Epsilla — agentic threat model
Epsilla presents a moderate-to-high risk profile as an enterprise agent-building platform; while it promises robust security controls, its deep integration with private enterprise data and external tools creates a high-value target for prompt injection and data exfiltration.
OWASP AIVSS score rationale
| Autonomy of Action | 0.60 | |
| Goal-Driven Planning | 0.70 | |
| Self-Modification | 0.30 | |
| Dynamic Tool Use | 0.70 | |
| Persistent Memory | 0.60 | |
| Contextual Awareness | 0.80 | |
| Dynamic Identity | 0.40 | |
| Multi-Agent Interactions | 0.50 | |
| Non-Determinism | 0.70 | |
| Opacity & Reflexivity | 0.60 |
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 does not specify which foundation models are supported or how they are hosted, leaving open standard LLM vulnerabilities such as prompt injection, model alignment drift, and membership inference.
Since Epsilla builds agents 'powered by your private data', it heavily relies on vector databases and RAG pipelines. This introduces significant risks of data/knowledge-base poisoning, unauthorized data exfiltration, and embedding inversion.
As an agent-building platform, Epsilla orchestrates planning, memory, and tool execution. Vulnerabilities here include insecure tool integration, prompt injection leading to unauthorized tool calling, and state manipulation.
Not certain from the listing — The deployment architecture (SaaS vs. self-hosted/VPC) and sandboxing capabilities for executing agent code or tools are not detailed, presenting risks of container escape or secrets exposure.
Not certain from the listing — While positioned for enterprise use, the listing does not detail specific observability, guardrails, or real-time monitoring tools to detect anomalous agent behavior or drift.
The platform claims to be 'fully equipped for enterprise customers with security', implying the presence of access controls, authentication, and compliance frameworks, though specific certifications are not listed.
Not certain from the listing — It is unclear if the platform supports a multi-agent ecosystem, third-party marketplaces, or cross-organization agent interactions, which could introduce cascading trust boundaries and rogue agent risks.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).