Individual skill
recognizing-deception
Recognize defensive deception during an engagement — honeypots, honeytokens and canary tokens, decoy AD accounts and shares, canary files, and deceptive cloud credentials — before interacting with them, and handle a suspected decoy without burning the engagement. Use when a target is unexpectedly easy, when credentials or a service appear in an implausible place, when a privileged account has no logon history, when a file or bucket looks like bait, or when deciding whether to use credentials of unknown provenance.
secskills-offense/skills/recognizing-deception/SKILL.md
Source commit ca53957bcd8e3b83b5d4d190bb3ad008a4f15c84
What was checked
- Matches the observed repository HEAD
- Yes
- Scanner rules are current
- No
- Repository scan complete
- No
- Skill scan complete
- No
- Observed behavior
- Not checked yet
- Last checked
- Nov 11, 2026
These are observations at the recorded commit, not a guarantee of safety. Review the source before running it.
Install this skill
Choose an agent in your Stack or copy a command below. Commands fetch the pinned commit and copy only this skill directory. Existing destinations are refused. Copying a command does not install anything.
An install command is unavailable: current rules, complete repository and skill scans, an eligible behavior level and a permitted license are required.
Repository context
- Repository stars (not skill ratings)
- 189
- Repository last updated
- Sep 4, 2026
- License
- MIT
Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.
- security Repository topic
- skills Repository topic
- engineering Derived category
Files and excerpt
File metadata and permitted, redacted excerpts come from the recorded scan. Source links are pinned to that commit.
Browse files
Read excerpt
# 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.