---
name: attack-tree-construction
description: "Build comprehensive attack trees to visualize threat paths. Use when mapping attack scenarios, identifying defense gaps, or communicating security risks to stakeholders."
risk: unknown
source: community
date_added: "2026-02-27"
---

# Attack Tree Construction

Systematic attack path visualization and analysis.

## Use this skill when

- Visualizing complex attack scenarios
- Identifying defense gaps and priorities
- Communicating risks to stakeholders
- Planning defensive investments or test scopes

## Do not use this skill when

- You lack authorization or a defined scope to model the system
- The task is a general risk review without attack-path modeling
- The request is unrelated to security assessment or design

## Instructions

- Confirm scope, assets, and the attacker goal for the root node.
- Decompose into sub-goals with AND/OR structure.
- Annotate leaves with cost, skill, time, and detectability.
- Map mitigations per branch and prioritize high-impact paths.
- If detailed templates are required, open `resources/implementation-playbook.md`.

## Safety

- Share attack trees only with authorized stakeholders.
- Avoid including sensitive exploit details unless required.

## Resources

- `resources/implementation-playbook.md` for detailed patterns, templates, and examples.

---

<!-- AGI-INTEGRATION-START -->

## AGI Framework Integration

> **Adapted for [@techwavedev/agi-agent-kit](https://www.npmjs.com/package/@techwavedev/agi-agent-kit)**
> Original source: [antigravity-awesome-skills](https://github.com/sickn33/antigravity-awesome-skills)

### Memory-First Protocol

Cache compliance check results to avoid re-running expensive AWS API calls. Retrieve prior audit findings to track remediation progress across sessions.

```bash
# Check for prior security context before starting
python3 execution/memory_manager.py auto --query "prior security audit results for Attack Tree Construction"
```

### Storing Results

After completing work, store security decisions for future sessions:

```bash
python3 execution/memory_manager.py store \
  --content "Audit findings: 3 critical IAM misconfigurations found and remediated" \
  --type technical --project <project> \
  --tags attack-tree-construction security
```

### Multi-Agent Collaboration

Share security findings with other agents so they avoid introducing vulnerabilities in their code changes.

```bash
python3 execution/cross_agent_context.py store \
  --agent "<your-agent>" \
  --action "Completed security audit — 3 critical findings fixed, compliance score 94%" \
  --project <project>
```

### Signed Audit Trail

All security findings are cryptographically signed with the agent's Ed25519 identity, providing tamper-proof audit logs for compliance reporting.

### Semantic Cache for Compliance

Cache compliance check results (`semantic_cache.py`) to avoid redundant AWS API calls. Cache hit at similarity >0.92 returns prior results instantly.

<!-- AGI-INTEGRATION-END -->
