{
  "capability_id": "disler-hooks-mastery",
  "capability_type": "plugin",
  "status": "FINDINGS",
  "methodology_version": "1.0",
  "categories_tested": [
    "prompt_injection_chains",
    "privilege_escalation",
    "data_exfiltration_side_channels",
    "capability_squatting",
    "context_poisoning",
    "dependency_confusion"
  ],
  "test_counts": {
    "patterns_analyzed": 29
  },
  "findings": [
    {
      "id": "ADV-2026-0001",
      "category": "prompt_injection_chains",
      "capability_tested": "disler-hooks-mastery",
      "capability_version": "latest",
      "capability_type": "plugin",
      "severity": "high",
      "classification": "warn",
      "status": "confirmed",
      "summary": "Multiple hook scripts process user input without explicit data boundaries",
      "description": "Several hook scripts directly incorporate user-provided content (prompts, tool inputs, file paths) into their processing logic without explicit data boundary markers. The user_prompt_submit.py hook processes raw user prompts, and pre_tool_use.py processes tool inputs that could contain instruction-like content. While these hooks have some validation, they don't explicitly mark user content as data-only, creating potential injection surfaces.",
      "reproduction": {
        "steps": [
          "Analyze skill instructions against adversarial taxonomy"
        ],
        "inputs_used": [],
        "observed_behavior": "From user_prompt_submit.py: processes 'prompt' field directly. From pre_tool_use.py: processes 'tool_input' parameters. From multiple hooks: user content is logged and processed without explicit data boundary markers like 'USER_DATA:' or similar framing.",
        "expected_behavior": "No adversarial patterns in skill instructions"
      },
      "impact": "Several hook scripts directly incorporate user-provided content (prompts, tool inputs, file paths) into their processing logic without explicit data boundary markers. The user_prompt_submit.py hook processes raw user prompts, and pre_tool_use.py processes tool inputs that could contain instruction-like content. While these hooks have some validation, they don't explicitly mark user content as data-only, creating potential injection surfaces.",
      "recommendation": "Add explicit data boundary markers around all user-provided content. Prefix user inputs with clear data markers like 'USER_DATA:' or wrap in structured delimiters. Implement input sanitization that strips instruction-like patterns before processing.",
      "tested_at": "2026-04-01T03:00:54.343Z",
      "tested_by": "llm_assisted",
      "added_to_library": false
    },
    {
      "id": "ADV-2026-0002",
      "category": "prompt_injection_chains",
      "capability_tested": "disler-hooks-mastery",
      "capability_version": "latest",
      "capability_type": "plugin",
      "severity": "high",
      "classification": "warn",
      "status": "confirmed",
      "summary": "Hook scripts execute shell commands and file operations with user-controlled parameters",
      "description": "Multiple hook scripts execute system commands and file operations using user-provided content as parameters. The pre_tool_use.py hook processes bash commands from user input, and various hooks perform file operations with user-controlled paths. While there is some validation for dangerous patterns, the parameter injection surface remains significant, especially for path traversal and command injection through tool parameters.",
      "reproduction": {
        "steps": [
          "Analyze skill instructions against adversarial taxonomy"
        ],
        "inputs_used": [],
        "observed_behavior": "From pre_tool_use.py: processes bash commands with user input. From multiple hooks: file operations using user-provided paths like 'tool_input' parameters. Shell command execution via 'uv run' with user-controlled script paths and parameters.",
        "expected_behavior": "No adversarial patterns in skill instructions"
      },
      "impact": "Multiple hook scripts execute system commands and file operations using user-provided content as parameters. The pre_tool_use.py hook processes bash commands from user input, and various hooks perform file operations with user-controlled paths. While there is some validation for dangerous patterns, the parameter injection surface remains significant, especially for path traversal and command injection through tool parameters.",
      "recommendation": "Implement strict parameter validation and sanitization. Use parameterized command execution instead of string concatenation. Validate and sanitize all file paths to prevent directory traversal. Consider using allowlists for permitted commands and file operations rather than blocklists for dangerous ones.",
      "tested_at": "2026-04-01T03:00:54.343Z",
      "tested_by": "llm_assisted",
      "added_to_library": false
    },
    {
      "id": "ADV-2026-0003",
      "category": "capability_squatting",
      "capability_tested": "disler-hooks-mastery",
      "capability_version": "latest",
      "capability_type": "plugin",
      "severity": "high",
      "classification": "warn",
      "status": "confirmed",
      "summary": "Hook scripts contain conditional logic based on system state and environment variables",
      "description": "Multiple hook scripts contain conditional logic that references system state the user wouldn't anticipate, including environment variables (ENGINEER_NAME, ELEVENLABS_API_KEY, OPENAI_API_KEY), file existence checks, and system capabilities. These conditionals can alter behavior based on hidden system state rather than user-visible inputs.",
      "reproduction": {
        "steps": [
          "Analyze skill instructions against adversarial taxonomy"
        ],
        "inputs_used": [],
        "observed_behavior": "Examples include: 'if os.getenv(\"ENGINEER_NAME\")' in notification.py, 'if os.getenv(\"ELEVENLABS_API_KEY\")' in multiple TTS scripts, 'if shutil.which(\"git\")' in session_start.py, and various file existence checks like 'if os.path.exists(\".env\")' that modify behavior based on system state",
        "expected_behavior": "No adversarial patterns in skill instructions"
      },
      "impact": "Multiple hook scripts contain conditional logic that references system state the user wouldn't anticipate, including environment variables (ENGINEER_NAME, ELEVENLABS_API_KEY, OPENAI_API_KEY), file existence checks, and system capabilities. These conditionals can alter behavior based on hidden system state rather than user-visible inputs.",
      "recommendation": "Document all conditional behaviors in the plugin description and make environment-dependent features opt-in rather than automatic. Clearly communicate what system state affects plugin behavior.",
      "tested_at": "2026-04-01T03:00:54.343Z",
      "tested_by": "llm_assisted",
      "added_to_library": false
    },
    {
      "id": "ADV-2026-0004",
      "category": "capability_squatting",
      "capability_tested": "disler-hooks-mastery",
      "capability_version": "latest",
      "capability_type": "plugin",
      "severity": "high",
      "classification": "review",
      "status": "confirmed",
      "summary": "Plugin contains significantly more components than typical for educational content",
      "description": "The plugin declares 6 agents, 15 commands, 1 hook, and 35 scripts totaling 57 components, which is unusually large for a 'mastery' tutorial. This volume could obscure malicious content within legitimate educational material, making thorough review difficult.",
      "reproduction": {
        "steps": [
          "Analyze skill instructions against adversarial taxonomy"
        ],
        "inputs_used": [],
        "observed_behavior": "Plugin metadata shows: '6 agent(s), 15 command(s), 1 hook(s), 35 script(s)' - this is an exceptionally large number of components for what appears to be educational content about Claude Code hooks",
        "expected_behavior": "No adversarial patterns in skill instructions"
      },
      "impact": "The plugin declares 6 agents, 15 commands, 1 hook, and 35 scripts totaling 57 components, which is unusually large for a 'mastery' tutorial. This volume could obscure malicious content within legitimate educational material, making thorough review difficult.",
      "recommendation": "Consider breaking this into smaller, focused plugins. If the large scope is intentional, provide clear documentation explaining why so many components are necessary for the educational purpose.",
      "tested_at": "2026-04-01T03:00:54.343Z",
      "tested_by": "llm_assisted",
      "added_to_library": false
    }
  ],
  "summary": {
    "patterns_analyzed": 29,
    "categories_tested": [
      "prompt_injection_chains",
      "privilege_escalation",
      "data_exfiltration_side_channels",
      "capability_squatting",
      "context_poisoning",
      "dependency_confusion"
    ],
    "findings_count": 4,
    "findings_by_severity": {
      "critical": 0,
      "high": 4,
      "medium": 0,
      "low": 0,
      "informational": 0
    },
    "findings_by_classification": {
      "block": 0,
      "warn": 3,
      "review": 1,
      "info": 0
    },
    "findings_deduplicated": 0,
    "duration_ms": 33797,
    "api_usage": {
      "total_input_tokens": 60511,
      "total_output_tokens": 1087,
      "calls": 6
    }
  }
}