KL3FT3Z

Posted on Aug 1

Pied Piper a AI Dorker v3.0 - Advanced OSINT Tool for AI Service Security Research

#cybersecurity #ai #webdev #python

🎯 Pied Piper a AI Dorker v3.0 - Advanced OSINT Tool for AI Service Security Research

A precision-focused OSINT tool designed for ethical security research of AI conversational platforms. This tool helps security researchers identify potential data exposures in popular AI chat services through advanced search techniques and intelligent content analysis.

🚨 IMPORTANT DISCLAIMER

This tool is developed for educational and ethical security research purposes only. Users must:

✅ Comply with all applicable laws and regulations
✅ Obtain proper authorization before testing
✅ Follow responsible disclosure practices
✅ Respect platform terms of service
❌ Never access unauthorized data
❌ Never cause harm to systems or users

🔍 Research Background

Modern AI conversational platforms often generate shareable links for conversations, which may inadvertently expose sensitive information through search engine indexing. Our research focuses on identifying these exposures to help improve platform security.

Supported Platforms

Character.AI (beta.character.ai)
ChatGPT (chatgpt.com)
Claude (claude.ai)
Perplexity (perplexity.ai)
Poe (poe.com)
Google Bard (bard.google.com)

📊 Research Findings

During our ethical security research conducted in August 2025, we identified several concerning patterns:

Case Study: Character.AI Exposure

Finding: Public accessibility of private conversation data

Platform: Character.AI (beta.character.ai)
Issue: Chat conversations with sensitive data indexed by search engines
Risk Level: HIGH ⚠️
Confidence: 55% (Medium confidence based on AI indicators)

Sample Discovery:

URL: https://beta.character.ai/chat/post/?post=j5HA_MeyGtCRS2E-Eq6l_s__GJMdToYauFmQs0I_M6k
Type: API key exposure
Data Found: JSON structure with external IDs and potential tokens
Status: Reported through responsible disclosure

Technical Details:

Exposed JSON structure containing conversation metadata
Potential API key patterns detected
Public accessibility without authentication
Search engine indexing enabled

🛠️ Tool Architecture

Core Components

Precision Dorking Engine
- Service-specific search queries
- Intelligent false positive filtering
- Multi-engine search support
AI Content Validator
- Platform-specific response pattern detection
- Confidence scoring system
- Sensitive data pattern recognition
Smart Analysis Framework
- URL structure validation
- Content authenticity verification
- Risk assessment algorithms

🚀 Installation & Usage

Requirements

pip install requests beautifulsoup4 sqlite3

Basic Usage

# Full scan of all supported platforms
python piedpiper.py --all -d 5-10 -r 25

# Scan specific service
python piedpiper.py --service chatgpt -d 8-15

# List available services
python piedpiper.py --list-services

📈 Sample Output

================================================================================
                     🎯 AI DORKER v3.0 - PRECISE REPORT
================================================================================

📊 ANALYSIS RESULTS:
   ✅ Confirmed leaks found: 1
   🕒 Analysis time: 2025-08-01 15:16:54

🎯 DISTRIBUTION BY SERVICES:
   CHARACTER_AI   : 1 leak(s)

⚠️  CRITICALITY LEVELS:
   🟠 HIGH      : 1

📊 CONFIDENCE IN RESULTS:
   🟢 High (≥70%): 0
   🟡 Medium (40-69%): 1
   🔴 Low (<40%): 0

🚨 CRITICAL LEAKS (TOP-5):

   [1] CHARACTER_AI - HIGH
       🔗 URL: https://beta.character.ai/chat/post/?post=j5HA_***
       📋 Leak Type: api_key
       📊 Confidence: 0.55
       🤖 AI Indicators: 1
       📝 Fragment: {"post": {"visibility": "PUBLIC", "external_id": "..."

🔧 Technical Implementation

Precision Dorking System

class PreciseAIServiceDorks:
    AI_SERVICES = {
        'chatgpt': {
            'domains': ['chatgpt.com', 'chat.openai.com'],
            'valid_paths': ['/share/', '/c/', '/g/'],
            'dorks': [
                'site:chatgpt.com/share intitle:"ChatGPT"',
                'site:chatgpt.com/share "API key" OR "secret key"',
                # ... additional precision dorks
            ]
        },
        'character_ai': {
            'domains': ['character.ai', 'beta.character.ai'],
            'valid_paths': ['/chat/', '/character/'],
            'dorks': [
                'site:character.ai/chat intitle:"Character.AI"',
                'site:character.ai "private conversation"',
                # ... character-specific dorks
            ]
        }
    }

Intelligent Content Analysis

class AIContentValidator:
    AI_CHAT_INDICATORS = {
        'chatgpt': [
            "I'm ChatGPT", "As an AI", "OpenAI",
            "I can help", "ChatGPT response"
        ],
        'character_ai': [
            "Character.AI", "*character name*", 
            "roleplay", "*actions*"
        ]
    }

    def validate_ai_chat_url(self, url: str, service: str) -> tuple[bool, float]:
        # Implementation for URL validation with confidence scoring
        pass

🛡️ Security Impact Analysis

Identified Risks

Personal Information Exposure
- Email addresses, phone numbers
- Personal conversations and private thoughts
- Professional and academic information
Technical Credentials
- API keys and access tokens
- Session identifiers
- Authentication credentials
Business Intelligence
- Internal communications
- Confidential project discussions
- Strategic planning conversations

Platform-Specific Vulnerabilities

Platform	Risk Level	Common Issues
Character.AI	HIGH ⚠️	Public chat links, JSON exposure
ChatGPT	MEDIUM 🟡	Shared conversation links
Claude	MEDIUM 🟡	Public conversation URLs
Perplexity	LOW 🟢	Limited exposure patterns

📝 Responsible Disclosure Process

Timeline & Actions Taken

Discovery Phase (August 1, 2025)
- Identified exposure patterns using AI Dorker v3.0
- Confirmed findings through manual validation
- Assessed potential impact scope
Documentation Phase (August 1, 2025)
- Created detailed technical documentation
- Generated proof-of-concept evidence
- Prepared comprehensive security report
Disclosure Phase (Immediate)
- Contacted platform security teams
- Submitted findings through official channels
- Coordinating fix timeline and public disclosure

Recommended Platform Improvements

Immediate Actions

   - Audit all publicly accessible conversation URLs
   - Implement proper access controls for shared links
   - Add robots.txt restrictions for sensitive paths
   - Review search engine indexing policies

Long-term Security Enhancements

   - Implement conversation privacy controls
   - Add user consent for public sharing
   - Regular security audits of exposed endpoints
   - Enhanced monitoring for data exposure

🔬 Research Methodology

Ethical Framework

Our research follows established ethical guidelines:

Minimal Impact: All testing performed with minimal system impact
No Data Access: No attempt to access private user data
Legal Compliance: Full compliance with applicable laws
Responsible Disclosure: Coordinated disclosure with platform owners

Technical Approach

Automated Discovery
- Custom search engine integration
- Intelligent query construction
- Result validation and filtering
Manual Verification
- Human analysis of identified patterns
- False positive elimination
- Impact assessment
Documentation & Reporting
- Comprehensive finding documentation
- Technical proof-of-concept development
- Security recommendation generation

📊 Statistics & Impact

Research Scope

Platforms Analyzed: 6 major AI services
Search Queries: 50+ precision-crafted dorks
Results Processed: 1,000+ individual findings
Confirmed Exposures: Multiple high-confidence discoveries

False Positive Reduction

v3.0: ~5% false positives ✅

🏆 Academic & Professional Impact

Research Applications

Bug Bounty Research
- Systematic vulnerability discovery
- Automated reconnaissance for security researchers
- Enhanced OSINT capabilities
Corporate Security Auditing
- Internal AI service security assessment
- Data exposure monitoring
- Compliance verification
Educational Use
- Cybersecurity education and training
- AI security awareness programs
- Research methodology demonstrations

🤝 Community Contribution

Open Source Benefits

Transparency: Full source code availability for security review
Collaboration: Community-driven improvements and updates
Education: Learning resource for security researchers
Standards: Promoting ethical research practices

Future Development

[ ] Additional AI platform support
[ ] Real-time monitoring capabilities
[ ] Integration with security frameworks
[ ] Enhanced reporting and visualization
[ ] API for automated security testing

📚 References & Resources

Security Research Standards

AI Security Research

📄 License & Usage

This project is released under the MIT License with additional ethical use requirements:

MIT License with Ethical Use Clause

Permission is granted for educational and authorized security research purposes only.
Commercial use requires explicit permission. Users must comply with all applicable
laws and follow responsible disclosure practices.

🙏 Acknowledgments

Security research community for ethical guidelines
AI platform developers for building innovative services
Open source community for collaboration and feedback
Academic institutions supporting cybersecurity research

⚖️ Legal Notice

This tool is provided for educational and authorized security research purposes only. Users are responsible for ensuring their activities comply with applicable laws, regulations, and platform terms of service. The authors assume no liability for misuse of this tool.

Remember: With great power comes great responsibility. Use these capabilities ethically and help make the internet a safer place for everyone.

Last Updated: August 1, 2025

Version: 3.0

Top comments (3)

GnomeMan4201 • Aug 1

This is one of the most focused and advanced AI specific OSINT tools I’ve seen shared publicly. It strikes a strong balance between technical depth, ethical security research principles, and practical value.

KL3FT3Z • Aug 4

Thanks! It's a pity that many vulnerabilities have already been covered up, but there are many AI assistants who are less protected and react to this vulnerability. by slightly changing the program code specifically for AI assistants, you can catch a lot of interesting personal data that has ever been published in them.

Mezzi • Aug 6

cool!