the hosting cloud.
Not certain from the listing — There is no mention of built-in evaluation, logging, guardrails, or observability features to detect drift, anomalies, or malicious inputs.
Not certain from the listing — The framework does not detail built-in identity management, access control policies, or compliance certifications (e.g., SOC2, GDPR).
Supports 'composable AI pipes (agents)' and deployment to Langbase, implying multi-agent pipelines. Threats include cascading failures across chained agents and trust abuse between interconnected pipes.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).