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CC deploying-active-directory-honeytokens

Deploys deception-based honeytokens in Active Directory including fake privileged accounts with AdminCount=1, fake SPNs for Kerberoasting detection (honeyroasting), decoy GPOs with cpassword traps, and fake BloodHound paths. Monitors Windows Security Event IDs 4769, 4625, 4662, 5136 for honeytoken interaction. Use when implementing AD deception defenses for detecting lateral movement, credential theft, and reconnaissance.

mukul975/Anthropic-Cybersecurity-Skills Agent Skills author: mukul975 Apache-2.0 5 files · 2 scripts body ≈ 2 206 tokens Open the sourcegithub.com↗ analyzed 5 d ago

Deploys deception-based honeytokens in Active Directory including fake privileged accounts with AdminCount=1, fake SPNs for Kerberoasting detection…

As a process C 53/100 · Has gaps — weak spots: result and completion, when it triggers, failures and branches

ProcedureInfrastructuretype and topics are labelled automatically from the skill text
Runs in: Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) OpenClaw Goose OpenCode
JSON
Technical rating
C
86/100
safety, quality, tests
Safety 60%
93
Quality 40%
75
Run on models
none yet
Process rating
C
53/100
Has gaps
Result and completion w 14
0
Failures and branches w 10
0
When it triggers w 12
20
the three weakest of ten parameters · all ten

How to improve

    For the model run — optional
    • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
    • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

    Guard findings · 7

    ✓ No critical or high findings

    Medium and low: 7
    • low Secrets in code secret-high-entropy-token scripts/agent.py:270
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      <Groups clsid="{{3125…D26}}">
      quoted
    • low Secrets in code secret-high-entropy-token scripts/agent.py:271
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      <User clsid="{{DF5F…1D1}}"
      quoted
    • low Secrets in code secret-high-entropy-token scripts/Deploy-ADHoneytokens.ps1:323
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      <Groups clsid="{3125…D26}">
      quoted
    • low Secrets in code secret-high-entropy-token scripts/Deploy-ADHoneytokens.ps1:324
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      <User clsid="{DF5F…1D1}"
      quoted
    • low Risky intent intent-offensive-security SKILL.md:7
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use) (detector / deny-list definition)
      AD deception defenses for detecting lateral movement, credential theft, and reconnaissance.
      detector
    • low Risky intent intent-offensive-security SKILL.md:41
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use) (detector / deny-list definition)
      - When creating tripwire accounts to detect credential theft and lateral movement
      detector
    • low Risky intent intent-offensive-security SKILL.md:63
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use) (detector / deny-list definition)
      - **Fake privileged accounts** detect credential dumping (DCSync, NTDS.dit extraction)
      detector

    Files scanned: 5. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "domain"
    • note frontmatter-key unknown frontmatter key "subdomain"
    • note frontmatter-key unknown frontmatter key "nist_csf"
    • note frontmatter-key unknown frontmatter key "mitre_attack"
    • note edit-residue the text marks something as outdated (lines 55, 67, 75, 76, 77, 93): check that old rules are not kept next to new ones — the full check reads the text for contradictions

    Process rating: all ten parameters 53/100

    • 0Result and completion. Does not say what the result is
    • 0Failures and branches. Linear process with no failure handling
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 14 mutating operations with no state check
    • 60Tools and files. Uses tools (bash) that frontmatter does not declare
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Steps. 28 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2206 tokens
    • 100Progress reporting. Reports progress

    Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

    Quality signals

    • +5Description has no quoted example phrases that should trigger the skill
    • +4Description does not say when NOT to use the skill (false activations)
    • +3Output format is not stated: the model decides each time
    • -41 reference files, but SKILL.md never points to them: the model will not open them
    • -31 of 2 scripts are never mentioned in SKILL.md
    • +2Single-language instructions
    • +3Description length 425: enough signal without eating the budget
    • +4Structure: 19 headings
    • +3Step-by-step instructions: 28 items
    • +4Has examples (9 code blocks)
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 75.