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ktg-plugin-marketplace/plugins/llm-security-copilot/skills/scan/SKILL.md
Kjell Tore Guttormsen f418a8fe08 feat(llm-security-copilot): port llm-security v5.1.0 to GitHub Copilot CLI 
Full port of llm-security plugin for internal use on Windows with GitHub
Copilot CLI. Protocol translation layer (copilot-hook-runner.mjs)
normalizes Copilot camelCase I/O to Claude Code snake_case format — all
original hook scripts run unmodified.

- 8 hooks with protocol translation (stdin/stdout/exit code)
- 18 SKILL.md skills (Agent Skills Open Standard)
- 6 .agent.md agent definitions
- 20 scanners + 14 scanner lib modules (unchanged)
- 14 knowledge files (unchanged)
- 39 test files including copilot-port-verify.mjs (17 tests)
- Windows-ready: node:path, os.tmpdir(), process.execPath, no bash

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-04-09 21:56:10 +02:00

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name description
security-scan Scan files, directories, or repos for security issues — secrets, injection vulnerabilities, supply chain risks, OWASP LLM patterns

Security Scan

Scan target for security issues. Accepts local paths or repository URLs.

Step 1: Resolve Target

  • If $ARGUMENTS contains --deep → strip it, set run_deep_scan = true
  • If $ARGUMENTS contains --branch <name> → strip it, set branch = <name>
  • If $ARGUMENTS is empty → target = current working directory
  • If $ARGUMENTS starts with https:// → clone the repo: 
    node <plugin-root>/scanners/lib/git-clone.mjs clone "<url>" [--branch <branch>]
    Set clone_path = stdout (trimmed), target = clone_path
  • Otherwise → target = $ARGUMENTS

IMPORTANT: Cleanup Guarantee (remote scans)

If clone_path is set, cleanup MUST run regardless of scan outcome:

node <plugin-root>/scanners/lib/git-clone.mjs cleanup "<clone_path>"

Step 1.5: Pre-extraction (remote scans only)

If remote, extract evidence safely before analysis:

node <plugin-root>/scanners/lib/fs-utils.mjs tmppath "content-extract.json"
node <plugin-root>/scanners/content-extractor.mjs "<target>" --output-file "<evidence_file>"

Step 2: Detect Scan Type

  • Single .md file: skill scan only
  • Directory: Glob for **/commands/*.md, **/agents/*.md, **/skills/*/SKILL.md → skill scan. Glob for **/.mcp.json, **/package.json with mcpServers → MCP scan.

Step 3: Skill Security Analysis

Read <plugin-root>/knowledge/skill-threat-patterns.md and <plugin-root>/knowledge/secrets-patterns.md.

If remote (evidence package exists): Read the evidence JSON. Analyze sections: injection_findings, frontmatter_inventory, shell_commands, credential_references, persistence_signals, claude_md_analysis, cross_instruction_flags. [INJECTION-PATTERN-STRIPPED] markers are confirmed findings.

If local: Read target files directly. Apply 7 threat categories:

  1. Prompt Injection — ignore previous, forget your, identity redefinition, spoofed system headers
  2. Data Exfiltration — curl/wget to external URLs, base64 pipe chains, credential read+send
  3. Privilege Escalation — unjustified Bash access, Write+Bash without rationale, chmod/sudo
  4. Scope Creep — credential file access, crypto wallet paths, SSH keys, browser stores
  5. Hidden Instructions — Unicode Tag steganography, zero-width chars, base64 payloads, HTML comments
  6. Toolchain Manipulation — registry redirection, post-install scripts, external requirements
  7. Persistence — cron jobs, LaunchAgents, systemd, shell profile modification, git hooks

For each finding: severity (Critical/High/Medium/Low/Info), category, file, line, OWASP reference, evidence, remediation.

Step 4: MCP Security Analysis (if applicable)

Read <plugin-root>/knowledge/mcp-threat-patterns.md. Analyze MCP configs for: tool poisoning, hidden instructions in descriptions, rug pull signals, supply chain risks, permission surface.

Step 5: Aggregate and Report

Combine counts. risk_score = min(100, critical*25 + high*10 + medium*4 + low*1). Verdict: critical>=1 OR score>=61 → BLOCK, high>=1 OR score>=21 → WARNING, else ALLOW.

Output banner then all findings grouped by severity (critical→info).

Step 6: Deep Scan (only if --deep)

If run_deep_scan = true:

node <plugin-root>/scanners/scan-orchestrator.mjs "<target>" --output-file "<tmp>"

Parse results, merge with LLM findings, show "Deep Scan Findings" section.

Step 7: Cleanup (only if remote)

Run cleanup commands from Step 1.