Load test a Databricks App to find its maximum QPS. Use when: (1) User says 'load test', 'benchmark', 'QPS', 'throughput', or 'performance test', (2) User wants to find how many queries per second their app can handle, (3) User wants to set up load testing scripts for their agent, (4) User wants to view load test results/dashboard, (5) User wants to validate results or observe latency/error rate with MLflow tracing.
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High
Do not use without reviewing
Security
1 high severity finding. You should review these findings carefully before considering using this skill.
The skill handles credentials insecurely by requiring the agent to include secret values verbatim in its generated output. This exposes credentials in the agent’s context and conversation history, creating a risk of data exfiltration.
The skill instructs the agent to collect/use M2M OAuth credentials (client_id/client_secret) and shows examples embedding them in CLI flags and environment variable exports, which requires the LLM to handle/output secret values verbatim.
Low
Low-risk findings.
1 low severity finding. Worth noting, but not necessarily harmful.
The skill exposes the agent to untrusted, user-generated content from public third-party sources, creating a risk of indirect prompt injection. This includes browsing arbitrary URLs, reading social media posts or forum comments, and analyzing content from unknown websites.
Runtime path `examples/locustfile.py` sends user-authored free text prompts in `body = {"input": [{"role": "user", "content": random.choice(PROMPTS)}], "stream": True}` and the agent server reads that `content` from the `/invocations` request payload; thus the workflow exposes the deployed agent to outsider-supplied prompt text.
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