Credal — agentic threat model
Credal presents a moderate-to-high agentic risk profile due to its extensive data connectivity, multi-agent collaboration, and action-taking capabilities, which are significantly mitigated by its robust, built-in enterprise permissions mirroring.
OWASP AIVSS score rationale
| Autonomy of Action | 0.70 | |
| Goal-Driven Planning | 0.60 | |
| Self-Modification | 0.40 | |
| Dynamic Tool Use | 0.80 | |
| Persistent Memory | 0.50 | |
| Contextual Awareness | 0.80 | |
| Dynamic Identity | 0.70 | |
| Multi-Agent Interactions | 0.80 | |
| 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 — The listing does not specify which foundation models are used (though it mentions configurable agents). Standard LLM threats like prompt injection, adversarial examples, and misaligned outputs apply.
Credal connects to all major data sources. Threats include data exfiltration, knowledge-base poisoning, or unauthorized access if the permissions mirroring mechanism fails or is bypassed.
Orchestrates multi-agent collaboration and supports an open-source actions library for custom actions. Threats include insecure tool integration, malicious custom actions, and tool misuse.
Not certain from the listing — Mentions deployment via Slack, API, and No-code, but hosting details (SaaS vs. VPC) are not specified. Threats include container compromise, API key exposure, and Slack workspace integration abuse.
Mentions self-improving feedback loops that incorporate subject matter expertise. Threats include feedback loop poisoning or blind spots in monitoring custom actions.
Strong focus on enterprise-grade security with permissions mirroring that automatically mirrors underlying permissions across all connected data sources. Threats include authorization bypasses, sync lag in permission mirroring, and compliance drift.
Supports multi-agent collaboration and an open-source actions library. Threats include cascading failures across collaborating agents, A2A trust abuse, and malicious custom actions introduced via the open-source actions library.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).