analyzing-expensive-users
Analyze the most expensive users in AI observability and explain why they cost so much. Use when the user asks about top spenders, expensive users, per-user LLM cost, user-level cost drivers, or patterns behind high AI observability spend.
80
100%
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Low
Low-risk findings worth noting
Low
Low-risk findings.
1 low severity finding. Worth noting, but not necessarily harmful.
W011: Third-party content exposure detected (indirect prompt injection risk).
What this means
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.
Why it was flagged
SKILL.md’s required workflow calls PostHog trace-reading tools (e.g., `posthog:query-llm-trace` and related SQL queries) that return representative trace content/events for specific users; those values can include outsider-authored free text (e.g., user prompts or other messages) and the agent would ingest them into the LLM context to “read representative traces.”
- Repository
- PostHog/posthog
- Commit
047ca00
- Audited
- Security analysis
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