AI-Assisted Penetration Testing: Weaponizing Claude, Grok, and Metasploit - Part 1: OSINT & Reconnaissance

Introduction: The New Attack Surface You Didn’t Know You Had

Three years ago, reconnaissance against your organization required specialized skills, expensive tools, and significant time investment. Today, a moderately skilled attacker with access to Claude, Grok, or similar AI systems can compress weeks of intelligence gathering into hours. The barrier to entry for sophisticated attacks has collapsed.

This series is written for CISOs and security leaders who need to understand—viscerally—how modern attackers operate. Not theoretical frameworks or abstract threat models, but concrete techniques you can replicate in your own authorized penetration testing engagements. Understanding the offense is prerequisite to building effective defense.

A critical note before we proceed: Everything described in this series should only be performed against systems you own or have explicit written authorization to test. Unauthorized access to computer systems is illegal under the Computer Fraud and Abuse Act (18 U.S.C. § 1030) and similar laws worldwide. This content is for educational and authorized security testing purposes only.

Why AI Changes Everything

Traditional OSINT and reconnaissance followed predictable patterns that blue teams learned to detect: sequential subdomain enumeration, systematic port scanning, methodical directory brute-forcing. The patterns were mechanical and identifiable.

AI-assisted reconnaissance is different. It’s adaptive, contextual, and creative. When I feed Claude or Grok information about a target, they don’t just execute predetermined steps—they reason about the target, identify non-obvious connections, and suggest attack vectors that automated tools would miss.

Consider the difference:

Traditional approach: Run subfinder, amass, and theHarvester. Parse outputs. Run nmap against discovered hosts. Feed results to vulnerability scanner.

AI-assisted approach: Describe the target organization to Claude. Let it reason about likely infrastructure patterns based on industry, size, and technology stack. Have it analyze job postings to infer internal tools. Cross-reference with breach databases. Generate custom wordlists based on organizational naming conventions. Design targeted phishing pretexts based on discovered employee information.

The AI doesn’t replace the tools—it orchestrates them intelligently and fills the gaps between automated outputs with human-like reasoning.

The Reconnaissance Stack

Before diving into techniques, let’s establish the toolkit. All tools mentioned are open-source and freely available.

AI Systems

Claude (Anthropic): Exceptional at analysis, synthesis, and generating structured outputs. Particularly strong at understanding organizational context and suggesting attack vectors.

Grok (xAI): Integrated with X (formerly Twitter) data, providing real-time social intelligence. Useful for tracking employee movements, organizational announcements, and sentiment analysis.

OSINT Tools

Exploitation Framework

Metasploit Framework: The industry-standard exploitation framework. We’ll use the open-source version (msfconsole) for vulnerability validation and exploitation in later parts of this series.

Phase 1: Passive Reconnaissance with AI Assistance

Passive reconnaissance gathers information without directly touching target systems. It’s invisible to the target and leaves no logs.

Step 1: Organizational Profiling

Start by asking your AI to build an organizational profile. This is where Claude and Grok shine.

Prompt to Claude:

I'm conducting an authorized penetration test against [Target Company].
They are a [industry] company with approximately [size] employees,
headquartered in [location].

Based on this profile, help me understand:
1. What technology stack would they likely use?
2. What cloud providers are common in this industry?
3. What third-party services/SaaS platforms would they probably integrate?
4. What regulatory frameworks do they need to comply with?
5. What would their likely attack surface look like?

Be specific and cite reasoning.

Claude will generate a detailed profile that guides subsequent enumeration. For a mid-size fintech company, it might reason:

“Given regulatory requirements (PCI-DSS, SOX, potentially state-specific regulations like NYDFS Part 500), they likely use enterprise-grade cloud infrastructure. AWS is most common for fintech, with Azure as secondary. Expect integrations with payment processors (Stripe, Plaid), identity providers (Okta, Auth0), and monitoring solutions (Datadog, Splunk). Their attack surface likely includes customer-facing web applications, mobile APIs, partner integration endpoints, and internal collaboration tools.”

This contextual understanding shapes everything that follows.

Step 2: Domain and Subdomain Enumeration

Now we enumerate the attack surface. Start with passive DNS reconnaissance:

# TheHarvester - broad OSINT collection
theHarvester -d target.com -b all -f target_harvester.html

# Amass - comprehensive subdomain enumeration
amass enum -passive -d target.com -o target_amass.txt

# Subfinder - fast passive enumeration
subfinder -d target.com -o target_subfinder.txt

# Combine and deduplicate
cat target_*.txt | sort -u > target_subdomains.txt

Feed the results to Claude for analysis:

Prompt to Claude:

Here are the subdomains discovered for [target.com]:

[paste subdomain list]

Analyze this list and:
1. Categorize by likely function (production, staging, dev, internal tools, etc.)
2. Identify potential high-value targets
3. Flag any that suggest security weaknesses (exposed admin panels, dev environments, etc.)
4. Suggest additional subdomains to probe based on naming patterns

Claude might respond with something like:

“Based on naming conventions, I can identify several concerning patterns:

High-Value Targets:

• api-v2.target.com - Production API, likely contains authentication endpoints
• admin.target.com - Administrative interface
• vault.target.com - Possibly secrets management

Security Concerns:

• staging-api.target.com - Staging environment on public DNS; may lack production security controls
• jenkins.target.com - CI/CD system; historically juicy target for supply chain attacks
• old-portal.target.com - Legacy system; likely running outdated software

Suggested Additional Targets Based on Patterns:

• api-v1.target.com (version pattern)
• dev-jenkins.target.com (environment prefix pattern)
• portal-admin.target.com (function suffix pattern)”

Step 3: Technology Stack Identification

Now probe discovered hosts for technology fingerprints:

# httpx for HTTP probing with technology detection
cat target_subdomains.txt | httpx -title -tech-detect -status-code -o target_httpx.txt

# Wappalyzer CLI for detailed technology fingerprinting
for subdomain in $(cat target_subdomains.txt); do
    wappalyzer https://$subdomain >> target_tech.json
done

Shodan and Censys provide additional intelligence without active scanning:

# Shodan - search by domain
shodan search hostname:target.com --fields ip_str,port,org,os,product

# Censys search - similar intelligence
censys search "target.com" --index-type hosts

Step 4: Certificate Transparency Analysis

Certificate Transparency logs are a goldmine. Every SSL certificate issued for your domains is publicly logged.

# crt.sh query
curl -s "https://crt.sh/?q=%.target.com&output=json" | jq -r '.[].name_value' | sort -u

# Censys certificate search
censys search "parsed.names: target.com" --index-type certificates

Feed the certificate data to Claude:

Prompt:

Here are the SSL certificates issued for target.com from CT logs:

[paste certificate data]

Analyze for:
1. Subdomains not discovered through DNS enumeration
2. Internal hostnames accidentally included in SAN entries
3. Certificate lifecycle issues (about to expire, already expired)
4. Patterns suggesting infrastructure changes or new projects
5. Wildcard certificates that might indicate broader attack surface

Step 5: Social Intelligence with Grok

Grok’s integration with X (Twitter) provides real-time social intelligence. Use it for:

Employee Enumeration:

Search for people who list [Target Company] as their employer.
Identify:
- Key technical staff (engineers, DevOps, security)
- Recent hires (may have weaker access controls understanding)
- People posting about technical challenges (reveals technology stack)
- Conference speakers from the company (often share technical details)

Technology Intelligence:

Find posts from [Target Company] employees mentioning:
- Technology they're implementing
- Problems they're solving
- Tools they're evaluating
- Frustrations with current systems

Timing Intelligence:

Identify patterns in employee posting times and locations.
This reveals:
- Working hours (for timing attacks)
- Remote work patterns
- Office locations
- Travel schedules

Phase 2: Building the Target Profile

With raw data collected, now we synthesize it into actionable intelligence.

The Attack Surface Map

Create a structured inventory of discovered assets:

## Target: ACME Financial Corp

### External Attack Surface

#### Web Applications
| Subdomain | Technology | Purpose | Risk Level |
|-----------|------------|---------|------------|
| www.acme.com | React, Nginx, AWS | Main website | Medium |
| app.acme.com | Angular, Node.js, Kubernetes | Customer portal | High |
| api.acme.com | Express, PostgreSQL | Customer API | Critical |
| partners.acme.com | WordPress 5.4 | Partner portal | High |

#### Infrastructure
| Asset | Details | Notes |
|-------|---------|-------|
| Mail Server | Microsoft 365 | SPF, DKIM, DMARC configured |
| DNS | Route53 | DNSSEC enabled |
| CDN | Cloudflare | WAF detected |
| CI/CD | GitHub Actions | Inferred from job postings |

#### Potential Entry Points
1. `staging-api.acme.com` - Development environment, likely weaker controls
2. `partners.acme.com` - WordPress site, version 5.4 has known vulnerabilities
3. `vpn.acme.com` - VPN endpoint, target for credential stuffing

Employee Intelligence Dossier

For authorized red team engagements, employee intelligence shapes social engineering attacks:

## Key Personnel

### IT/Security
- **John Smith** - CISO, LinkedIn shows recent Zero Trust initiative
- **Jane Doe** - Platform Engineer, GitHub shows Kubernetes expertise
- **Bob Johnson** - SOC Analyst, Twitter active, posts about tool frustrations

### High-Value Targets for Phishing
- **Finance Team** - Likely handles wire transfers
- **HR Team** - Access to employee PII
- **Executive Assistants** - Calendar access, travel details

### Technical Stack Indicators (from job postings)
- AWS (heavy), some GCP
- Kubernetes, ArgoCD
- PostgreSQL, Redis
- Python, Go, TypeScript
- Okta SSO
- Datadog monitoring

Asking Claude to Identify Attack Vectors

Now for the synthesis that makes AI invaluable. Feed your collected intelligence to Claude:

Prompt:

I'm conducting an authorized penetration test. Here is my reconnaissance data:

[Paste attack surface map and employee intelligence]

Based on this information:

1. Identify the top 5 most promising attack vectors, ranked by likelihood of success
2. For each vector, describe:
   - The specific vulnerability or weakness
   - Prerequisites for exploitation
   - Potential impact if successful
   - Detection difficulty for the defender
3. Suggest a phased attack approach, starting with lowest-risk reconnaissance
4. Identify any quick wins that might provide initial access
5. What information am I missing that would improve attack planning?

Claude will generate a prioritized attack plan that a skilled pentester would develop—but in minutes instead of hours.

Operational Security for Reconnaissance

Even passive reconnaissance has OPSEC considerations:

Avoid Detection:

• Use residential proxies or cloud VPS, not your corporate IP
• Rate-limit API queries to avoid triggering alerts
• Vary user agents and request patterns
• Use different exit nodes for different reconnaissance activities

Maintain Attribution Separation:

• Create dedicated accounts for OSINT
• Don’t link reconnaissance infrastructure to your organization
• Consider timing—avoid patterns that link to business hours

Document Everything:

• Screenshots with timestamps
• Raw data preservation
• Chain of custody for evidence

Key Takeaways for Part 1

1. AI amplifies reconnaissance capability by providing context, analysis, and creative attack vector identification that automated tools miss.

2. Passive reconnaissance is invisible to targets but reveals extensive information about technology stacks, employees, and potential vulnerabilities.

3. Certificate Transparency and job postings are underutilized intelligence sources that reveal infrastructure details organizations don’t realize they’re exposing.

4. Social intelligence from platforms like X provides real-time insight into organizational activities, employee movements, and technology decisions.

5. The combination of automated tools and AI analysis compresses weeks of manual reconnaissance into hours.

What’s Next

In Part 2, we’ll move from passive reconnaissance to active vulnerability discovery and exploitation. We’ll cover:

• Active scanning techniques with Nmap and Nuclei
• Using AI to analyze vulnerability scan results
• Metasploit fundamentals for authorized exploitation
• Web application testing with Burp Suite and AI assistance
• Chaining vulnerabilities for maximum impact

The goal: demonstrate how an attacker moves from reconnaissance data to initial access.

Lab Environment Setup

If you want to follow along with this series, set up a practice environment:

# Install Kali Linux (VM or bare metal)
# Contains most tools pre-installed

# Install additional tools
sudo apt update && sudo apt install -y \
    amass \
    subfinder \
    httpx \
    nuclei \
    feroxbuster

# Install SpiderFoot
pip3 install spiderfoot

# Verify Metasploit installation
msfconsole --version

# Set up vulnerable lab targets
# Options:
# - DVWA (Damn Vulnerable Web Application)
# - Metasploitable 2/3
# - VulnHub VMs
# - HackTheBox (online platform)

Critical Reminder: Never test against systems without explicit authorization. Use lab environments for practice.

In Part 2, we weaponize this intelligence. Read Here