Agent skill

Performing Deception Technology Deployment

by mukul975 in mukul975/Anthropic-Cybersecurity-Skills

Deploys deception technology including honeypots, honeytokens, and decoy systems to detect attackers who have bypassed perimeter defenses, providing high-fidelity alerts with near-zero false…

Apache-2.0Auto-check: warningsSecurity

Install Performing Deception Technology Deployment

The automated check flagged lines worth reading first. See the safety section below.

skills CLI
$ npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill performing-deception-technology-deployment -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills performing-deception-technology-deployment --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/performing-deception-technology-deployment .claude/skills/performing-deception-technology-deployment && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
performing-deception-technology-deployment
GitHub stars
34k
Token cost
~2.8k tokens
SKILL.md length
530 words
Files
4 (incl. scripts, references)
Skills in repo
644
Repo updated
First seen
Licence
Apache-2.0

At a glance

Deploys deception technology including honeypots, honeytokens, and decoy systems to detect attackers who have bypassed perimeter defenses, providing high-fidelity alerts with near-zero false…

  • Works in 6 steps: Map Attack Surface for Deception Placement → Deploy Thinkst Canary Devices → Deploy Honeytokens in Active Directory → …
  • SOC teams need early warning of lateral movement
  • SKILL.md covers When to Use, Prerequisites, Workflow and Key Concepts, plus 3 more sections
  • Runs Python scripts from its folder

What it does

Performing Deception Technology Deployment is an agent skill from mukul975/Anthropic-Cybersecurity-Skills. Deploys deception technology including honeypots, honeytokens, and decoy systems to detect attackers who have bypassed perimeter defenses, providing high-fidelity alerts with near-zero false positive rates. Use when SOC teams need early warning of lateral movement, credential abuse, or internal reconnaissance by deploying convincing traps across the network.

Its SKILL.md is about 2.8k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files, including scripts and reference files (for example `references/api-reference.md` and `scripts/agent.py`).

It sits in Security, covering Deployment, Red teaming and adversary simulation and Security operations. The repository describes itself as: 817 structured cybersecurity skills for AI agents · Mapped to 6 frameworks: MITRE ATT&CK, NIST CSF 2.0, MITRE ATLAS, D3FEND, NIST AI RMF & MITRE F3 (Fight Fraud) · agentskills.io…. The licence is Apache-2.0.

When your agent uses it

  • SOC teams need early warning of lateral movement
  • Credential abuse
  • Internal reconnaissance by deploying convincing traps across the network

Example prompts

  • “Use the performing-deception-technology-deployment skill to deploy deception technology including honeypots, honeytokens, and decoy systems to…”
  • “/performing-deception-technology-deployment”

Requirements

  • Python 3
  • Docker
  • A credential in YOUR_API_TOKEN

Workflow steps

6 steps, taken from the step headings in SKILL.md.

  1. Map Attack Surface for Deception Placement
  2. Deploy Thinkst Canary Devices
  3. Deploy Honeytokens in Active Directory
  4. Deploy Canary Files and Documents
  5. Integrate Deception Alerts with SIEM/SOAR
  6. Maintain Deception Realism

What it can do on your machine

Read from SKILL.md and the folder at commit 54a7988. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 1 file in scripts/ (Python), which the agent can run.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Performing Deception Technology Deployment loads about 2.8k tokens when it runs, and up to ~3.3k if it reads all its reference files. Until then it costs about 101 tokens; SKILL.md has 530 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~101
When it runs · the whole SKILL.md, loaded when a task matches
~2.8k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~3.3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check: warnings

The automated check found patterns that need a careful read before installing.

  • WarningMentions a credentials file (SSH keys, cloud or package-manager tokens)SKILL.md:195
    o": "Canary AWS key in developer laptop .aws/credentials"
  • WarningMentions a credentials file (SSH keys, cloud or package-manager tokens)SKILL.md:201
    # Plant in .aws/credentials on developer workstations

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from mukul975/Anthropic-Cybersecurity-Skills at commit 54a7988, republished under its Apache-2.0 licence (© mukul975). 530 words, ~2,801 tokens.

Download SKILL.mdSave it as .claude/skills/performing-deception-technology-deployment/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
performing-deception-technology-deployment
description
Deploys deception technology including honeypots, honeytokens, and decoy systems to detect attackers who have bypassed perimeter defenses, providing high-fidelity alerts with near-zero false positive rates. Use when SOC teams need early warning of lateral movement, credential abuse, or internal reconnaissance by deploying convincing traps across the network.
domain
cybersecurity
subdomain
soc-operations
tags
soc, deception, honeypot, honeytoken, canary, lateral-movement, detection
version
1.0
author
mahipal
license
Apache-2.0
nist_csf
DE.CM-01, DE.AE-02, RS.MA-01, DE.AE-06
mitre_attack
T1078, T1685.002, T1685.005, T1566, T1021

Performing Deception Technology Deployment

When to Use

Use this skill when:

  • SOC teams need high-fidelity detection of post-compromise lateral movement with near-zero false positives
  • Existing detection tools miss advanced attackers who avoid triggering threshold-based alerts
  • The organization wants to detect credential abuse by planting fake credentials as honeytokens
  • Network segmentation gaps need compensating detection controls

Do not use as a replacement for fundamental security controls (patching, EDR, network segmentation) — deception is a detection layer, not a prevention mechanism.

Prerequisites

  • Network segments identified for honeypot/decoy deployment (server VLANs, DMZ, OT networks)
  • Deception platform (Thinkst Canary, Attivo/SentinelOne Hologram, or open-source alternatives)
  • SIEM integration for deception alerts (any interaction with deception assets is suspicious)
  • Active Directory access for honeytoken account and credential creation
  • Network team coordination for IP allocation and traffic routing

Workflow

Step 1: Map Attack Surface for Deception Placement

Identify high-value network segments where attackers would traverse:

DECEPTION DEPLOYMENT MAP
━━━━━━━━━━━━━━━━━━━━━━━━
Segment              Decoy Type          Rationale
Server VLAN          Fake file server    Attackers enumerate SMB shares during recon
Database VLAN        Fake DB server      SQL scanning detected in past incidents
AD/DC Segment        Honeytoken account  Credential theft detection
Executive Subnet     Fake workstation    Targeted attacks pivot through exec systems
DMZ                  Honeypot web app    External attacker detection
OT Network           Fake PLC/HMI        Industrial threat detection
Cloud (AWS VPC)      Canary EC2 + S3     Cloud lateral movement detection
Step 2: Deploy Thinkst Canary Devices

Configure Canary devices mimicking real infrastructure:

Windows File Server Canary:

json
{
  "device_name": "FILESERVER-BK04",
  "personality": "windows-server-2019",
  "services": {
    "smb": {
      "enabled": true,
      "shares": ["Finance_Backup", "HR_Archive", "IT_Docs"],
      "files": [
        {"name": "Q4_Revenue_2024.xlsx", "alert_on": "read"},
        {"name": "employee_ssn_export.csv", "alert_on": "read"},
        {"name": "admin_passwords.kdbx", "alert_on": "read"}
      ]
    },
    "rdp": {"enabled": true},
    "http": {"enabled": false}
  },
  "network": {
    "ip": "10.0.5.200",
    "hostname": "FILESERVER-BK04",
    "domain": "company.local"
  },
  "alert_webhook": "https://soar.company.com/api/webhook/canary"
}

Database Server Canary:

json
{
  "device_name": "DB-ARCHIVE-02",
  "personality": "linux-mysql",
  "services": {
    "mysql": {
      "enabled": true,
      "port": 3306,
      "databases": ["customer_pii", "payment_archive"],
      "alert_on_login_attempt": true
    },
    "ssh": {
      "enabled": true,
      "port": 22,
      "alert_on_login_attempt": true
    }
  },
  "network": {
    "ip": "10.0.10.50",
    "hostname": "db-archive-02"
  }
}
Step 3: Deploy Honeytokens in Active Directory

Create fake privileged accounts that should never be used:

powershell
# Create honeytoken service account
New-ADUser -Name "svc_sql_backup" `
    -SamAccountName "svc_sql_backup" `
    -UserPrincipalName "svc_sql_backup@company.local" `
    -Description "SQL Backup Service Account - DO NOT DELETE" `
    -AccountPassword (ConvertTo-SecureString "FakeP@ssw0rd2024!" -AsPlainText -Force) `
    -Enabled $true `
    -PasswordNeverExpires $true `
    -CannotChangePassword $true

# Add to a group that looks attractive (but monitor for any use)
Add-ADGroupMember -Identity "Domain Admins" -Members "svc_sql_backup"

# Place cached credentials on decoy workstation
# (Mimikatz/credential dumping will find these)
cmdkey /add:fileserver-bk04.company.local /user:company\svc_sql_backup /pass:FakeP@ssw0rd2024!

Monitor honeytoken usage in Splunk:

spl
index=wineventlog sourcetype="WinEventLog:Security"
(EventCode=4624 OR EventCode=4625 OR EventCode=4648 OR EventCode=4768 OR EventCode=4769)
TargetUserName="svc_sql_backup"
| eval alert_severity = "CRITICAL"
| eval alert_message = "HONEYTOKEN ACCOUNT USED — Likely credential theft detected"
| table _time, EventCode, src_ip, ComputerName, TargetUserName, Logon_Type, alert_message
Step 4: Deploy Canary Files and Documents

Plant tracked documents that beacon when opened:

Canary Document (Word doc with tracking):

python
# Using Thinkst Canary API to create a canary token document
import requests

response = requests.post(
    "https://YOURCOMPANY.canary.tools/api/v1/canarytoken/create",
    data={
        "auth_token": "YOUR_API_TOKEN",
        "kind": "doc-msword",
        "memo": "Finance backup folder canary document",
        "flock_id": "flock:default"
    }
)
token = response.json()
download_url = token["canarytoken"]["canarytoken_url"]
print(f"Download canary doc: {download_url}")
# Place this document in honeypot SMB shares and sensitive directories

AWS Canary Token (S3 access key):

python
# Create AWS canary token — alerts when access key is used
response = requests.post(
    "https://YOURCOMPANY.canary.tools/api/v1/canarytoken/create",
    data={
        "auth_token": "YOUR_API_TOKEN",
        "kind": "aws-id",
        "memo": "Canary AWS key in developer laptop .aws/credentials"
    }
)
aws_keys = response.json()
print(f"Access Key: {aws_keys['canarytoken']['access_key_id']}")
print(f"Secret Key: {aws_keys['canarytoken']['secret_access_key']}")
# Plant in .aws/credentials on developer workstations
Step 5: Integrate Deception Alerts with SIEM/SOAR

All deception alerts are high-fidelity — any interaction is suspicious:

Splunk Alert for Canary Triggers:

spl
index=canary sourcetype="canary:alerts"
| eval severity = "CRITICAL"
| eval confidence = "HIGH — Deception asset triggered, zero false positive expected"
| table _time, canary_name, alert_type, source_ip, service, details
| sendalert create_notable param.rule_title="Deception Alert — Canary Triggered"
  param.severity="critical" param.drilldown_search="index=canary source_ip=$source_ip$"

SOAR Automated Response:

python
def canary_triggered(container):
    """Auto-response for deception alerts — high confidence, no approval needed"""
    source_ip = container["artifacts"][0]["cef"]["sourceAddress"]

    # Immediately isolate the source
    phantom.act("quarantine device",
                parameters=[{"ip_hostname": source_ip}],
                assets=["crowdstrike_prod"],
                name="isolate_attacker_host")

    # Block at firewall
    phantom.act("block ip",
                parameters=[{"ip": source_ip, "direction": "both"}],
                assets=["palo_alto_prod"],
                name="block_attacker_ip")

    # Create high-priority incident
    phantom.act("create ticket",
                parameters=[{
                    "short_description": f"DECEPTION ALERT: Canary triggered from {source_ip}",
                    "urgency": "1",
                    "impact": "1"
                }],
                assets=["servicenow_prod"])

    phantom.set_severity(container, "critical")
Step 6: Maintain Deception Realism

Regularly update decoys to maintain believability:

  • Rotate honeytoken passwords quarterly (update cached credentials on decoy workstations)
  • Update canary file modification dates to appear recently accessed
  • Add realistic network traffic to honeypots (scheduled SMB enumeration, DNS lookups)
  • Register honeypot hostnames in DNS and Active Directory to appear in network scans
  • Update canary document contents to match current business context
Show full SKILL.md (231 more words)Show less

Key Concepts

TermDefinition
HoneypotDecoy system mimicking real infrastructure to attract and detect attackers in the network
HoneytokenFake credential, file, or data record that triggers an alert when accessed or used
CanaryLightweight deception device or token that alerts on any interaction (Thinkst Canary platform)
BreadcrumbPlanted artifact (cached credential, bookmark, config file) leading attackers to deception assets
High-Fidelity AlertDetection signal with near-zero false positive rate because no legitimate user should interact with deception assets
Decoy NetworkSet of interconnected honeypots simulating a realistic network segment to observe attacker TTPs

Tools & Systems

  • Thinkst Canary: Commercial deception platform offering hardware/virtual canaries and canary tokens
  • Canarytokens.org: Free honeytoken generation service (DNS, HTTP, AWS keys, Word docs, SQL queries)
  • Attivo Networks (SentinelOne): Enterprise deception platform with AD decoys and endpoint breadcrumbs
  • HoneyDB: Community honeypot data aggregation platform for threat intelligence sharing
  • T-Pot: Open-source multi-honeypot platform combining 20+ honeypot types in a Docker deployment

Common Scenarios

  • Lateral Movement Detection: Attacker enumerates SMB shares and accesses honeypot file server — immediate high-fidelity alert
  • Credential Theft Discovery: Mimikatz dumps honeytoken cached credentials — usage of fake account triggers alert
  • Cloud Key Compromise: Stolen AWS canary token used from external IP — detects supply chain or insider compromise
  • Ransomware Early Warning: Ransomware encrypts canary files on honeypot shares — early detection before production systems affected
  • Insider Threat Signal: Employee accesses honeypot "salary database" — indicates unauthorized data exploration

Output Format

DECEPTION ALERT — CRITICAL
━━━━━━━━━━━━━━━━━━━━━━━━━━
Time:         2024-03-15 14:23:07 UTC
Canary:       FILESERVER-BK04 (10.0.5.200)
Service:      SMB — File share "Finance_Backup" accessed
Source:       192.168.1.105 (WORKSTATION-042, Finance Dept)
User:         company\jsmith
File Accessed: Q4_Revenue_2024.xlsx (canary document)

Alert Confidence: HIGH — No legitimate reason to access deception asset
False Positive Likelihood: <1%

Automated Response:
  [DONE] WORKSTATION-042 isolated via CrowdStrike
  [DONE] 192.168.1.105 blocked at firewall (bidirectional)
  [DONE] Incident INC0012567 created (P1 — Critical)
  [PENDING] Tier 2 investigation — determine if workstation compromised or insider threat

© mukul975, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 3 other files (scripts, references) in skills/performing-deception-technology-deployment of mukul975/Anthropic-Cybersecurity-Skills.

  • SKILL.md
  • LICENSE
  • references/api-reference.md
  • scripts/agent.py

Open the folder on GitHubat commit 54a7988

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Rational Red Blue Debatedigoal/blog8.6k—~2.2kAutomated safety check: PassGPL-2.0
Threat Huntinghypnguyen1209/offensive-claude388—~2.4kAutomated safety check: PassMIT

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Categories

Questions about Performing Deception Technology Deployment

What does Performing Deception Technology Deployment do?

Deploys deception technology including honeypots, honeytokens, and decoy systems to detect attackers who have bypassed perimeter defenses, providing high-fidelity alerts with near-zero false…. Performing Deception Technology Deployment is an agent skill from mukul975/Anthropic-Cybersecurity-Skills. Deploys deception technology including honeypots, honeytokens, and decoy systems to detect attackers who have bypassed perimeter defenses, providing high-fidelity alerts with near-zero false positive rates.

When should I use Performing Deception Technology Deployment?

Performing Deception Technology Deployment fits situations like: SOC teams need early warning of lateral movement; credential abuse; internal reconnaissance by deploying convincing traps across the network.

How do I install Performing Deception Technology Deployment in Claude Code?

Run `npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill performing-deception-technology-deployment -a claude-code`. Or copy the skill folder (skills/performing-deception-technology-deployment in mukul975/Anthropic-Cybersecurity-Skills) into .claude/skills/performing-deception-technology-deployment in your project. Claude Code loads it when a task matches its description.

How do I install Performing Deception Technology Deployment in Codex?

Run `npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill performing-deception-technology-deployment -a codex`. Or copy the skill folder (skills/performing-deception-technology-deployment in mukul975/Anthropic-Cybersecurity-Skills) into .agents/skills/performing-deception-technology-deployment in your project. Codex loads it when a task matches its description.

Can I use Performing Deception Technology Deployment in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill performing-deception-technology-deployment -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/performing-deception-technology-deployment, .gemini/skills/performing-deception-technology-deployment, .github/skills/performing-deception-technology-deployment and .opencode/skills/performing-deception-technology-deployment in your project.

What does Performing Deception Technology Deployment need to run?

Going by SKILL.md and its folder, Performing Deception Technology Deployment needs Python for the scripts in its folder. Our summary lists: Python 3; Docker; A credential in YOUR_API_TOKEN.

Does Performing Deception Technology Deployment access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Performing Deception Technology Deployment safe to install?

Our automated static check of SKILL.md flagged 2 warning(s): mentions a credentials file (ssh keys, cloud or package-manager tokens). Read the flagged lines before installing; the check is not a guarantee either way. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Performing Deception Technology Deployment use?

Performing Deception Technology Deployment is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Performing Deception Technology Deployment use?

About 2.8k tokens (SKILL.md is roughly 11k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 540 tokens, read only when the agent opens those files.

What are the alternatives to Performing Deception Technology Deployment?

Skills that share tags, products or a category with Performing Deception Technology Deployment: Agent Red Teaming (seb1n/awesome-ai-agent-skills, 206 stars), Council (warpdotdev/common-skills, 611 stars), Cybersecurity (ohmyjahh/xquads-squads, 277 stars) and Rational Red Blue Debate (digoal/blog, 8.6k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Performing Deception Technology Deployment?

mukul975 (a GitHub user) maintains it in mukul975/Anthropic-Cybersecurity-Skills, which has 33,993 GitHub stars. The repository holds 644 skills in this directory. The repository was last updated on August 31, 2026.

Source: mukul975/Anthropic-Cybersecurity-Skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.