Attack Trees & Attack Feasibility Rating

#!/usr/bin/env python3
# Attack tree feasibility aggregation per ISO/SAE 21434 Annex B

from dataclasses import dataclass
from typing import List, Optional

@dataclass
class AttackNode:
    name: str
    gate: str           # "AND", "OR", or "LEAF"
    afr_score: Optional[int] = None   # LEAF nodes only
    children: List = None

    def __post_init__(self):
        if self.children is None:
            self.children = []

def compute_feasibility(node: AttackNode) -> int:
    if node.gate == "LEAF":
        return node.afr_score

    child_scores = [compute_feasibility(c) for c in node.children]

    if node.gate == "OR":
        # Attacker picks easiest (lowest score = highest feasibility)
        return min(child_scores)
    elif node.gate == "AND":
        # Attacker must complete all steps → sum scores
        return sum(child_scores)

def score_to_level(score: int) -> str:
    if score <= 13: return "High"
    if score <= 19: return "Medium"
    if score <= 24: return "Low"
    return "Very Low"

# Example: Remote CAN injection attack tree
path_b = AttackNode("Path B: LTE exploit", "AND", children=[
    AttackNode("B1: Find open port", "LEAF", afr_score=2),   # ET=1d(0)+SE=layman(0)+KT=public(0)+WoO=easy(1)+EQ=std(0)=1
    AttackNode("B2: Buffer overflow in cellular stack", "LEAF", afr_score=10),
])

path_c = AttackNode("Path C: OBD-II physical", "LEAF", afr_score=1)  # trivially easy

root = AttackNode("Remote CAN Injection", "OR", children=[path_b, path_c])

total = compute_feasibility(root)
print(f"Root attack feasibility: score={total} → {score_to_level(total)}")
print(f"  Path B score: {compute_feasibility(path_b)} → {score_to_level(compute_feasibility(path_b))}")
print(f"  Path C score: {compute_feasibility(path_c)} → {score_to_level(compute_feasibility(path_c))}")