KodeAgent — agentic threat model
KodeAgent presents a high-risk security profile primarily due to its out-of-the-box CodeAct (code-executing) capabilities combined with a lack of native sandboxing. While its strong observability integrations (Langfuse/Langsmith) and 'Glassbox' design aid in debugging, executing arbitrary code as a core reasoning mechanism requires strict external isolation to prevent host compromise.
OWASP AIVSS score rationale
| Autonomy of Action | 0.80 | |
| Goal-Driven Planning | 0.80 | |
| Self-Modification | 0.40 | |
| Dynamic Tool Use | 0.90 | |
| Persistent Memory | 0.20 | |
| Contextual Awareness | 0.50 | |
| Dynamic Identity | 0.10 | |
| Multi-Agent Interactions | 0.20 | |
| Non-Determinism | 0.70 | |
| Opacity & Reflexivity | 0.40 |
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.
Supports swapping between major LLMs (Gemini, OpenAI, Claude). Threats include model-specific prompt injection, alignment drift, and dependency on external API security postures.
Not certain from the listing — No native vector database or RAG pipeline is detailed; data operations appear limited to transient task context and Recurrent Mode.
Supports ReAct and CodeAct. CodeAct allows direct code execution, which introduces severe risks of arbitrary code execution and tool misuse if the agent's planning logic is hijacked via prompt injection.
Not certain from the listing — No sandboxing, containerization, or secure execution environments are specified for the CodeAct engine, posing immediate host-compromise and privilege-escalation risks if run unsandboxed.
Excellent observability posture with native Langfuse and Langsmith support, alongside an internal 'Observer' to monitor trajectory. Risks include the exposure of sensitive execution logs to these third-party platforms.
Not certain from the listing — No built-in authentication, authorization, policy enforcement, or compliance frameworks are mentioned for this minimalistic library.
Not certain from the listing — While designed to integrate into larger systems, there is no native multi-agent orchestration, marketplace, or agent-to-agent trust boundaries described.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).