implementing-deception-based-detection-with-canarytoken
Teaches your AI agent to find and fix bugs and ship projects and manage infrastructure.
What it does
This tool creates 'false alarms' called Canary Tokens. They are like digital traps that trigger an alert if an attacker tries to access them, helping to detect attacks early.
When to use it
- ✓When you want to set up traps to detect intruders before they cause damage.
- ✓When you need to show how your security defenses are covering different areas.
- ✓When you are setting up or improving your network security.
Example of what to ask your AI
“Create a Word document token to detect if someone tries to open it on a network share.”
Want more? Open the sections below.
How to use
The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.
Skill link
Ready-to-paste message
How it works
The tool connects to your Thinkst Canary account and creates different types of traps (links, files, etc.). It also monitors if anyone triggers these traps and generates a report on what was detected.
Installation
Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.
In the terminal (Claude Code, Codex, Cursor), in your project folder:
npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill implementing-deception-based-detection-with-canarytokenOr download just the file:
curl -L https://raw.githubusercontent.com/mukul975/Anthropic-Cybersecurity-Skills/main/skills/implementing-deception-based-detection-with-canarytoken/SKILL.md -o SKILL.mdTechnical content (original SKILL.md)
Original text by the author, straight from GitHub.
Implementing Deception-Based Detection with Canarytoken
Overview
Canary Tokens are lightweight tripwire mechanisms that alert when an attacker accesses a resource. This skill uses the Thinkst Canary REST API to programmatically create tokens (web bugs, DNS tokens, MS Word documents, AWS API keys), deploy them to strategic locations, monitor for triggered alerts, and generate deception coverage reports.
When to Use
- When deploying or configuring implementing deception based detection with canarytoken capabilities in your environment
- When establishing security controls aligned to compliance requirements
- When building or improving security architecture for this domain
- When conducting security assessments that require this implementation
Prerequisites
- Thinkst Canary Console or canarytokens.org account
- API auth token from Canary Console
- Python 3.9+ with
requests - File system access for deploying document and file tokens
Steps
- Authenticate to the Canary Console API using auth_token
- Create web bug (HTTP) tokens for embedding in documents and web pages
- Create DNS tokens for monitoring DNS resolution attempts
- Create MS Word document tokens for file share deployment
- List all active tokens and their trigger history
- Query recent alerts for triggered token events
- Generate deception coverage report with deployment recommendations
Expected Output
- JSON report listing all deployed Canary Tokens, trigger history, alert details, and coverage analysis
- Deployment map showing token types across network segments
FAQ
What are Canary Tokens?
They are alert mechanisms that trigger a warning when a protected resource is accessed by an unauthorized person.
Do I need a Thinkst Canary account?
Yes, you need an account with Thinkst Canary Console or canarytokens.org and an API authentication token.
What does the final report show?
The report lists all created tokens, when they were triggered, alert details, and recommendations on where else to place traps.
Details
Original description by the author
Deploys and monitors Canary Tokens via the Thinkst Canary REST API for deception-based breach detection, programmatically creating web bug, DNS, MS Word document, and AWS API key tokens and generating deception coverage reports from triggered alerts. Use when standing up honeytoken tripwires for early breach detection or building a deception-technology coverage report.
Popularity
- GitHub stars
- 35k
- Ranking position
- #5861
- License
- Apache-2.0
- Updated
- 8/31/2026
Comments
- …
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