# Performing Active Directory Forest Trust Attack

> Use when enumerate and audit Active Directory forest trust relationships using impacket for SID filtering analysis, trust key extraction, cross-forest SID history abuse detection, and inter-realm Kerberos ticket assessment. Use when working with performing active directory forest trust attack.

- Skill: `oyi77/performing-active-directory-forest-trust-attack` (Agent Skill)
- Install (CLI): `npx skillmds add oyi77/performing-active-directory-forest-trust-attack`
- Raw SKILL.md: https://api.skillmd.com/api/skills/oyi77/performing-active-directory-forest-trust-attack/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Security
- License: Apache-2.0
- Author: oyi77 (https://skillmd.com/u/oyi77)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/oyi77/performing-active-directory-forest-trust-attack

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# Performing Active Directory Forest Trust Attack

## Overview

Active Directory forest trusts enable authentication across organizational boundaries but introduce attack surface if misconfigured. This skill uses impacket to enumerate trust relationships, analyze SID filtering configuration, detect SID history abuse vectors, perform cross-forest SID lookups via LSA/LSAT RPC calls, and assess inter-realm Kerberos ticket configurations for trust ticket forgery risks.


## When to Use
**Trigger phrases:**
- "performing active directory forest trust attack"
- "Enumerate and audit Active Directory forest trust relationships using impacket f"


- When conducting security assessments that involve performing active directory forest trust attack
- When following incident response procedures for related security events
- When performing scheduled security testing or auditing activities
- When validating security controls through hands-on testing

## Prerequisites

- Python 3.9+ with `impacket`, `ldap3`
- Domain credentials with read access to AD trust objects
- Network access to Domain Controllers (ports 389, 445, 88)
- Authorized penetration testing engagement or lab environment


> **Legal Notice:** This skill is for authorized security testing and educational purposes only. Unauthorized use against systems you do not own or have written permission to test is illegal and may violate computer fraud laws.

## Steps

```python
# Example: IOC detection
import re

IOC_PATTERNS = {
    "ip": r"\b(?:\d{1,3}\.){3}\d{1,3}\b",
    "domain": r"\b[a-z0-9-]+\.[a-z]{2,}\b",
    "hash_md5": r"\b[a-f0-9]{32}\b",
    "hash_sha256": r"\b[a-f0-9]{64}\b",
}

def extract_iocs(text: str) -> dict:
    return {k: re.findall(v, text) for k, v in IOC_PATTERNS.items()}
```

1. Enumerate forest trust relationships via LDAP trusted domain objects
2. Query trust attributes and SID filtering status for each trust
3. Perform SID lookups across trust boundaries using LsarLookupNames3
4. Enumerate foreign security principals in trusted domains
5. Check for SID history on cross-forest accounts
6. Assess trust direction and transitivity for lateral movement paths
7. Generate trust security audit report with risk findings

## Expected Output

- JSON report listing all trust relationships, SID filtering status, foreign principals, trust direction/transitivity, and risk assessment
- Cross-forest attack path analysis with remediation recommendations
## When NOT to Use

- You don't have explicit written authorization to test
- Task is about defense/detection, not offense (use detection skills)
- You need to implement security controls (use implementing-* skills)
- Task requires compliance auditing (use auditing-* skills)
- You're investigating an incident (use incident response skills)
- Target is out of scope for your engagement
- Task is about vulnerability scanning only (use scanning tools)


## Red Flags

- Performing actions without explicit written authorization from the asset owner
- Testing against production systems without a defined scope and rules of engagement
- Capturing traffic on networks without authorization or privacy considerations
- Leaving packet captures containing sensitive data unencrypted on disk
- Deploying inline blocking rules without testing for false positives first

## Process

1. **Reconnaissance** — Gather target information, identify attack surface, enumerate services
1. **Analysis/Exploitation** — Execute the technique, analyze results, document findings
1. **Reporting** — Document IOCs, write findings, provide remediation recommendations

## Verification

- All steps executed successfully against a test environment before production use
- Output documented with screenshots or logs demonstrating expected behavior
- Captures verified as complete with no dropped packets
- Detection rules tested against known-benign traffic for false positive rate
- Alert thresholds validated and tuned to reduce noise

## Anti-Rationalization Table

| Rationalization | Reality |
|---|---|
| "We are too small to be targeted" | Automated attacks target everyone. Size does not matter. |
| "Security slows us down" | A breach slows you down 100x more. Build security in from the start. |
| "We will fix it after launch" | Vulnerabilities in production are exploited within hours. Fix before deploy. |