Elastic kibana-detection (security alert-triage) — agentic threat model

Not certain from the listing — the underlying foundation model is not specified. The primary L1 risk is indirect prompt injection, where malicious payloads embedded within ingested security alerts or log data reprogram the model to misclassify, deprioritize, or ignore active threats.

The agent reads detection and alert data directly from the user's Elastic deployment. This introduces risks of data poisoning (adversaries crafting specific log events to skew correlation logic) and unauthorized data exposure if the agent's context window leaks sensitive alert details.

The agent encodes triage workflows over the Security app, implying tool integration to pull alerts and correlate signals. Vulnerabilities in the orchestration framework could allow an attacker to manipulate the tool-calling parameters or bypass triage logic entirely.

Not certain from the listing — the hosting environment (Elastic Cloud vs. self-hosted Kibana) is unspecified. The agent requires network access to the Elastic deployment and API keys/tokens to read alert data, which must be securely stored and sandboxed to prevent credential theft.

Not certain from the listing — there are no details on how the agent's triage decisions are logged, audited, or evaluated for drift. A lack of observability could allow silent failures or adversarial manipulation of priority scores to go unnoticed by human analysts.

The agent must adhere to strict Role-Based Access Control (RBAC) within the Elastic Security app. If the agent's identity is over-privileged, it could expose sensitive index data beyond what the active human analyst is authorized to view.

Not certain from the listing — while described as an 'Elastic Agent Skill', it is unclear if it interacts with other specialized security agents. If multi-agent workflows are supported, cascading trust failures could occur if a compromised agent feeds falsified triage data to this agent.