vector-memory-hack
ยท v1.0.3
Fast semantic search for AI agent memory files using TF-IDF and SQLite. Enables instant context retrieval from MEMORY.md or any markdown documentation. Use when the agent needs to (1) Find relevant context before starting a task, (2) Search through large memory files efficiently, (3) Retrieve specific rules or decisions without reading entire files, (4) Enable semantic similarity search instead of keyword matching. Lightweight alternative to heavy embedding models - zero external dependencies, <10ms search time.
โ ๏ธ Hazard Flags
๐ Capabilities
Execution
- โ Shell execution
- โ Code execution
- โ Install dependencies
- โ Persistence
- Privilege: user
Filesystem
- โ Read workspace
- โ Write workspace
- โ Read home
- โ Write home
- โ Read system
- โ Delete
Network
- Egress: any
- โ Ingress
Credentials
- โ Environment vars
- โ Credential files
- โ Browser data
- โ Keychain
Actions
๐ Containment
Level: elevated
- SANDBOX_CONTAINER: Code execution capability
- LOG_ACTIONS: Audit trail for all actions
โก Risks
Mitigation: Use pre-built packages or vendored dependencies instead of cloning repos
Mitigation: Provide clear, detailed description of skill functionality
Want a deeper analysis?
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๐ง Deep Analysis โ $5.00๐จ Incident Response
Kill switch: Stop the agent process
Containment: Review logs for unexpected actions
Recovery: Depends on skill capabilities
๐ Raw SSDS JSON click to expand
{
"meta": {
"document_id": "ssds:auto:vector-memory-hack:1.0.3",
"ssds_version": "0.2.0",
"scanner_version": "0.4.0+fe6fd9123d50",
"created_at": "2026-03-05T02:44:21.455Z",
"created_by": {
"agent": "safeagentskills-cli/generate-ssds"
},
"language": "en",
"notes": "Auto-generated SSDS. Manual review recommended."
},
"skill": {
"name": "vector-memory-hack",
"version": "1.0.3",
"format": "agent_skill",
"description": "Fast semantic search for AI agent memory files using TF-IDF and SQLite. Enables instant context retrieval from MEMORY.md or any markdown documentation. Use when the agent needs to (1) Find relevant context before starting a task, (2) Search through large memory files efficiently, (3) Retrieve specific rules or decisions without reading entire files, (4) Enable semantic similarity search instead of keyword matching. Lightweight alternative to heavy embedding models - zero external dependencies, <10ms search time.",
"publisher": "ClawHub",
"source": {
"channel": "local"
},
"artifact": {
"sha256": "6751f51aa17d9c9eb06d983a5040a16c392c7f92fa5b4ba64b40a13697be2a02",
"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": false,
"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": false,
"reads_credential_files": false,
"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": 3,
"D": 0,
"A": 0,
"C": 1
},
"flags": [
"EXEC",
"NET_EGRESS_ANY",
"PI_WEB"
],
"custom_flags": [
{
"code": "TOOL_ABUSE",
"name": "Unauthorized Tool Use",
"description": "INSTRUCTED_GIT_CLONE_AND_BUILD: Instructs agent to clone and potentially build from source"
},
{
"code": "SOCIAL_ENGINEERING",
"name": "Social Engineering Risk",
"description": "SOCIAL_ENG_VAGUE_DESCRIPTION: Skill description is too vague or missing"
}
],
"confidence": {
"level": "medium",
"basis": [
"static_analysis"
],
"notes": "Detected 2 security patterns (2 vendored rule hits). Review recommended."
},
"rationale": {
"H": "H3: Shell/code execution or persistence detected",
"D": "D0: No sensitive data access",
"A": "A0: No side effects detected",
"C": "C1: General content"
}
},
"containment": {
"level": "elevated",
"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_GIT_CLONE_AND_BUILD",
"severity": "medium",
"mitigation": "Use pre-built packages or vendored dependencies instead of cloning repos"
},
{
"risk": "Social engineering indicators: SOCIAL_ENG_VAGUE_DESCRIPTION",
"severity": "low",
"mitigation": "Provide clear, detailed description of skill functionality"
}
],
"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": "README.md",
"file_path": "README.md"
},
{
"evidence_id": "EV:file-3",
"type": "file_excerpt",
"title": "scripts/vector_search.py",
"file_path": "scripts/vector_search.py"
},
{
"evidence_id": "EV:file-4",
"type": "file_excerpt",
"title": "SKILL.md",
"file_path": "SKILL.md"
},
{
"evidence_id": "EV:cisco-1",
"type": "file_excerpt",
"title": "INSTRUCTED_GIT_CLONE_AND_BUILD [MEDIUM] README.md:135: git clone https://github.com/yourusername/vector-memory-hack.git",
"file_path": "README.md"
},
{
"evidence_id": "EV:cisco-2",
"type": "file_excerpt",
"title": "SOCIAL_ENG_VAGUE_DESCRIPTION [LOW] SKILL.md:1: ---",
"file_path": "SKILL.md"
}
]
}