k8s-security-policies — agentic threat model
This agent skill presents high risk due to its capability to generate and apply critical Kubernetes configurations (RBAC, NetworkPolicies, admission controls); if compromised or manipulated via prompt injection, it could lead to full cluster takeover or severe security degradation.
OWASP AIVSS score rationale
| Autonomy of Action | 0.50 | |
| Goal-Driven Planning | 0.30 | |
| Self-Modification | 0.00 | |
| Dynamic Tool Use | 0.70 | |
| Persistent Memory | 0.10 | |
| Contextual Awareness | 0.50 | |
| Dynamic Identity | 0.20 | |
| Multi-Agent Interactions | 0.30 | |
| Non-Determinism | 0.50 | |
| 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.
Not certain from the listing — The underlying LLM is not specified. Standard risks include prompt injection leading to the generation of insecure or malicious K8s manifests (e.g., privilege escalation in RBAC).
Not certain from the listing — The source of the 'production-grade Kubernetes security guidance' and compliance mappings is unspecified. Risks include poisoned reference manifests or outdated compliance data.
The skill supplies manifest patterns that the agent applies to cluster configs. Threat: Insecure tool integration where the agent executes `kubectl` commands or applies manifests without strict schema validation or human-in-the-loop approval.
Not certain from the listing — The hosting environment of the agent and its access to the K8s API are not detailed. Threat: If the agent runs with high-privilege K8s service accounts, a compromise allows full cluster takeover.
Not certain from the listing — No monitoring, logging, or guardrails are mentioned. Threat: Lack of validation on generated manifests before applying them to production clusters.
The agent maps controls to compliance requirements and enforces least-privilege RBAC. Threat: Misalignment or incorrect mapping of compliance controls, leading to false sense of security or compliance violations.
The skill is designed to be injected into an agent. Threat: A compromised host agent could abuse this skill to inject malicious NetworkPolicies or RBAC rules, or a malicious skill update could compromise the host agent.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).