High Risk
"Track AI token usage and costs across providers. Import sessions, view dashboard, costs breakdown, and compare Max plan savings."
H:4 D:4 A:1 C:1
⚠️ Hazard Flags
EXEC FS_READ_WORKSPACE FS_READ_USER FS_WRITE_WORKSPACE FS_WRITE_USER FS_DELETE NET_EGRESS_ANY CREDS_ENV CREDS_FILES 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
Unauthorized tool use: INSTRUCTED_BINARY_INSTALL, INSTRUCTED_GIT_CLONE_AND_BUILD, MCP_SYS_CRITICAL_ACCESS high
Mitigation: Avoid instructing agents to install arbitrary binaries; bundle dependencies or use sandboxed environments
Command injection risk: COMMAND_INJECTION_EVAL, COMMAND_INJECTION_USER_INPUT critical
Mitigation: Avoid eval(), exec(), and compile(). Use safer alternatives like ast.literal_eval()
Social engineering indicators: SOCIAL_ENG_VAGUE_DESCRIPTION, SOCIAL_ENG_ANTHROPIC_IMPERSONATION low
Mitigation: Provide clear, detailed description of skill functionality
Tool poisoning: hidden behaviors detected (MCP_TOOL_POISONING_SENSITIVE_DATA) high
Mitigation: Remove references to sensitive data collection.
Data exfiltration patterns: DATA_EXFIL_NETWORK_REQUESTS, DATA_EXFIL_SENSITIVE_FILES high
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:tokenmeter:0.1.1",
"ssds_version": "0.2.0",
"scanner_version": "0.4.0+fe6fd9123d50",
"created_at": "2026-03-05T12:17:52.012Z",
"created_by": {
"agent": "safeagentskills-cli/generate-ssds"
},
"language": "en",
"notes": "Auto-generated SSDS. Manual review recommended."
},
"skill": {
"name": "tokenmeter",
"version": "0.1.1",
"format": "agent_skill",
"description": "\"Track AI token usage and costs across providers. Import sessions, view dashboard, costs breakdown, and compare Max plan savings.\"",
"publisher": "ClawHub",
"source": {
"channel": "clawhub",
"slug": "tokenmeter",
"owner": "cheenu1092-oss",
"downloads": 985,
"stars": 0
},
"artifact": {
"sha256": "76e76dcbaabf0b8143fd1500746916b0a147a374c7d0b93aa21e8df24005ea73",
"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": true,
"reads_user_home": true,
"reads_system": false,
"writes_workspace": true,
"writes_user_home": true,
"writes_system": false,
"can_delete": true
},
"network": {
"egress": "any",
"ingress": false
},
"credentials": {
"reads_env_vars": true,
"reads_credential_files": true,
"reads_browser_data": false,
"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": 1,
"C": 1
},
"flags": [
"EXEC",
"FS_READ_WORKSPACE",
"FS_READ_USER",
"FS_WRITE_WORKSPACE",
"FS_WRITE_USER",
"FS_DELETE",
"NET_EGRESS_ANY",
"CREDS_ENV",
"CREDS_FILES",
"PI_WEB"
],
"custom_flags": [
{
"code": "FILE_DELETE",
"name": "File Deletion",
"description": "Can delete files in: tokenmeter/checkpoint.py"
},
{
"code": "TOOL_ABUSE",
"name": "Unauthorized Tool Use",
"description": "INSTRUCTED_BINARY_INSTALL, INSTRUCTED_GIT_CLONE_AND_BUILD, MCP_SYS_CRITICAL_ACCESS: Instructs agent to install external binary or package"
},
{
"code": "SOCIAL_ENGINEERING",
"name": "Social Engineering Risk",
"description": "SOCIAL_ENG_VAGUE_DESCRIPTION, SOCIAL_ENG_ANTHROPIC_IMPERSONATION: Skill description is too vague or missing"
},
{
"code": "COMMAND_INJECTION",
"name": "Command Injection Risk",
"description": "COMMAND_INJECTION_EVAL, COMMAND_INJECTION_USER_INPUT: Dangerous code execution functions that can execute arbitrary code"
},
{
"code": "TOOL_POISONING",
"name": "Tool Poisoning",
"description": "Hidden secondary behavior detected: MCP_TOOL_POISONING_SENSITIVE_DATA"
},
{
"code": "DATA_EXFILTRATION",
"name": "Data Exfiltration Risk",
"description": "DATA_EXFIL_NETWORK_REQUESTS, DATA_EXFIL_SENSITIVE_FILES: HTTP client library imports that enable external communication"
}
],
"confidence": {
"level": "medium",
"basis": [
"static_analysis"
],
"notes": "Detected 6 security patterns (13 vendored rule hits). Review recommended."
},
"rationale": {
"H": "H4: Critical: Privilege escalation or malware detected",
"D": "D4: Critical: Credential theft or data exfiltration",
"A": "A1: Local side effects only",
"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": "Unauthorized tool use: INSTRUCTED_BINARY_INSTALL, INSTRUCTED_GIT_CLONE_AND_BUILD, MCP_SYS_CRITICAL_ACCESS",
"severity": "high",
"mitigation": "Avoid instructing agents to install arbitrary binaries; bundle dependencies or use sandboxed environments"
},
{
"risk": "Command injection risk: COMMAND_INJECTION_EVAL, COMMAND_INJECTION_USER_INPUT",
"severity": "critical",
"mitigation": "Avoid eval(), exec(), and compile(). Use safer alternatives like ast.literal_eval()"
},
{
"risk": "Social engineering indicators: SOCIAL_ENG_VAGUE_DESCRIPTION, SOCIAL_ENG_ANTHROPIC_IMPERSONATION",
"severity": "low",
"mitigation": "Provide clear, detailed description of skill functionality"
},
{
"risk": "Tool poisoning: hidden behaviors detected (MCP_TOOL_POISONING_SENSITIVE_DATA)",
"severity": "high",
"mitigation": "Remove references to sensitive data collection."
},
{
"risk": "Data exfiltration patterns: DATA_EXFIL_NETWORK_REQUESTS, DATA_EXFIL_SENSITIVE_FILES",
"severity": "high",
"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": "tokenmeter/time_utils.py",
"file_path": "tokenmeter/time_utils.py"
},
{
"evidence_id": "EV:file-3",
"type": "file_excerpt",
"title": "tokenmeter/pricing.py",
"file_path": "tokenmeter/pricing.py"
},
{
"evidence_id": "EV:file-4",
"type": "file_excerpt",
"title": "tokenmeter/importer.py",
"file_path": "tokenmeter/importer.py"
},
{
"evidence_id": "EV:file-5",
"type": "file_excerpt",
"title": "tokenmeter/fetcher.py",
"file_path": "tokenmeter/fetcher.py"
},
{
"evidence_id": "EV:file-6",
"type": "file_excerpt",
"title": "tokenmeter/db.py",
"file_path": "tokenmeter/db.py"
},
{
"evidence_id": "EV:file-7",
"type": "file_excerpt",
"title": "tokenmeter/cli.py",
"file_path": "tokenmeter/cli.py"
},
{
"evidence_id": "EV:file-8",
"type": "file_excerpt",
"title": "tokenmeter/checkpoint.py",
"file_path": "tokenmeter/checkpoint.py"
},
{
"evidence_id": "EV:file-9",
"type": "file_excerpt",
"title": "tokenmeter/__init__.py",
"file_path": "tokenmeter/__init__.py"
},
{
"evidence_id": "EV:file-10",
"type": "file_excerpt",
"title": "tests/test_pricing.py",
"file_path": "tests/test_pricing.py"
},
{
"evidence_id": "EV:cisco-1",
"type": "file_excerpt",
"title": "DATA_EXFIL_NETWORK_REQUESTS [MEDIUM] tokenmeter/fetcher.py:60: import requests",
"file_path": "tokenmeter/fetcher.py"
},
{
"evidence_id": "EV:cisco-2",
"type": "file_excerpt",
"title": "COMMAND_INJECTION_EVAL [CRITICAL] tokenmeter/checkpoint.py:141: \"last_imported\": __import__(\"datetime\").datetime.now().isoformat(),",
"file_path": "tokenmeter/checkpoint.py"
},
{
"evidence_id": "EV:cisco-3",
"type": "file_excerpt",
"title": "DATA_EXFIL_SENSITIVE_FILES [HIGH] tokenmeter/checkpoint.py:25: with open(filepath, \"rb\") as f:",
"file_path": "tokenmeter/checkpoint.py"
},
{
"evidence_id": "EV:cisco-4",
"type": "file_excerpt",
"title": "INSTRUCTED_BINARY_INSTALL [HIGH] SKILL.md:51: pip install -e .",
"file_path": "SKILL.md"
},
{
"evidence_id": "EV:cisco-5",
"type": "file_excerpt",
"title": "INSTRUCTED_GIT_CLONE_AND_BUILD [MEDIUM] SKILL.md:43: git clone https://github.com/jugaad-lab/tokenmeter.git",
"file_path": "SKILL.md"
},
{
"evidence_id": "EV:cisco-6",
"type": "file_excerpt",
"title": "SOCIAL_ENG_VAGUE_DESCRIPTION [LOW] SKILL.md:1: ---",
"file_path": "SKILL.md"
},
{
"evidence_id": "EV:cisco-7",
"type": "file_excerpt",
"title": "SOCIAL_ENG_ANTHROPIC_IMPERSONATION [MEDIUM] SKILL.md:103: anthropic/claude-opus-4 $741.95 65.0%",
"file_path": "SKILL.md"
},
{
"evidence_id": "EV:cisco-8",
"type": "file_excerpt",
"title": "MCP_TOOL_POISONING_SENSITIVE_DATA [HIGH] SKILL.md:200: - Extract token usage from each LLM call",
"file_path": "SKILL.md"
},
{
"evidence_id": "EV:cisco-9",
"type": "file_excerpt",
"title": "INSTRUCTED_BINARY_INSTALL [HIGH] README.md:25: pip install tokenmeter",
"file_path": "README.md"
},
{
"evidence_id": "EV:cisco-10",
"type": "file_excerpt",
"title": "MCP_TOOL_POISONING_SENSITIVE_DATA [HIGH] README.md:17: - 📊 Tracking token usage across OpenAI, Anthropic, Azure OpenAI, and Google",
"file_path": "README.md"
}
]
}