recognizing-deception

recognizing-deception is a skill for Claude Code from trilwu/secskills. It costs 107 tokens per session (2,337 once invoked), scanned A, original, MIT.

A guide to spotting defensive deception such as honeypots, honeytokens, canary files, decoy accounts, and fake cloud credentials. These are deliberately planted resources designed to reveal or mislead an intruder.

In plain words
What is it for?
Use it during security engagements when a service, credential, account, or exposed file seems unusually convenient or behaves strangely.
Why use it?
It helps prevent a tester from treating fabricated evidence as a real weakness or triggering an alert by interacting with a decoy.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the secskills-offense plugin — 40 skills shipped together

Install

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.

agentmods
npx agentmods add skills/trilwu/secskills/recognizing-deception
Any agent
npx skills add trilwu/secskills --skill recognizing-deception
Clone the repo
git clone --depth 1 https://github.com/trilwu/secskills

Made for: Claude Code.

Or install secskills-offense, the plugin that ships this one along with the rest of its 40 skills.

Wrote 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.

agentmods badge for recognizing-deception

README.md
[![agentmods](https://agentmods.dev/badge/skills/trilwu/secskills/recognizing-deception.svg)](https://agentmods.dev/skills/trilwu/secskills/recognizing-deception)
Your own site
<a href="https://agentmods.dev/skills/trilwu/secskills/recognizing-deception"><img src="https://agentmods.dev/badge/skills/trilwu/secskills/recognizing-deception.svg" alt="Measured on agentmods" height="20"></a>
Per session 107 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,337 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 1 finding. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00107 $0.02337
Opus 5 $0.00053 $0.01169
Sonnet 5 $0.00021 $0.00467
Haiku 4.5 $0.00011 $0.00234

Measured 2d ago against content hash c180d43c53cd, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-06, from the pricing page.

Security

Grade A, and why

recognizing-deception scanned grade A with 1 finding 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 2d 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.

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

curl -sL "https://defuddle.md/<url>" # scheme in the path is optional
secskills-offense/skills/recognizing-deception/SKILL.md · 211 lines

How it starts

The opening of the file, as written. The whole thing — 211 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Recognizing Deception

Every other skill in this collection assumes the environment is telling you the truth. Deception technology exists specifically to break that assumption, and it is the failure mode an automated or semi-automated tester is least equipped to catch.

The distinction that matters: careful evidence-handling protects you against conclusions you invented. It does nothing against a false belief the environment deliberately planted. A honeypot presenting a convincingly vulnerable service produces real banners, real responses, and real artifacts. Every verification step you would normally run confirms it, because the evidence is genuine — it was manufactured to be.

Assume competent defenders have planted something. Your job is to notice before you touch it, because most deception fires on first use, and first use cannot be undone.

When to Use

  • Anything is markedly easier than the rest of the environment
  • Credentials turn up somewhere convenient — a share, a wiki, a config, a pastebin-shaped file
  • A privileged account exists with a SPN, a weak password, and no logon history
  • A service answers with a vulnerable banner but behaves oddly under real use
  • You are about to use credentials whose provenance you cannot state
  • A file, bucket, or database is named to attract attention (passwords.xlsx, backup-prod, domain_admins.txt)
  • Before authenticating with anything recovered from an unexpected location

When NOT to Use

  • Evading detection generally — that is engagement OPSEC, not deception recognition; a canary is not something you evade, it is something you avoid triggering
  • Analysing an adversary's own decoys during an incident — use responding-to-incidents and producing-threat-intelligence
  • Building a deception capability — this skill is about encountering deception, not deploying it
  • A finding that is merely surprising — real environments contain real misconfigurations; see the base-rate discussion below before crying honeypot

Read the full file on GitHub · 211 lines

Changes

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.

  1. 2d ago First seen · 211 lines · 107 tokens per session scan A c180d43c53cd

Subscribe to this mod's changes

recognizing-deception is a skill published in the GitHub repository trilwu/secskills (134 stars, last pushed yesterday), licensed MIT. It adds 107 tokens to every session and 2,337 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.

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