Agentok AI — agentic threat model
Agentok AI leverages the AG2 (AutoGen) framework to enable complex multi-agent interactions and tool usage, presenting elevated risks of tool misuse, cascading multi-agent failures, and insecure code execution if deployed without strict sandboxing.
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 | 0.50 | |
| Contextual Awareness | 0.60 | |
| Dynamic Identity | 0.50 | |
| Multi-Agent Interactions | 0.90 | |
| 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 specific foundation models used by Agentok Studio are not detailed, though as an AG2-based platform, it likely supports various LLMs via API keys, inheriting standard risks like prompt injection and model misalignment.
Not certain from the listing — The directory listing does not specify the data storage, vector databases, or RAG pipelines utilized, leaving risks like data poisoning or embedding inversion unquantified.
Agentok Studio is built on AG2 (formerly AutoGen) and supports tool execution and code generation. This introduces significant risks of insecure tool integration, arbitrary code execution via generated AG2 code, and framework-level vulnerabilities inherent to complex orchestration.
Not certain from the listing — The hosting, sandboxing, and secrets management infrastructure are not described. If run locally or in un-sandboxed containers, the code generation and tool execution features pose severe host compromise risks.
Not certain from the listing — There is no mention of built-in evaluation, logging, guardrails, or observability tools to monitor multi-agent conversations and detect anomalous tool calls.
Not certain from the listing — No compliance certifications, access control mechanisms, or enterprise security policies are specified for this open-source tool.
With explicit support for multi-agent chat and diverse conversation patterns, the platform is highly exposed to ecosystem risks, including agent-to-agent trust abuse, cascading failures, and rogue agent behavior during complex collaborative tasks.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).