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CC hunting-for-lateral-movement-via-wmi

Detects WMI-based lateral movement (e.g. wmic process call create, Win32_Process.Create()) by analyzing Windows Event ID 4688 and Sysmon Event ID 1 for WmiPrvSE.exe spawning suspicious child processes like cmd.exe or powershell.exe, plus WMI-Activity/Operational events 5857/5860/5861 for subscription-based persistence. Use when hunting for remote code execution via WMI or investigating lateral movement across Windows hosts.

mukul975/Anthropic-Cybersecurity-Skills Agent Skills author: mukul975 Apache-2.0 4 files · 1 script body ≈ 459 tokens Open the sourcegithub.com↗ analyzed 5 d ago

Detects WMI-based lateral movement (e.g. wmic process call create, Win32Process.Create()) by analyzing Windows Event ID 4688 and Sysmon Event ID 1 for…

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

AnalyzerPeople and hiringtype 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
83/100
safety, quality, tests
Safety 60%
91
Quality 40%
71
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 · 9

    ✓ No critical or high findings

    Medium and low: 9
    • low Risky intent intent-offensive-security references/api-reference.md:1
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      # API Reference: Hunting for Lateral Movement via WMI
    • low Risky intent intent-offensive-security references/api-reference.md:13
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      ## WMI Lateral Movement Process Chain
    • low Risky intent intent-offensive-security references/api-reference.md:77
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use) (detector / deny-list definition)
      - Detecting WMI Lateral Movement: https://imphash.medium.com/dete…t-2
      detector
    • low Risky intent intent-offensive-security references/api-reference.md:78
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      - JPCERT Lateral Movement: https://www.jpcert.or.jp/english/pub/sr/2017…pdf
    • low Risky intent intent-offensive-security SKILL.md:3
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use) (detector / deny-list definition)
      description: Detects WMI-based lateral movement (e.g. wmic process call create,
      detector
    • low Risky intent intent-offensive-security SKILL.md:7
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      when hunting for remote code execution via WMI or investigating lateral movement across
    • low Risky intent intent-offensive-security SKILL.md:33
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      # Hunting for Lateral Movement via WMI
    • low Risky intent intent-offensive-security SKILL.md:37
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      Windows Management Instrumentation (WMI) is commonly abused for lateral movement via `wmic process call create` or Win3…ate() to execute commands on remote hosts. Detection focuses on iden
    • low Risky intent intent-offensive-security SKILL.md:42
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      - When investigating security incidents that require hunting for lateral movement via wmi

    Files scanned: 4. 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"

    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. 2 mutating operations with no state check
    • 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Steps. 8 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 459 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
    • +4No input/output examples
    • -41 reference files, but SKILL.md never points to them: the model will not open them
    • -31 of 1 scripts are never mentioned in SKILL.md
    • +2Single-language instructions
    • +3Description length 427: enough signal without eating the budget
    • +4Structure: 10 headings
    • +3Step-by-step instructions: 8 items
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 71.