Global AI Governance Landscape（全球AI治理版图）

• Outcome: Pass
• Latest audit: Nov 6, 2026 · 11:30 AM UTC
• Version: 1.0.3

Overview

This skill provides an offline AI governance reference toolkit with proportionate local checks and no observed harmful behavior.

The toolkit needs Python and no credentials or network access. Optional validation rewrites reports inside verify/. Treat the bundled audit and checksum attestation as publisher claims because they contain inconsistencies. Policy facts were not independently verified; check current official sources before relying on them for decisions.

A.I.G

Vulnerability Patterns

• Skill Instruction Hijacking: Alters the agent's session goals or safety constraints when the skill loads
• Agent Memory Poisoning: Writes attacker-controlled rules into memory that affect later sessions
• Remote Payload Retrieval and Execution: Fetches external code whose behavior can change after review
• Embedded Malicious Code: Ships malicious scripts inside the skill and executes them locally
• Unauthorized Access and Privilege Escalation: Obtains permissions beyond the task's legitimate needs

SkillSpector

Vulnerability Patterns

• Behavioral AST: exec() Call, eval() Call, Dynamic Import
• MCP Least Privilege: Underdeclared Capability, Wildcard Permission, Missing Permission Declaration
• Prompt Injection: Instruction Override, Hidden Instructions, Exfiltration Commands
• Data Exfiltration: External Transmission, Env Variable Harvesting, File System Enumeration
• Privilege Escalation: Excessive Permissions, Sudo/Root Execution, Credential Access

Findings (7)

Skill declares no tool scope ('permissions' or 'allowed-tools') but code capabilities were detected: file_read, file_write, shell.

Medium

• Category: MCP Least Privilege
• Confidence: 70% confidence

Finding

Without declared permissions the skill's intent is opaque and cannot be validated.

Content

No source excerpt is available for this finding.

subprocess module call

Medium

• Category: Dangerous Code Execution
• Confidence: 70% confidence

Finding

subprocess module calls execute external commands. Without careful input validation, this enables command injection.

Content

Scanner excerpt · verify/global_ai_quality_test.py (reported line 55)May include surrounding context.

python

def check_tools():
    items = []
    try:
        r = subprocess.run([PYTHON, TOOL, "org", "--name", "waico"], capture_output=True, text=True, timeout=60)
        out = r.stdout or ""
        items.append({"name": "org 命令执行", "pass": r.returncode == 0 and "WAICO" in out and "29" in out})
        r2 = subprocess.run([PYTHON, TOOL, "initiative", "--actor", "cn"], capture_output=True, text=True, timeout=60)

subprocess module call

Medium

• Category: Dangerous Code Execution
• Confidence: 70% confidence

Finding

subprocess module calls execute external commands. Without careful input validation, this enables command injection.

Content

Scanner excerpt · verify/global_ai_quality_test.py (reported line 58)May include surrounding context.

python

r = subprocess.run([PYTHON, TOOL, "org", "--name", "waico"], capture_output=True, text=True, timeout=60)
        out = r.stdout or ""
        items.append({"name": "org 命令执行", "pass": r.returncode == 0 and "WAICO" in out and "29" in out})
        r2 = subprocess.run([PYTHON, TOOL, "initiative", "--actor", "cn"], capture_output=True, text=True, timeout=60)
        items.append({"name": "initiative 命令执行", "pass": r2.returncode == 0 and "全球人工智能治理倡议" in (r2.stdout or "")})
        r3 = subprocess.run([PYTHON, TOOL, "region", "--name", "eu"], capture_output=True, text=True, timeout=60)
        items.append({"name": "region 命令执行", "pass": r3.returncode == 0 and "AI Act" in (r3.stdout or "")})

subprocess module call

Medium

• Category: Dangerous Code Execution
• Confidence: 70% confidence

Finding

subprocess module calls execute external commands. Without careful input validation, this enables command injection.

Content

Scanner excerpt · verify/global_ai_quality_test.py (reported line 60)May include surrounding context.

python

items.append({"name": "org 命令执行", "pass": r.returncode == 0 and "WAICO" in out and "29" in out})
        r2 = subprocess.run([PYTHON, TOOL, "initiative", "--actor", "cn"], capture_output=True, text=True, timeout=60)
        items.append({"name": "initiative 命令执行", "pass": r2.returncode == 0 and "全球人工智能治理倡议" in (r2.stdout or "")})
        r3 = subprocess.run([PYTHON, TOOL, "region", "--name", "eu"], capture_output=True, text=True, timeout=60)
        items.append({"name": "region 命令执行", "pass": r3.returncode == 0 and "AI Act" in (r3.stdout or "")})
        r4 = subprocess.run([PYTHON, TOOL, "timeline"], capture_output=True, text=True, timeout=60)
        out4 = r4.stdout or ""

subprocess module call

Medium

• Category: Dangerous Code Execution
• Confidence: 70% confidence

Finding

subprocess module calls execute external commands. Without careful input validation, this enables command injection.

Content

Scanner excerpt · verify/global_ai_quality_test.py (reported line 62)May include surrounding context.

python

items.append({"name": "initiative 命令执行", "pass": r2.returncode == 0 and "全球人工智能治理倡议" in (r2.stdout or "")})
        r3 = subprocess.run([PYTHON, TOOL, "region", "--name", "eu"], capture_output=True, text=True, timeout=60)
        items.append({"name": "region 命令执行", "pass": r3.returncode == 0 and "AI Act" in (r3.stdout or "")})
        r4 = subprocess.run([PYTHON, TOOL, "timeline"], capture_output=True, text=True, timeout=60)
        out4 = r4.stdout or ""
        items.append({"name": "timeline 命令执行", "pass": r4.returncode == 0 and "2026-07-16" in out4 and "2019" in out4})
        r5 = subprocess.run([PYTHON, TOOL, "assess"], capture_output=True, text=True, timeout=60)

subprocess module call

Medium

• Category: Dangerous Code Execution
• Confidence: 70% confidence

Finding

subprocess module calls execute external commands. Without careful input validation, this enables command injection.

Content

Scanner excerpt · verify/global_ai_quality_test.py (reported line 65)May include surrounding context.

python

r4 = subprocess.run([PYTHON, TOOL, "timeline"], capture_output=True, text=True, timeout=60)
        out4 = r4.stdout or ""
        items.append({"name": "timeline 命令执行", "pass": r4.returncode == 0 and "2026-07-16" in out4 and "2019" in out4})
        r5 = subprocess.run([PYTHON, TOOL, "assess"], capture_output=True, text=True, timeout=60)
        items.append({"name": "assess 命令执行", "pass": r5.returncode == 0 and "机制化" in (r5.stdout or "")})
    except Exception as e:
        items = [{"name": f"执行失败: {e}", "pass": False}]

subprocess module call

Medium

• Category: Dangerous Code Execution
• Confidence: 70% confidence

Finding

subprocess module calls execute external commands. Without careful input validation, this enables command injection.

Content

Scanner excerpt · verify/global_ai_quality_test.py (reported line 77)May include surrounding context.

python

items = []
    for name, expects in cases:
        try:
            r = subprocess.run([PYTHON, TOOL, "org", "--name", name], capture_output=True, text=True, timeout=60)
            ok = r.returncode == 0 and all(e in (r.stdout or "") for e in expects)
            items.append({"name": f"org({name})→{expects[0]}", "pass": ok})
        except Exception as e: