OpenLobster — agentic threat model
OpenLobster presents a high agentic risk profile due to its multi-channel integration, scheduling capabilities, and MCP tool execution, though this is partially mitigated by its strong focus on built-in security controls like encrypted secrets, dashboard authentication, and per-user permissions.
OWASP AIVSS score rationale
| Autonomy of Action | 0.80 | |
| Goal-Driven Planning | 0.70 | |
| Self-Modification | 0.20 | |
| Dynamic Tool Use | 0.80 | |
| Persistent Memory | 0.90 | |
| Contextual Awareness | 0.80 | |
| Dynamic Identity | 0.60 | |
| Multi-Agent Interactions | 0.40 | |
| Non-Determinism | 0.70 | |
| 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.
Connects to multiple external and local LLM providers (OpenAI, Anthropic, Ollama, OpenRouter). Primary threats include prompt injection attacks that bypass system instructions, causing the agent to execute unauthorized actions or leak sensitive user data across its multi-user environment.
Uses Neo4j or file-based structured memory for long-term context. Threats include graph database injection, memory poisoning via malicious inputs from external channels (e.g., Slack or Telegram), and unauthorized extraction of sensitive user history.
Features a real scheduler and MCP integrations. Threats include scheduler manipulation to execute unauthorized recurring tasks, tool misuse via compromised MCP servers, and insecure handling of tool outputs that could lead to remote code execution.
Self-hosted deployment utilizing a GraphQL API and encrypted secrets storage. Threats include GraphQL injection, unauthorized API access, container escape if hosted insecurely, and potential exposure of the encrypted secrets key in self-hosted environments.
Not certain from the listing — there is no explicit mention of evaluation frameworks, real-time guardrails, or observability/logging tools. Without these, detecting prompt injection, drift, or anomalous tool execution in production will be difficult.
Includes dashboard authentication enabled by default, per-user histories/permissions, and OAuth support for MCP. Threats include privilege escalation between users, authentication bypass on the dashboard, and misconfigured OAuth flows.
Integrates with external communication ecosystems (Telegram, Discord, Slack, WhatsApp, Twilio). Threats include channel hijacking, API key compromise for these platforms, and cascading failures if a connected channel is compromised and used to inject malicious commands.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).
These scores are auto-generated from public information (the agent's own listing, docs, and repository) using the canonical OWASP AIVSS formula and the MAESTRO framework — an estimate for guidance, not a penetration test, audit, or certification. See the scoring methodology. Are you the vendor? Factual corrections are free.