Borrowing it
Nothing to install: this file belongs to gke-labs/kube-agents. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/gke-labs/kube-agents/main/.agents/skills/review-security-k8s-agents-data-exfil/SKILL.mdgit clone --depth 1 https://github.com/gke-labs/kube-agentsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/gke-labs/kube-agents/review-security-k8s-agents-data-exfil)<a href="https://agentmods.dev/skills/gke-labs/kube-agents/review-security-k8s-agents-data-exfil"><img src="https://agentmods.dev/badge/skills/gke-labs/kube-agents/review-security-k8s-agents-data-exfil/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/gke-labs/kube-agents/review-security-k8s-agents-data-exfil"><img src="https://agentmods.dev/badge/skills/gke-labs/kube-agents/review-security-k8s-agents-data-exfil.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00024 | $0.00218 |
| Opus 5 | $0.00012 | $0.00109 |
| Sonnet 5 | $0.00005 | $0.00044 |
| Haiku 4.5 | $0.00002 | $0.00022 |
Grade A, and why
review-security-k8s-agents-data-exfil scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 13d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
Task
Review infrastructure for AI agent data exfiltration risks (e.g. via prompt injection).
Checks
1. Strict Egress Allowlisting
- NetworkPolicies: Require restrictive egress. Flag missing egress controls or
0.0.0.0/0. - Allowlisting: Limit egress strictly to necessary internal services and authorized LLM APIs.
2. Egress Gateways
- Interception: Require outbound traffic routing through transparent proxies/Egress Gateways (e.g., Service Mesh,
HTTP_PROXY) for Deep Packet Inspection and SNI allowlisting.
3. Data Access
- Blanket Permissions: Flag broad
get/list/watchRBAC on sensitive resources (secrets,configmaps). - Over-privileged Mounts: Flag broad or shared sensitive volume mounts.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 13d ago First seen · 19 lines · 24 tokens per session scan A 9dcfbb85094e
review-security-k8s-agents-data-exfil is a skill published in the GitHub repository gke-labs/kube-agents (54 stars, last pushed today), licensed Apache-2.0. It adds 24 tokens to every session and 218 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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