High Risk
12306 查询与订票辅助技能，支持余票查询、经停站查询、中转换乘、候补查询与提交/取消、登录状态检查、密码登录与二维码登录、下单与支付链接获取；当用户提到火车票、高铁票、经停站、中转、候补或 12306 查票时触发。
H:4 D:4 A:0 C:1
⚠️ Hazard Flags
EXEC FS_READ_USER NET_EGRESS_ANY CREDS_ENV CREDS_BROWSER PI_WEB
📋 Capabilities
Execution
- ✅ Shell execution
- ❌ Code execution
- ❌ Install dependencies
- ❌ Persistence
- Privilege: user
Filesystem
- ❌ Read workspace
- ❌ Write workspace
- ✅ Read home
- ❌ Write home
- ❌ Read system
- ❌ Delete
Network
- Egress: any
- ❌ Ingress
Credentials
- ✅ Environment vars
- ❌ Credential files
- ✅ Browser data
- ❌ Keychain
Actions
❌ send messages❌ post public❌ purchase❌ transfer money❌ deploy❌ delete external
🔒 Containment
Level: maximum
Required:
- SANDBOX_CONTAINER: Code execution capability
Recommended:
- LOG_ACTIONS: Audit trail for all actions
⚡ Risks
Base64 decode + execute pattern in: client.py critical
Mitigation: Decode and review obfuscated content before use.
Resource abuse detected: RESOURCE_ABUSE_INFINITE_LOOP high
Mitigation: Add proper exit conditions or limits to loops
Command injection risk: MCP_SQL_BLIND high
Mitigation: Remove SQL exploitation patterns.
Social engineering indicators: SOCIAL_ENG_VAGUE_DESCRIPTION low
Mitigation: Provide clear, detailed description of skill functionality
Data exfiltration patterns: DATA_EXFIL_NETWORK_REQUESTS, DATA_EXFIL_ENV_VARS, DATA_EXFIL_BASE64_AND_NETWORK critical
Mitigation: Ensure network access is necessary and documented
Want a deeper analysis?
This report was generated by static analysis. Get an LLM-powered deep review with behavioral reasoning and attack surface mapping.
🧠 Deep Analysis — $5.00🚨 Incident Response
Kill switch: Stop the agent process
Containment: Review logs for unexpected actions
Recovery: Depends on skill capabilities
📄 Raw SSDS JSON click to expand
{
"meta": {
"document_id": "ssds:auto:12306-train-assistant:0.1.7",
"ssds_version": "0.2.0",
"scanner_version": "0.4.0+fe6fd9123d50",
"created_at": "2026-03-14T05:38:22.608Z",
"created_by": {
"agent": "safeagentskills-cli/generate-ssds"
},
"language": "en",
"notes": "Auto-generated SSDS. Manual review recommended."
},
"skill": {
"name": "12306 Train Assistant",
"version": "0.1.7",
"format": "agent_skill",
"description": "12306 查询与订票辅助技能，支持余票查询、经停站查询、中转换乘、候补查询与提交/取消、登录状态检查、密码登录与二维码登录、下单与支付链接获取；当用户提到火车票、高铁票、经停站、中转、候补或 12306 查票时触发。",
"publisher": "ClawHub",
"source": {
"channel": "clawhub",
"slug": "12306-train-assistant",
"owner": "myxtype",
"downloads": 172,
"stars": 2
},
"artifact": {
"sha256": "dee5fc08232054dc3807b936202bf6394d73cdf7be68bb2c2d53f4bcafb3e5a5",
"hash_method": "files_sorted"
}
},
"capabilities": {
"execution": {
"can_exec_shell": true,
"can_exec_code": false,
"privilege_level": "user",
"can_install_deps": false,
"can_persist": false
},
"filesystem": {
"reads_workspace": false,
"reads_user_home": true,
"reads_system": false,
"writes_workspace": false,
"writes_user_home": false,
"writes_system": false,
"can_delete": false
},
"network": {
"egress": "any",
"ingress": false
},
"credentials": {
"reads_env_vars": true,
"reads_credential_files": false,
"reads_browser_data": true,
"reads_keychain": false
},
"services": [],
"actions": {
"can_send_messages": false,
"can_post_public": false,
"can_purchase": false,
"can_transfer_money": false,
"can_deploy": false,
"can_delete_external": false
},
"prompt_injection_surfaces": [
"web"
],
"content_types": [
"general"
]
},
"hazards": {
"hdac": {
"H": 4,
"D": 4,
"A": 0,
"C": 1
},
"flags": [
"EXEC",
"FS_READ_USER",
"NET_EGRESS_ANY",
"CREDS_ENV",
"CREDS_BROWSER",
"PI_WEB"
],
"custom_flags": [
{
"code": "BASE64_EXEC",
"name": "Base64 Execute",
"description": "Decodes and executes obfuscated code in: client.py"
},
{
"code": "RESOURCE_ABUSE",
"name": "Resource Abuse Risk",
"description": "RESOURCE_ABUSE_INFINITE_LOOP detected: Infinite loop without clear exit condition"
},
{
"code": "SOCIAL_ENGINEERING",
"name": "Social Engineering Risk",
"description": "SOCIAL_ENG_VAGUE_DESCRIPTION: Skill description is too vague or missing"
},
{
"code": "COMMAND_INJECTION",
"name": "Command Injection Risk",
"description": "MCP_SQL_BLIND: Blind SQL injection, system table access, or stored procedure abuse"
},
{
"code": "DATA_EXFILTRATION",
"name": "Data Exfiltration Risk",
"description": "DATA_EXFIL_NETWORK_REQUESTS, DATA_EXFIL_ENV_VARS, DATA_EXFIL_BASE64_AND_NETWORK: HTTP client library imports that enable external communication"
}
],
"confidence": {
"level": "medium",
"basis": [
"static_analysis"
],
"notes": "Detected 5 security patterns (6 vendored rule hits). Review recommended."
},
"rationale": {
"H": "H4: Critical: Privilege escalation or malware detected",
"D": "D4: Critical: Credential theft or data exfiltration",
"A": "A0: No side effects detected",
"C": "C1: General content"
}
},
"containment": {
"level": "maximum",
"required": [
{
"control": "SANDBOX_CONTAINER",
"reason": "Code execution capability"
}
],
"recommended": [
{
"control": "LOG_ACTIONS",
"reason": "Audit trail for all actions"
}
],
"uncontained_risk": "Risk level depends on manual review of actual capabilities."
},
"risks": {
"risks": [
{
"risk": "Base64 decode + execute pattern in: client.py",
"severity": "critical",
"mitigation": "Decode and review obfuscated content before use."
},
{
"risk": "Resource abuse detected: RESOURCE_ABUSE_INFINITE_LOOP",
"severity": "high",
"mitigation": "Add proper exit conditions or limits to loops"
},
{
"risk": "Command injection risk: MCP_SQL_BLIND",
"severity": "high",
"mitigation": "Remove SQL exploitation patterns."
},
{
"risk": "Social engineering indicators: SOCIAL_ENG_VAGUE_DESCRIPTION",
"severity": "low",
"mitigation": "Provide clear, detailed description of skill functionality"
},
{
"risk": "Data exfiltration patterns: DATA_EXFIL_NETWORK_REQUESTS, DATA_EXFIL_ENV_VARS, DATA_EXFIL_BASE64_AND_NETWORK",
"severity": "critical",
"mitigation": "Ensure network access is necessary and documented"
}
],
"limitations": [
"Static analysis only - runtime behavior not verified"
]
},
"incident_response": {
"kill_switch": [
"Stop the agent process"
],
"containment": [
"Review logs for unexpected actions"
],
"recovery": [
"Depends on skill capabilities"
]
},
"evidence": [
{
"evidence_id": "EV:file-1",
"type": "file_excerpt",
"title": "_meta.json",
"file_path": "_meta.json"
},
{
"evidence_id": "EV:file-2",
"type": "file_excerpt",
"title": "SKILL.md",
"file_path": "SKILL.md"
},
{
"evidence_id": "EV:file-3",
"type": "file_excerpt",
"title": "client.py",
"file_path": "client.py"
},
{
"evidence_id": "EV:cisco-1",
"type": "file_excerpt",
"title": "SOCIAL_ENG_VAGUE_DESCRIPTION [LOW] SKILL.md:1: ---",
"file_path": "SKILL.md"
},
{
"evidence_id": "EV:cisco-2",
"type": "file_excerpt",
"title": "DATA_EXFIL_NETWORK_REQUESTS [MEDIUM] client.py:21: import requests",
"file_path": "client.py"
},
{
"evidence_id": "EV:cisco-3",
"type": "file_excerpt",
"title": "DATA_EXFIL_ENV_VARS [MEDIUM] client.py:3377: env_pwd = os.getenv(\"KYFW_PASSWORD\")",
"file_path": "client.py"
},
{
"evidence_id": "EV:cisco-4",
"type": "file_excerpt",
"title": "DATA_EXFIL_BASE64_AND_NETWORK [CRITICAL] client.py:477: return base64.b64encode(cipher).decode(\"ascii\")",
"file_path": "client.py"
},
{
"evidence_id": "EV:cisco-5",
"type": "file_excerpt",
"title": "RESOURCE_ABUSE_INFINITE_LOOP [HIGH] client.py:1461: while True:",
"file_path": "client.py"
},
{
"evidence_id": "EV:cisco-6",
"type": "file_excerpt",
"title": "MCP_SQL_BLIND [HIGH] client.py:1507: time.sleep(max(0.3, poll_interval))",
"file_path": "client.py"
}
]
}