DotAgent AI — agentic threat model
DotAgent AI acts as a centralized dynamic router and orchestrator for multi-model/multi-agent tasks, presenting a high-value target for API interception, downstream key theft, and routing manipulation.
OWASP AIVSS score rationale
| Autonomy of Action | 0.40 | |
| Goal-Driven Planning | 0.50 | |
| Self-Modification | 0.10 | |
| Dynamic Tool Use | 0.30 | |
| Persistent Memory | 0.20 | |
| Contextual Awareness | 0.50 | |
| Dynamic Identity | 0.30 | |
| Multi-Agent Interactions | 0.70 | |
| Non-Determinism | 0.60 | |
| 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 dynamically routes to external foundation models (like GPT-4). Threats include adversarial prompt injection bypassing the router, or downstream models returning misaligned/poisoned outputs.
Not certain from the listing — the platform processes task data to perform routing. Threats include data exfiltration of API payloads or poisoning of the 'Agent Genome' matching dataset.
The platform acts as an orchestration framework matching tasks to agents. Threats include insecure orchestration, logic flaws in the matching algorithm, or routing tasks to malicious/unintended agents.
Not certain from the listing — hosted as a cloud API. Threats include API key exposure (for downstream LLMs), container compromise, or man-in-the-middle attacks on API transit.
Not certain from the listing — mentions performance optimization but not specific security guardrails or logging. Threats include blind spots in detecting malicious payloads routed to downstream models.
Not certain from the listing — no security certifications or compliance standards are mentioned. Threats include lack of tenant isolation and unauthorized API access.
Explicitly matches tasks to 'agents' using 'Agent Genome' technology. Threats include cascading failures if a downstream agent is compromised, or trust abuse between the router and the target agents.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).