Elastic manage-slos — agentic threat model
This agent skill poses a moderate risk as it possesses write access to Elastic Observability configurations, meaning compromise could allow an attacker to manipulate SLOs and error budgets to mask malicious activity.
OWASP AIVSS score rationale
| Autonomy of Action | 0.40 | |
| Goal-Driven Planning | 0.30 | |
| Self-Modification | 0.00 | |
| Dynamic Tool Use | 0.50 | |
| Persistent Memory | 0.10 | |
| Contextual Awareness | 0.40 | |
| Dynamic Identity | 0.20 | |
| Multi-Agent Interactions | 0.30 | |
| Non-Determinism | 0.30 | |
| Opacity & Reflexivity | 0.30 |
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 model powering this skill is not disclosed. Standard LLM risks like prompt injection could lead to malformed SLO configurations.
Not certain from the listing — while the agent reads metric/observability schemas to define indicators, it is unclear if it utilizes a local vector database or RAG architecture.
The agent framework manages tool calling specifically for Elastic Observability APIs to create and edit SLO definitions. Threats include insecure tool integration or parameter injection leading to unauthorized SLO modifications.
Not certain from the listing — the deployment model (e.g., local Elastic Agent container vs. cloud-hosted) is not specified, affecting the risk of container escape or lateral network movement.
Not certain from the listing — there is no mention of built-in guardrails or evaluation frameworks to validate that the generated SLO configurations are safe and accurate before deployment.
Requires write access to the user's Elastic Observability deployment. Lack of fine-grained authorization could allow the agent to modify critical system configurations beyond SLOs.
As an Elastic Agent Skill, it operates within the broader Elastic ecosystem. Threats include cascading failures if a compromised orchestrator agent abuses this skill to manipulate SLOs and mask ongoing attacks.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).