Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add andycungkrinx91/konoha --skill deploying-cloud-deception-with-decoy-resourcesgit clone --depth 1 https://github.com/andycungkrinx91/konohaWrote 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/andycungkrinx91/konoha/deploying-cloud-deception-with-decoy-resources)<a href="https://agentmods.dev/skills/andycungkrinx91/konoha/deploying-cloud-deception-with-decoy-resources"><img src="https://agentmods.dev/badge/skills/andycungkrinx91/konoha/deploying-cloud-deception-with-decoy-resources/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/andycungkrinx91/konoha/deploying-cloud-deception-with-decoy-resources"><img src="https://agentmods.dev/badge/skills/andycungkrinx91/konoha/deploying-cloud-deception-with-decoy-resources.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00224 | $0.02627 |
| Opus 5.5 | $0.00090 | $0.01051 |
| Sonnet 5.5 | $0.00045 | $0.00525 |
| Haiku 4.5 | $0.00022 | $0.00263 |
Grade A, and why
deploying-cloud-deception-with-decoy-resources 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 today.
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.
How it starts
The opening of the file, as written. The whole thing — 170 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Deploying Cloud Deception with Decoy Resources
When to Use
- When cloud accounts (AWS/Azure/GCP) hold crown-jewel data or infrastructure and you need a tripwire that fires the moment an attacker who has gained access starts to operate.
- When the only deception in place is on-prem honeypots, leaving the cloud control plane uninstrumented.
- When seeding fake credentials to catch credential theft, accidental code-repo leaks, or secrets exposed in build pipelines.
- When detecting cloud reconnaissance (enumeration of IAM, storage, or secrets) and lateral movement that legitimate users would never perform.
- When you want detections that survive into incident response with strong fidelity — a touch on a decoy resource almost always means malicious or unauthorized activity.
This is the cloud counterpart to on-prem honeypot/honeytoken/canary-token deployment skills. For program strategy and how these Activities map to adversary engagement goals, use designing-adversary-engagement-with-mitre-engage.
Prerequisites
- Cloud admin/IAM permissions to create decoy principals, storage, secrets, and detection wiring, ideally in a dedicated deployment role with least privilege.
- Cloud audit logging already enabled: AWS CloudTrail (multi-region, with management and relevant data events), Azure Activity log + Microsoft Entra audit/sign-in logs, GCP Cloud Audit Logs (Admin Activity always on; Data Access enabled where needed).
- A SIEM/alert sink: SNS topic, Microsoft Sentinel workspace, or GCP Pub/Sub + Monitoring, with routing to the SOC.
- A naming and tagging convention that is plausible to an attacker but unambiguous to defenders internally (e.g., realistic names, plus an internal
deception=truetag/label kept out of attacker-visible metadata). - Decoy principals must be permission-less (explicit deny-all). The value is the alert, never the access. A decoy that grants real privilege is a liability, not a control.
Workflow
1. Decide what to mimic
Pick decoys that match how your attackers operate: leaked AWS keys (credential theft), an "admin" S3 bucket (data discovery), a prod-db-password secret (secrets harvesting), a privileged-looking service account (cloud lateral movement). Place credential decoys where harvesting tools look: env files, CI variables, code comments, an internal wiki.
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- today First seen · 170 lines · 224 tokens per session scan A 38a696a5b368
deploying-cloud-deception-with-decoy-resources is a skill published in the GitHub repository andycungkrinx91/konoha (12 stars, last pushed today), licensed MIT. It adds 224 tokens to every session and 2,627 once invoked, about $0.0009 per session on Opus 5.5. 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 2026-10-25.
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