Attack Tree Construction

Systematic attack path visualization and analysis.

When to Use This Skill

• Visualizing complex attack scenarios
• Identifying defense gaps and priorities
• Communicating risks to stakeholders
• Planning defensive investments
• Penetration test planning
• Security architecture review

Core Concepts

1. Attack Tree Structure

                    [Root Goal]
                         |
            ┌────────────┴────────────┐
            │                         │
       [Sub-goal 1]              [Sub-goal 2]
       (OR node)                 (AND node)
            │                         │
      ┌─────┴─────┐             ┌─────┴─────┐
      │           │             │           │
   [Attack]   [Attack]      [Attack]   [Attack]
    (leaf)     (leaf)        (leaf)     (leaf)

2. Node Types

3. Attack Attributes

Templates

Template 1: Attack Tree Data Model

from dataclasses import dataclass, field
from enum import Enum
from typing import List, Dict, Optional, Union
import json

class NodeType(Enum):
    OR = "or"
    AND = "and"
    LEAF = "leaf"


class Difficulty(Enum):
    TRIVIAL = 1
    LOW = 2
    MEDIUM = 3
    HIGH = 4
    EXPERT = 5


class Cost(Enum):
    FREE = 0
    LOW = 1
    MEDIUM = 2
    HIGH = 3
    VERY_HIGH = 4


class DetectionRisk(Enum):
    NONE = 0
    LOW = 1
    MEDIUM = 2
    HIGH = 3
    CERTAIN = 4


@dataclass
class AttackAttributes:
    difficulty: Difficulty = Difficulty.MEDIUM
    cost: Cost = Cost.MEDIUM
    detection_risk: DetectionRisk = DetectionRisk.MEDIUM
    time_hours: float = 8.0
    requires_insider: bool = False
    requires_physical: bool = False


@dataclass
class AttackNode:
    id: str
    name: str
    description: str
    node_type: NodeType
    attributes: AttackAttributes = field(default_factory=AttackAttributes)
    children: List['AttackNode'] = field(default_factory=list)
    mitigations: List[str] = field(default_factory=list)
    cve_refs: List[str] = field(default_factory=list)

    def add_child(self, child: 'AttackNode') -> None:
        self.children.append(child)

    def calculate_path_difficulty(self) -> float:
        """Calculate aggregate difficulty for this path."""
        if self.node_type == NodeType.LEAF:
            return self.attributes.difficulty.value

        if not self.children:
            return 0

        child_difficulties = [c.calculate_path_difficulty() for c in self.children]

        if self.node_type == NodeType.OR:
            return min(child_difficulties)
        else:  # AND
            return max(child_difficulties)

    def calculate_path_cost(self) -> float:
        """Calculate aggregate cost for this path."""
        if self.node_type == NodeType.LEAF:
            return self.attributes.cost.value

        if not self.children:
            return 0

        child_costs = [c.calculate_path_cost() for c in self.children]

        if self.node_type == NodeType.OR:
            return min(child_costs)
        else:  # AND
            return sum(child_costs)

    def to_dict(self) -> Dict:
        """Convert to dictionary for serialization."""
        return {
            "id": self.id,
            "name": self.name,
            "description": self.description,
            "type": self.node_type.value,
            "attributes": {
                "difficulty": self.attributes.difficulty.name,
                "cost": self.attributes.cost.name,
                "detection_risk": self.attributes.detection_risk.name,
                "time_hours": self.attributes.time_hours,
            },
            "mitigations": self.mitigations,
            "children": [c.to_dict() for c in self.children]
        }


@dataclass
class AttackTree:
    name: str
    description: str
    root: AttackNode
    version: str = "1.0"

    def find_easiest_path(self) -> List[AttackNode]:
        """Find the path with lowest difficulty."""
        return self._find_path(self.root, minimize="difficulty")

    def find_cheapest_path(self) -> List[AttackNode]:
        """Find the path with lowest cost."""
        return self._find_path(self.root, minimize="cost")

    def find_stealthiest_path(self) -> List[AttackNode]:
        """Find the path with lowest detection risk."""
        return self._find_path
how to use attack-tree-construction
CursorClaude CodeClineCodexWindsurfGooseGitHub CopilotVS CodeGemini CLIKiloOpencodeKimi CLIRoo ClineAiderContinueZedBolt.newLovablev0Replit AgentSweepSmol DeveloperDevinCavemanAmpAntigravity
How to use attack-tree-construction on Cursor
AI-first code editor with Composer
1
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
›Cursor installed and configured on your development machine
›Node.js version 16.0+ with npm package manager (verify with node --version)
›Active project directory or workspace where you want to add attack-tree-construction
2
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
$npx skills add https://github.com/wshobson/agents --skill attack-tree-constructioncopy
The skills CLI fetches attack-tree-construction from GitHub repository wshobson/agents and configures it for Cursor.
3
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
◆ Which agents do you want to install to?
│
│ ── Universal (.agents/skills) ── always included ────
│   • Amp
│   • Antigravity
│   • Cline
│   • Codex
│ ●Cursor(selected)
│   • Cursor
│   • Windsurf
4
Verify installation
Confirm successful installation by checking the skill directory location:
.cursor/skills/attack-tree-construction
Reload or restart Cursor to activate attack-tree-construction. Access the skill through slash commands (e.g., /attack-tree-construction) or your agent's skill management interface.
⚠
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
Additional Resources
›View source code on GitHub›Skills CLI documentation›Learn more about Cursor›What are agent skills?