Memento

· v0.6.0

High Risk

Local persistent memory for OpenClaw agents. Captures conversations, extracts structured facts via LLM, and auto-recalls relevant knowledge before each turn. Privacy-first, all stored data stays local in SQLite.

H:4 D:4 A:0 C:1

⚠️ Hazard Flags

EXEC CODE_EXEC FS_READ_WORKSPACE FS_READ_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_GIT_CLONE_AND_BUILD medium

Mitigation: Use pre-built packages or vendored dependencies instead of cloning repos

Command injection risk: COMMAND_INJECTION_EVAL, MCP_SQL_BLIND 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

Data exfiltration patterns: MCP_EXFIL_CONVERSATION high

Mitigation: Remove references to exfiltrating conversation data or monitoring input.

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🚨 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:memento:0.6.0",
    "ssds_version": "0.2.0",
    "scanner_version": "0.4.0+fe6fd9123d50",
    "created_at": "2026-03-05T14:31:06.105Z",
    "created_by": {
      "agent": "safeagentskills-cli/generate-ssds"
    },
    "language": "en",
    "notes": "Auto-generated SSDS. Manual review recommended."
  },
  "skill": {
    "name": "Memento",
    "version": "0.6.0",
    "format": "agent_skill",
    "description": "Local persistent memory for OpenClaw agents. Captures conversations, extracts structured facts via LLM, and auto-recalls relevant knowledge before each turn. Privacy-first, all stored data stays local in SQLite.",
    "publisher": "unknown",
    "source": {
      "channel": "local"
    },
    "artifact": {
      "sha256": "60da3ca18122564ffd16f261574bb0b11b3acb2d5ee4c32a5bf4c7f09e227cee",
      "hash_method": "files_sorted"
    }
  },
  "capabilities": {
    "execution": {
      "can_exec_shell": true,
      "can_exec_code": true,
      "privilege_level": "user",
      "can_install_deps": true,
      "can_persist": false
    },
    "filesystem": {
      "reads_workspace": true,
      "reads_user_home": true,
      "reads_system": false,
      "writes_workspace": false,
      "writes_user_home": false,
      "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": 0,
      "C": 1
    },
    "flags": [
      "EXEC",
      "CODE_EXEC",
      "FS_READ_WORKSPACE",
      "FS_READ_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: src/extraction/embedded-runner.ts"
      },
      {
        "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, SOCIAL_ENG_ANTHROPIC_IMPERSONATION: Skill description is too vague or missing"
      },
      {
        "code": "COMMAND_INJECTION",
        "name": "Command Injection Risk",
        "description": "COMMAND_INJECTION_EVAL, MCP_SQL_BLIND: Dangerous code execution functions that can execute arbitrary code"
      },
      {
        "code": "DATA_EXFILTRATION",
        "name": "Data Exfiltration Risk",
        "description": "MCP_EXFIL_CONVERSATION: Conversation, clipboard, or keystroke exfiltration patterns"
      }
    ],
    "confidence": {
      "level": "medium",
      "basis": [
        "static_analysis"
      ],
      "notes": "Detected 5 security patterns (10 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": "Unauthorized tool use: INSTRUCTED_GIT_CLONE_AND_BUILD",
        "severity": "medium",
        "mitigation": "Use pre-built packages or vendored dependencies instead of cloning repos"
      },
      {
        "risk": "Command injection risk: COMMAND_INJECTION_EVAL, MCP_SQL_BLIND",
        "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": "Data exfiltration patterns: MCP_EXFIL_CONVERSATION",
        "severity": "high",
        "mitigation": "Remove references to exfiltrating conversation data or monitoring input."
      }
    ],
    "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": "package.json",
      "file_path": "package.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": "SKILL.md",
      "file_path": "SKILL.md"
    },
    {
      "evidence_id": "EV:file-4",
      "type": "file_excerpt",
      "title": "src/capture/buffer.ts",
      "file_path": "src/capture/buffer.ts"
    },
    {
      "evidence_id": "EV:file-5",
      "type": "file_excerpt",
      "title": "src/capture/writer.ts",
      "file_path": "src/capture/writer.ts"
    },
    {
      "evidence_id": "EV:file-6",
      "type": "file_excerpt",
      "title": "src/cli/deep-consolidate.ts",
      "file_path": "src/cli/deep-consolidate.ts"
    },
    {
      "evidence_id": "EV:file-7",
      "type": "file_excerpt",
      "title": "src/cli/ingest-tokens.ts",
      "file_path": "src/cli/ingest-tokens.ts"
    },
    {
      "evidence_id": "EV:file-8",
      "type": "file_excerpt",
      "title": "src/cli/process-raw-sessions.ts",
      "file_path": "src/cli/process-raw-sessions.ts"
    },
    {
      "evidence_id": "EV:file-9",
      "type": "file_excerpt",
      "title": "src/config.ts",
      "file_path": "src/config.ts"
    },
    {
      "evidence_id": "EV:file-10",
      "type": "file_excerpt",
      "title": "src/consolidation/consolidator.ts",
      "file_path": "src/consolidation/consolidator.ts"
    },
    {
      "evidence_id": "EV:cisco-1",
      "type": "file_excerpt",
      "title": "INSTRUCTED_GIT_CLONE_AND_BUILD [MEDIUM] README.md:108: git clone https://github.com/openclaw/memento",
      "file_path": "README.md"
    },
    {
      "evidence_id": "EV:cisco-2",
      "type": "file_excerpt",
      "title": "MCP_EXFIL_CONVERSATION [HIGH] README.md:166: | `extraction.autoExtract` | `false` | **Opt-in**: automatically extract facts a",
      "file_path": "README.md"
    },
    {
      "evidence_id": "EV:cisco-3",
      "type": "file_excerpt",
      "title": "INSTRUCTED_GIT_CLONE_AND_BUILD [MEDIUM] SKILL.md:214: git clone https://github.com/braibaud/Memento",
      "file_path": "SKILL.md"
    },
    {
      "evidence_id": "EV:cisco-4",
      "type": "file_excerpt",
      "title": "SOCIAL_ENG_VAGUE_DESCRIPTION [LOW] SKILL.md:1: ---",
      "file_path": "SKILL.md"
    },
    {
      "evidence_id": "EV:cisco-5",
      "type": "file_excerpt",
      "title": "SOCIAL_ENG_ANTHROPIC_IMPERSONATION [MEDIUM] SKILL.md:16: when: \"Using anthropic/* models for extraction\"",
      "file_path": "SKILL.md"
    },
    {
      "evidence_id": "EV:cisco-6",
      "type": "file_excerpt",
      "title": "MCP_EXFIL_CONVERSATION [HIGH] SKILL.md:142: | `autoExtract` (default: `false`) | ⚠️ Yes, if cloud LLM | Sends conversation t",
      "file_path": "SKILL.md"
    },
    {
      "evidence_id": "EV:cisco-7",
      "type": "file_excerpt",
      "title": "COMMAND_INJECTION_EVAL [CRITICAL] src/cli/ingest-tokens.ts:58: db.exec(`",
      "file_path": "src/cli/ingest-tokens.ts"
    },
    {
      "evidence_id": "EV:cisco-8",
      "type": "file_excerpt",
      "title": "MCP_SQL_BLIND [HIGH] src/cli/process-raw-sessions.ts:158: function sleep(ms: number): Promise<void> {",
      "file_path": "src/cli/process-raw-sessions.ts"
    },
    {
      "evidence_id": "EV:cisco-9",
      "type": "file_excerpt",
      "title": "MCP_SQL_BLIND [HIGH] src/extraction/migrate.ts:281: function sleep(ms: number): Promise<void> {",
      "file_path": "src/extraction/migrate.ts"
    },
    {
      "evidence_id": "EV:cisco-10",
      "type": "file_excerpt",
      "title": "COMMAND_INJECTION_EVAL [CRITICAL] src/storage/db.ts:114: this.db.exec(ddl);",
      "file_path": "src/storage/db.ts"
    }
  ]
}