loki-mode — agentic threat model
loki-mode presents a high-risk agentic profile due to its multi-agent orchestration, autonomous code generation, and deployment capabilities, which create a wide attack surface for remote code execution and supply chain compromise.
OWASP AIVSS score rationale
| Autonomy of Action | 0.90 | |
| Goal-Driven Planning | 0.90 | |
| Self-Modification | 0.50 | |
| Dynamic Tool Use | 0.90 | |
| Persistent Memory | 0.60 | |
| Contextual Awareness | 0.80 | |
| Dynamic Identity | 0.40 | |
| Multi-Agent Interactions | 1.00 | |
| Non-Determinism | 0.80 | |
| Opacity & Reflexivity | 0.70 |
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.
Uses 5 external AI providers. Threats include adversarial prompt injection bypassing the 11 quality gates, leading to malicious code generation or unauthorized deployment actions.
Not certain from the listing — processes specifications (PRD, GitHub issues, OpenAPI/JSON/YAML) which are susceptible to data poisoning or injection of malicious requirements that corrupt the downstream code generation pipeline.
Orchestrates a multi-agent autonomous software-development framework. High risk of tool misuse, insecure tool integration, and framework vulnerabilities during the spec-to-deployed-app pipeline.
Not certain from the listing — the agent builds and deploys applications, implying access to hosting environments, container registries, and secrets. If unsandboxed, this poses severe risks of container escape, host compromise, and lateral movement.
Features 11 quality gates to evaluate code and deployment readiness. However, these gates may have blind spots or be bypassed by sophisticated adversarial code patterns generated by the LLM.
Not certain from the listing — as an open-source community skill, it lacks explicit details on access control, identity management, and audit logging for deployment actions, risking unauthorized code execution.
Employs heavy multi-agent and swarm orchestration. Highly vulnerable to agent-to-agent trust abuse, cascading failures across the swarm, and compromised agents generating malicious pull requests or deployments.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).