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Product Requirements Document: AI Agent Security Reconnaissance Tool (AASRT)

Version: 1.0
Date: February 8, 2026
Author: AGK
Document Type: Comprehensive Technical Specification

1. EXECUTIVE SUMMARY

1.1 Product Vision

Develop a Python-based security reconnaissance tool that automates the discovery of publicly exposed AI agent implementations (ClawdBot, AutoGPT, LangChain agents, etc.) with potential security misconfigurations across the internet using multiple search engines and APIs.

1.2 Problem Statement

Security researchers and organizations currently lack automated tools to:

  • Identify exposed AI agent instances at scale
  • Assess the security posture of AI agent deployments
  • Monitor for misconfigurations that expose sensitive data
  • Track the proliferation of vulnerable AI agent implementations

1.3 Target Audience

  • Security researchers and penetration testers
  • Bug bounty hunters
  • DevSecOps teams auditing AI deployments
  • Cybersecurity educators and students
  • Incident response teams

1.4 Success Metrics

  • Ability to discover 90%+ of publicly indexed vulnerable instances
  • Query execution time < 30 seconds per search engine
  • False positive rate < 15%
  • Support for 5+ search engines/data sources
  • Exportable reports in multiple formats (JSON, CSV, HTML, PDF)

2. LEGAL & ETHICAL FRAMEWORK

2.1 Compliance Requirements

MANDATORY IMPLEMENTATION:

  • Tool operates in passive reconnaissance mode only (no active exploitation)
  • Strict adherence to search engine Terms of Service
  • Rate limiting to prevent API abuse
  • Legal disclaimer displayed on every execution
  • User consent required before execution
  • Logging of all queries for accountability

2.2 Responsible Disclosure

  • Built-in responsible disclosure workflow
  • Templates for notifying affected parties
  • Integration with CVE/vulnerability databases
  • Option to anonymize findings before reporting

2.3 Terms of Service Compliance

Per Search Engine:

  • Shodan: Max 1 query/second, API key required
  • Censys: Respect rate limits (120 requests/5 minutes)
  • ZoomEye: Authentication required, rate limits enforced
  • Google Dorking: Robots.txt compliance, no automated queries without approval
  • GitHub: API rate limits, token-based authentication

2.4 Legal Disclaimer Template

WARNING: This tool is for authorized security research and defensive purposes only.
Unauthorized access to computer systems is illegal under CFAA (US), Computer Misuse Act (UK),
and similar laws worldwide. Users are responsible for ensuring lawful use.
By proceeding, you acknowledge:
1. You have authorization to scan target systems
2. You will comply with all applicable laws and terms of service
3. You will responsibly disclose findings
4. You will not exploit discovered vulnerabilities

3. FUNCTIONAL REQUIREMENTS

3.1 Core Features

3.1.1 Multi-Source Search Integration

Requirement ID: FR-001
Priority: P0 (Critical)

Description: Integrate with multiple search engines and threat intelligence platforms to maximize discovery coverage.

Supported Data Sources:

  1. Shodan

    • API Endpoint: https://api.shodan.io/shodan/host/search
    • Authentication: API Key (user-provided)
    • Search capabilities: Port scanning, service fingerprinting, banner grabbing
    • Rate limit: 1 query/second (enforce client-side)
    • Data returned: IP, port, hostname, service, banner, SSL cert info

  2. Censys

    • API Endpoint: https://search.censys.io/api/v2/hosts/search
    • Authentication: API ID + Secret
    • Search capabilities: Host search, certificate search
    • Rate limit: 120 requests per 5 minutes
    • Data returned: IP, services, protocols, certificates, autonomous system info

  3. ZoomEye

    • API Endpoint: https://api.zoomeye.org/host/search
    • Authentication: JWT token
    • Search capabilities: Device fingerprinting, service identification
    • Rate limit: Variable by account type
    • Data returned: IP, port, service, device info, location

  4. GitHub Code Search

    • API Endpoint: https://api.github.com/search/code
    • Authentication: Personal Access Token
    • Search capabilities: Code pattern matching, configuration file discovery
    • Rate limit: 30 requests/minute (authenticated)
    • Data returned: Repository, file path, code snippets, commit history

  5. Google Custom Search (Optional)

    • API Endpoint: https://customsearch.googleapis.com/customsearch/v1
    • Authentication: API Key + Search Engine ID
    • Search capabilities: Dork-based discovery
    • Rate limit: 100 queries/day (free tier)
    • Data returned: URLs, snippets, page metadata

  6. GreyNoise

    • API Endpoint: https://api.greynoise.io/v3/community/
    • Authentication: API Key
    • Purpose: Filter out internet scanners/noise
    • Rate limit: Variable by tier
    • Data returned: Classification (benign, malicious, unknown)

Input Parameters:

{
    "search_engines": ["shodan", "censys", "zoomeye", "github"],
    "query_templates": {
        "shodan": 'product:"ClawdBot" http.title:"ClawdBot"',
        "censys": 'services.http.response.body: "ClawdBot"',
        "github": '"ANTHROPIC_API_KEY" filename:.env'
    },
    "max_results_per_engine": 100,
    "enable_rate_limiting": true
}

Output Format:

{
    "scan_id": "uuid-v4",
    "timestamp": "ISO-8601",
    "source": "shodan",
    "results": [
        {
            "ip": "192.0.2.1",
            "port": 8080,
            "hostname": "example.com",
            "service": "http",
            "banner": "ClawdBot/1.2.3",
            "vulnerability_indicators": [
                "no_authentication",
                "exposed_api_keys",
                "shell_access_enabled"
            ],
            "risk_score": 9.2,
            "metadata": {}
        }
    ],
    "total_results": 42,
    "query_executed": "product:ClawdBot"
}

3.1.2 Search Query Templates

Requirement ID: FR-002
Priority: P0 (Critical)

Description: Pre-built, optimized search queries for discovering vulnerable AI agent implementations.

Query Categories:

Category 1: Exposed AI Agent Dashboards

QUERY_TEMPLATES = {
    "clawdbot_instances": {
        "shodan": [
            'http.title:"ClawdBot Dashboard"',
            'http.html:"ClawdBot" port:3000',
            'product:"ClawdBot"'
        ],
        "censys": [
            'services.http.response.html_title: "ClawdBot"',
            'services.http.response.body: "anthropic"'
        ]
    },
    
    "autogpt_instances": {
        "shodan": [
            'http.title:"Auto-GPT"',
            'http.html:"autogpt" port:8000'
        ]
    },
    
    "langchain_agents": {
        "shodan": [
            'http.html:"langchain" http.html:"agent"',
            'product:"LangChain"'
        ]
    },
    
    "openai_playground_exposed": {
        "shodan": [
            'http.title:"OpenAI Playground"',
            'http.html:"sk-" http.html:"openai"'
        ]
    }
}

Category 2: Exposed Configuration Files

CONFIG_EXPOSURE_QUERIES = {
    "github": [
        '"ANTHROPIC_API_KEY" extension:env',
        '"OPENAI_API_KEY" extension:json',
        '"claude-" AND "sk-ant-" filename:.env',
        'path:.env "API_KEY"',
        'filename:config.json "anthropic"'
    ],
    
    "google_dorks": [
        'inurl:.env "ANTHROPIC_API_KEY"',
        'filetype:json "openai_api_key"',
        'intitle:"index of" .env'
    ]
}

Category 3: Vulnerable Service Patterns

VULNERABILITY_PATTERNS = {
    "no_authentication": {
        "shodan": 'http.status:200 "Authorization" -http.headers:"www-authenticate"',
        "censys": 'services.http.response.headers.www_authenticate: ""'
    },
    
    "exposed_api_endpoints": {
        "shodan": [
            'http.html:"/api/messages"',
            'http.html:"/api/execute"',
            'http.html:"/shell"'
        ]
    },
    
    "debug_mode_enabled": {
        "shodan": [
            'http.html:"DEBUG=True"',
            'http.html:"development mode"'
        ]
    }
}

Query Customization: Users should be able to:

  • Add custom queries via configuration file
  • Combine multiple query patterns (AND/OR logic)
  • Use regex patterns for flexible matching
  • Save favorite queries for reuse
  • Import/export query sets

3.1.3 Vulnerability Assessment Engine

Requirement ID: FR-003
Priority: P0 (Critical)

Description: Analyze discovered instances for specific vulnerability indicators and assign risk scores.

Vulnerability Checks:

VULNERABILITY_CHECKS = {
    "authentication": {
        "check_no_auth": {
            "method": "http_request",
            "endpoint": "/",
            "expected_status": [401, 403],
            "vulnerability_if": [200],
            "severity": "CRITICAL",
            "cvss_score": 9.1,
            "description": "No authentication required for access"
        },
        
        "check_default_credentials": {
            "method": "credential_test",
            "credentials": [
                {"username": "admin", "password": "admin"},
                {"username": "root", "password": "root"}
            ],
            "severity": "HIGH",
            "cvss_score": 8.5
        }
    },
    
    "information_disclosure": {
        "check_exposed_env": {
            "method": "path_check",
            "paths": ["/.env", "/config.json", "/.git/config"],
            "severity": "CRITICAL",
            "cvss_score": 9.8,
            "description": "Environment files publicly accessible"
        },
        
        "check_api_key_exposure": {
            "method": "regex_scan",
            "patterns": [
                r"sk-ant-[a-zA-Z0-9-_]{95}",  # Anthropic API key
                r"sk-[a-zA-Z0-9]{48}",          # OpenAI API key
                r"AKIA[0-9A-Z]{16}"              # AWS Access Key
            ],
            "severity": "CRITICAL",
            "cvss_score": 10.0
        }
    },
    
    "dangerous_functionality": {
        "check_shell_access": {
            "method": "endpoint_check",
            "endpoints": ["/shell", "/exec", "/api/execute"],
            "severity": "CRITICAL",
            "cvss_score": 9.9,
            "description": "Shell command execution accessible"
        },
        
        "check_file_upload": {
            "method": "endpoint_check",
            "endpoints": ["/upload", "/api/files"],
            "test_upload": false,  # Passive only
            "severity": "HIGH",
            "cvss_score": 7.8
        }
    },
    
    "network_exposure": {
        "check_public_ip": {
            "method": "network_check",
            "verify": "public_internet_accessible",
            "severity": "MEDIUM",
            "cvss_score": 6.5
        },
        
        "check_cloudflare": {
            "method": "header_check",
            "headers": ["cf-ray", "server"],
            "protected_if": "cloudflare_present",
            "severity": "INFO"
        }
    }
}

Risk Scoring Algorithm:

def calculate_risk_score(vulnerabilities):
    """
    Calculate overall risk score (0-10) based on discovered vulnerabilities.
    
    Formula:
    - Base score: Highest CVSS score found
    - Multiplier: 1.0 + (0.1 * number_of_critical_vulns)
    - Cap at 10.0
    """
    base_score = max([v['cvss_score'] for v in vulnerabilities])
    critical_count = len([v for v in vulnerabilities if v['severity'] == 'CRITICAL'])
    
    risk_score = min(base_score * (1.0 + (0.1 * critical_count)), 10.0)
    
    return {
        "overall_score": round(risk_score, 1),
        "severity_breakdown": {
            "critical": len([v for v in vulnerabilities if v['severity'] == 'CRITICAL']),
            "high": len([v for v in vulnerabilities if v['severity'] == 'HIGH']),
            "medium": len([v for v in vulnerabilities if v['severity'] == 'MEDIUM']),
            "low": len([v for v in vulnerabilities if v['severity'] == 'LOW'])
        },
        "exploitability": "HIGH" if critical_count >= 2 else "MEDIUM"
    }

3.1.4 Data Enrichment

Requirement ID: FR-004
Priority: P1 (High)

Description: Enrich discovered instances with additional context and threat intelligence.

Enrichment Sources:

  1. WHOIS Lookup

    • Domain registration details
    • Registrant information (if public)
    • Creation/expiration dates
    • Nameservers

  2. Geolocation

    • Country, city, region
    • ISP/hosting provider
    • ASN (Autonomous System Number)
    • Coordinates

  3. SSL/TLS Certificate Analysis

    • Certificate validity
    • Issuer information
    • Subject Alternative Names (SANs)
    • Expiration date
    • Self-signed detection

  4. DNS Records

    • A, AAAA, MX, TXT records
    • Subdomain enumeration (passive)
    • DNS security (DNSSEC, CAA)

  5. Historical Data

    • First seen timestamp
    • Changes over time
    • Archive.org Wayback Machine lookups

  6. Threat Intelligence Integration

    • GreyNoise classification
    • AbuseIPDB reputation score
    • Blocklist presence
    • Known malicious infrastructure

Enrichment Output:

{
    "target": "192.0.2.1",
    "enrichment": {
        "whois": {
            "domain": "example.com",
            "registrar": "GoDaddy",
            "creation_date": "2020-01-15",
            "registrant_country": "US"
        },
        "geolocation": {
            "country": "United States",
            "city": "San Francisco",
            "isp": "DigitalOcean",
            "asn": "AS14061"
        },
        "ssl_certificate": {
            "valid": true,
            "issuer": "Let's Encrypt",
            "expiration": "2026-05-08",
            "self_signed": false,
            "sans": ["example.com", "www.example.com"]
        },
        "threat_intelligence": {
            "greynoise": {
                "classification": "benign",
                "last_seen": "2026-02-01"
            },
            "abuseipdb": {
                "abuse_confidence_score": 0,
                "total_reports": 0
            }
        },
        "historical": {
            "first_seen": "2025-12-01",
            "changes_detected": 3,
            "archive_url": "https://web.archive.org/..."
        }
    }
}

3.1.5 Reporting & Export

Requirement ID: FR-005
Priority: P0 (Critical)

Description: Generate comprehensive reports in multiple formats for different audiences.

Report Formats:

  1. JSON (Machine-readable)

    {
    "scan_metadata": {
        "scan_id": "uuid",
        "timestamp": "ISO-8601",
        "duration_seconds": 45.3,
        "engines_used": ["shodan", "censys"],
        "total_results": 127
    },
    "summary": {
        "critical_findings": 12,
        "high_findings": 34,
        "medium_findings": 56,
        "low_findings": 25
    },
    "findings": [...]
}

  2. CSV (Spreadsheet-friendly)

    IP,Port,Hostname,Service,Risk Score,Vulnerabilities,First Seen,Location
192.0.2.1,8080,example.com,http,9.2,"no_auth,api_keys",2026-02-08,US

  3. HTML (Interactive Dashboard)

    • Executive summary with charts
    • Filterable/sortable table of findings
    • Vulnerability distribution pie chart
    • Geographic heatmap
    • Timeline of discoveries
    • Clickable links to detailed findings

  4. PDF (Executive Report)

    • Professional formatting
    • Executive summary (1 page)
    • Detailed findings with screenshots
    • Remediation recommendations
    • Appendices with technical details

  5. Markdown (Documentation)

    • GitHub-compatible formatting
    • Suitable for issue tracking
    • Easy to version control

Report Sections:

# AI Agent Security Reconnaissance Report

## Executive Summary
- Total instances discovered: 127
- Critical vulnerabilities: 12
- Affected organizations: 8
- Average risk score: 7.3/10

## Key Findings
1. [CRITICAL] 12 ClawdBot instances with no authentication
2. [HIGH] 34 instances exposing API keys in source code
3. [MEDIUM] 56 instances with debug mode enabled

## Detailed Analysis
### Finding #1: Unauthenticated ClawdBot Dashboard
- **Target:** 192.0.2.1:8080
- **Risk Score:** 9.2/10
- **Vulnerabilities:**
  - No authentication required
  - Shell access enabled
  - API keys visible in JavaScript
- **Evidence:** [Screenshot/URL]
- **Remediation:** Implement authentication, remove shell access

## Geographic Distribution
[Map showing affected countries]

## Remediation Roadmap
1. Immediate: Disable unauthenticated instances
2. Short-term: Rotate exposed API keys
3. Long-term: Implement security hardening guide

## Appendix
- A: Full query list
- B: CVSS score methodology
- C: Responsible disclosure template

3.1.6 Notification & Alerting

Requirement ID: FR-006
Priority: P2 (Medium)

Description: Alert users when new vulnerable instances are discovered or critical findings emerge.

Alert Channels:

  • Email (SMTP)
  • Slack webhook
  • Discord webhook
  • Telegram bot
  • SMS (Twilio integration)
  • PagerDuty (for critical findings)

Alert Triggers:

ALERT_RULES = {
    "new_critical_finding": {
        "condition": "risk_score >= 9.0",
        "throttle": "15_minutes",
        "channels": ["email", "slack"],
        "template": "critical_finding_alert"
    },
    
    "api_key_exposure": {
        "condition": "api_key_pattern_matched",
        "throttle": "immediate",
        "channels": ["email", "slack", "pagerduty"],
        "template": "api_key_leak_alert"
    },
    
    "bulk_discovery": {
        "condition": "new_findings >= 10",
        "throttle": "1_hour",
        "channels": ["email"],
        "template": "bulk_discovery_summary"
    }
}

Alert Template Example:

🚨 CRITICAL: Exposed AI Agent Instance Detected

Target: 192.0.2.1:8080
Service: ClawdBot Dashboard
Risk Score: 9.2/10

Vulnerabilities:
- No authentication required
- Shell command execution enabled
- Anthropic API key exposed (sk-ant-...)

Action Required:
1. Verify if this is your infrastructure
2. If yes: Immediately disable public access
3. If no: Report via responsible disclosure

View full details: [URL to report]

3.2 Advanced Features

3.2.1 Continuous Monitoring

Requirement ID: FR-007
Priority: P2 (Medium)

Description: Scheduled scans to monitor for new vulnerable instances over time.

Features:

  • Cron-like scheduling (hourly, daily, weekly)
  • Delta reporting (show only new findings)
  • Historical trending
  • Automated baseline creation
  • Change detection alerts

Configuration:

monitoring:
  enabled: true
  schedule: "0 */6 * * *"  # Every 6 hours
  retention_days: 90
  delta_only: true
  alert_on_new: true
  
  targets:
    - name: "ClawdBot Instances"
      queries: ["clawdbot_instances"]
      baseline_date: "2026-02-01"

3.2.2 False Positive Filtering

Requirement ID: FR-008
Priority: P1 (High)

Description: Machine learning-based or rule-based filtering to reduce false positives.

Filtering Strategies:

  1. Honeypot Detection

    • Identify intentional honeypots
    • Check for deceptive characteristics
    • Cross-reference with known honeypot databases

  2. Legitimate Development Environments

    • Detect localhost/internal IPs
    • Identify staging/dev subdomain patterns
    • Check for development framework banners

  3. Whitelisting

    • User-defined whitelist (IP ranges, domains)
    • Trusted organization list
    • Known security research infrastructure

  4. Confidence Scoring

    def calculate_confidence(finding):
    confidence = 100

    # Deduct points for uncertainty indicators
    if finding.get('honeypot_indicators'):
        confidence -= 30
    if finding.get('inconclusive_response'):
        confidence -= 20
    if not finding.get('service_confirmed'):
        confidence -= 15

    return max(confidence, 0)

3.2.3 Integration with Vulnerability Databases

Requirement ID: FR-009
Priority: P2 (Medium)

Description: Cross-reference findings with known vulnerabilities and exploits.

Integrations:

  • CVE/NVD database
  • ExploitDB
  • Metasploit modules
  • GitHub security advisories
  • Vendor-specific security bulletins

Output Enhancement:

{
    "finding": {
        "service": "ClawdBot 1.2.3",
        "related_cves": [
            {
                "cve_id": "CVE-2024-XXXXX",
                "description": "Authentication bypass in ClawdBot < 1.3.0",
                "cvss_score": 9.8,
                "exploit_available": true,
                "exploit_url": "https://exploit-db.com/..."
            }
        ]
    }
}

3.2.4 Responsible Disclosure Workflow

Requirement ID: FR-010
Priority: P1 (High)

Description: Built-in workflow for responsible disclosure of findings.

Workflow Steps:

  1. Identify Contact

    • Extract abuse@, security@ email from WHOIS
    • Check for security.txt file
    • Lookup on HackerOne/Bugcrowd
    • Search for CERT/PSIRT contacts

  2. Generate Disclosure Report

    Subject: Security Issue: Exposed AI Agent Instance

Dear Security Team,

I have discovered a potentially vulnerable AI agent deployment:

**Affected System:**
- URL: https://example.com:8080
- IP: 192.0.2.1
- Discovered: 2026-02-08

**Vulnerability Summary:**
- No authentication required for dashboard access
- Potential exposure of API credentials
- Shell command execution interface accessible

**Risk Assessment:**
- Severity: Critical (CVSS 9.2)
- Exploitability: High
- Impact: Confidentiality, Integrity, Availability

**Recommended Actions:**
1. Implement authentication immediately
2. Audit for credential exposure
3. Review access logs for unauthorized access

I am available to provide additional details if needed.

This disclosure follows a 90-day timeline.

Best regards,
[Your Name]
Security Researcher

  3. Track Disclosure

    • Status tracking (reported, acknowledged, fixed, disclosed)
    • Countdown timer for public disclosure
    • Communication log
    • PGP encryption support for sensitive details

  4. Public Disclosure

    • Anonymize or redact sensitive details
    • Publish to blog/database
    • Submit to CVE if applicable
    • Credit researchers appropriately

4. NON-FUNCTIONAL REQUIREMENTS

4.1 Performance

Requirement ID: NFR-001
Priority: P0 (Critical)

  • Query execution: < 30 seconds per search engine
  • Vulnerability assessment: < 5 seconds per target
  • Report generation: < 10 seconds for 1000 findings
  • Memory usage: < 500MB for typical scans
  • Concurrent requests: Support 10+ parallel API calls
  • Cache frequently accessed data (TTL: 1 hour)

4.2 Scalability

Requirement ID: NFR-002
Priority: P1 (High)

  • Handle 10,000+ results per scan
  • Support distributed scanning (future: multi-node)
  • Queue-based architecture for large jobs
  • Pagination for API responses
  • Database storage for historical data (SQLite → PostgreSQL)

4.3 Reliability

Requirement ID: NFR-003
Priority: P0 (Critical)

  • Graceful handling of API failures
  • Retry logic with exponential backoff (3 attempts)
  • Timeout handling (30s per request)
  • Partial results on engine failure
  • State persistence (resume interrupted scans)
  • Error logging with stack traces

4.4 Security

Requirement ID: NFR-004
Priority: P0 (Critical)

  • API keys stored encrypted (AES-256)
  • No credential storage in logs
  • HTTPS-only for API communications
  • Input validation on all user inputs
  • Rate limiting enforcement
  • Audit logging of all scans
  • Secrets management via environment variables or vault

4.5 Usability

Requirement ID: NFR-005
Priority: P1 (High)

  • CLI interface with intuitive commands
  • Progress indicators for long-running tasks
  • Colored output for severity levels
  • Interactive mode for configuration
  • Help documentation built-in
  • Example queries provided
  • Wizard for first-time setup

4.6 Maintainability

Requirement ID: NFR-006
Priority: P1 (High)

  • Modular architecture (plugins for search engines)
  • Comprehensive inline documentation
  • Type hints for all functions
  • Unit test coverage > 80%
  • Integration tests for critical paths
  • Configuration via YAML/JSON
  • Logging at appropriate levels (DEBUG, INFO, WARNING, ERROR)

4.7 Portability

Requirement ID: NFR-007
Priority: P2 (Medium)

  • Cross-platform (Linux, macOS, Windows)
  • Python 3.9+ compatibility
  • Docker containerization
  • Minimal external dependencies
  • Standalone binary distribution (PyInstaller)

5. TECHNICAL ARCHITECTURE

5.1 System Architecture

┌─────────────────────────────────────────────────────────────┐
│                         CLI Interface                        │
│                   (argparse + rich/click)                    │
└────────────────────────┬────────────────────────────────────┘
                         │
                         ▼
┌─────────────────────────────────────────────────────────────┐
│                     Core Engine                              │
│  - Query Manager                                             │
│  - Result Aggregator                                         │
│  - Vulnerability Assessor                                    │
└────────────────────────┬────────────────────────────────────┘
                         │
         ┌───────────────┼───────────────┬────────────────┐
         ▼               ▼               ▼                ▼
┌─────────────┐  ┌─────────────┐  ┌──────────┐  ┌──────────────┐
│   Shodan    │  │   Censys    │  │  GitHub  │  │   ZoomEye    │
│   Module    │  │   Module    │  │  Module  │  │   Module     │
└─────────────┘  └─────────────┘  └──────────┘  └──────────────┘
         │               │               │                │
         └───────────────┴───────────────┴────────────────┘
                         │
                         ▼
┌─────────────────────────────────────────────────────────────┐
│                   Data Enrichment Layer                      │
│  - WHOIS | Geolocation | SSL | DNS | Threat Intel           │
└────────────────────────┬────────────────────────────────────┘
                         │
                         ▼
┌─────────────────────────────────────────────────────────────┐
│                   Storage Layer                              │
│  - SQLite (local) / PostgreSQL (production)                  │
│  - File system (JSON/CSV exports)                            │
└────────────────────────┬────────────────────────────────────┘
                         │
                         ▼
┌─────────────────────────────────────────────────────────────┐
│                   Reporting Engine                           │
│  - JSON | CSV | HTML | PDF | Markdown                       │
└─────────────────────────────────────────────────────────────┘

5.2 Data Flow

User Input → Query Builder → API Clients → Raw Results → 
Deduplication → Vulnerability Assessment → Enrichment → 
Filtering → Risk Scoring → Storage → Report Generation → 
Output (File/Screen/Alert)

5.3 Database Schema

-- SQLite/PostgreSQL Schema

CREATE TABLE scans (
    scan_id TEXT PRIMARY KEY,
    timestamp DATETIME NOT NULL,
    engines_used TEXT,  -- JSON array
    total_results INTEGER,
    duration_seconds REAL,
    status TEXT,  -- 'completed', 'failed', 'partial'
    metadata TEXT  -- JSON
);

CREATE TABLE findings (
    finding_id TEXT PRIMARY KEY,
    scan_id TEXT REFERENCES scans(scan_id),
    source_engine TEXT,
    target_ip TEXT,
    target_port INTEGER,
    target_hostname TEXT,
    service TEXT,
    risk_score REAL,
    vulnerabilities TEXT,  -- JSON array
    first_seen DATETIME,
    last_seen DATETIME,
    status TEXT,  -- 'new', 'confirmed', 'false_positive', 'remediated'
    metadata TEXT  -- JSON
);

CREATE TABLE enrichment_data (
    finding_id TEXT REFERENCES findings(finding_id),
    whois_data TEXT,  -- JSON
    geolocation TEXT,  -- JSON
    ssl_cert TEXT,  -- JSON
    threat_intel TEXT,  -- JSON
    timestamp DATETIME
);

CREATE TABLE alerts (
    alert_id TEXT PRIMARY KEY,
    finding_id TEXT REFERENCES findings(finding_id),
    severity TEXT,
    channel TEXT,
    sent_at DATETIME,
    acknowledged BOOLEAN DEFAULT FALSE
);

CREATE INDEX idx_findings_risk ON findings(risk_score DESC);
CREATE INDEX idx_findings_timestamp ON findings(first_seen DESC);

5.4 Configuration Management

Configuration File: config.yaml

# API Credentials
api_keys:
  shodan: ${SHODAN_API_KEY}  # Environment variable
  censys:
    api_id: ${CENSYS_API_ID}
    secret: ${CENSYS_SECRET}
  zoomeye: ${ZOOMEYE_API_KEY}
  github: ${GITHUB_TOKEN}
  greynoise: ${GREYNOISE_API_KEY}

# Search Engine Configuration
engines:
  shodan:
    enabled: true
    rate_limit: 1  # queries per second
    max_results: 100
    timeout: 30
  
  censys:
    enabled: true
    rate_limit: 0.4  # 120 per 5 min = 0.4/sec
    max_results: 100
    timeout: 30
  
  github:
    enabled: true
    rate_limit: 0.5  # 30 per min
    max_results: 50
    timeout: 30

# Vulnerability Assessment
vulnerability_checks:
  enabled: true
  passive_only: true
  timeout_per_check: 10
  max_concurrent: 5

# Enrichment
enrichment:
  enabled: true
  sources:
    - whois
    - geolocation
    - ssl_certificate
    - threat_intel
  cache_ttl: 3600  # 1 hour

# Reporting
reporting:
  formats:
    - json
    - csv
    - html
  output_dir: "./reports"
  include_screenshots: false
  anonymize_by_default: false

# Alerting
alerts:
  enabled: false
  channels:
    email:
      smtp_server: "smtp.gmail.com"
      smtp_port: 587
      from_address: "alerts@example.com"
      to_addresses:
        - "security@example.com"
    slack:
      webhook_url: ${SLACK_WEBHOOK_URL}

# Monitoring
monitoring:
  enabled: false
  schedule: "0 */12 * * *"
  retention_days: 90

# Filtering
filtering:
  whitelist_ips: []
  whitelist_domains: []
  min_confidence_score: 70
  exclude_honeypots: true

# Logging
logging:
  level: "INFO"  # DEBUG, INFO, WARNING, ERROR
  file: "./logs/scanner.log"
  max_size_mb: 100
  backup_count: 5

# Database
database:
  type: "sqlite"  # sqlite or postgresql
  sqlite:
    path: "./data/scanner.db"
  postgresql:
    host: "localhost"
    port: 5432
    database: "aasrt"
    user: ${DB_USER}
    password: ${DB_PASSWORD}

6. PYTHON IMPLEMENTATION SPECIFICATIONS

6.1 Project Structure

ai-agent-security-scanner/
├── src/
│   ├── __init__.py
│   ├── main.py                    # CLI entry point
│   ├── core/
│   │   ├── __init__.py
│   │   ├── query_manager.py       # Query building & execution
│   │   ├── result_aggregator.py   # Result deduplication & merging
│   │   ├── vulnerability_assessor.py  # Vulnerability checks
│   │   └── risk_scorer.py         # Risk calculation
│   ├── engines/
│   │   ├── __init__.py
│   │   ├── base.py                # Abstract base class
│   │   ├── shodan_engine.py
│   │   ├── censys_engine.py
│   │   ├── zoomeye_engine.py
│   │   ├── github_engine.py
│   │   └── google_engine.py
│   ├── enrichment/
│   │   ├── __init__.py
│   │   ├── whois.py
│   │   ├── geolocation.py
│   │   ├── ssl_analyzer.py
│   │   ├── dns.py
│   │   └── threat_intel.py
│   ├── reporting/
│   │   ├── __init__.py
│   │   ├── json_reporter.py
│   │   ├── csv_reporter.py
│   │   ├── html_reporter.py
│   │   ├── pdf_reporter.py
│   │   └── markdown_reporter.py
│   ├── storage/
│   │   ├── __init__.py
│   │   ├── database.py            # Database abstraction
│   │   └── cache.py               # Caching layer
│   ├── alerts/
│   │   ├── __init__.py
│   │   ├── email_notifier.py
│   │   ├── slack_notifier.py
│   │   └── webhook_notifier.py
│   └── utils/
│       ├── __init__.py
│       ├── config.py              # Configuration loader
│       ├── logger.py              # Logging setup
│       ├── validators.py          # Input validation
│       └── crypto.py              # Encryption helpers
├── tests/
│   ├── __init__.py
│   ├── test_engines/
│   ├── test_vulnerability_assessor.py
│   ├── test_enrichment.py
│   └── test_reporting.py
├── queries/
│   ├── clawdbot.yaml              # ClawdBot query templates
│   ├── autogpt.yaml
│   └── custom.yaml.example
├── reports/                       # Generated reports (gitignored)
├── logs/                          # Log files (gitignored)
├── data/                          # Database files (gitignored)
├── config.yaml.example            # Example configuration
├── requirements.txt               # Python dependencies
├── Dockerfile                     # Docker image
├── docker-compose.yml             # Docker Compose setup
├── README.md                      # Project documentation
├── LICENSE                        # GPL-3.0 or MIT
└── .env.example                   # Environment variables example

6.2 Required Python Libraries

# requirements.txt

# API Clients
shodan==1.31.0
censys==2.2.12
requests==2.31.0

# Data Processing
pandas==2.2.0
python-whois==0.9.2
dnspython==2.6.1
pyOpenSSL==24.0.0
cryptography==42.0.5

# Database
sqlalchemy==2.0.28
psycopg2-binary==2.9.9  # PostgreSQL adapter

# Reporting
jinja2==3.1.3  # HTML templates
matplotlib==3.8.3  # Charts
reportlab==4.1.0  # PDF generation
markdown==3.5.2

# CLI & UI
click==8.1.7  # CLI framework
rich==13.7.0  # Rich text formatting
tqdm==4.66.2  # Progress bars

# Utilities
pyyaml==6.0.1
python-dotenv==1.0.1
pydantic==2.6.3  # Data validation
validators==0.22.0

# Networking
ipaddress==1.0.23
geoip2==4.7.0
python-nmap==0.7.1  # Optional

# Security
keyring==25.0.0  # Secure credential storage
bcrypt==4.1.2

# Testing
pytest==8.0.2
pytest-cov==4.1.0
pytest-mock==3.12.0

# Async (optional for performance)
aiohttp==3.9.3
asyncio==3.4.3

6.3 Core Classes & Interfaces

Abstract Base Class for Search Engines:

# engines/base.py

from abc import ABC, abstractmethod
from typing import List, Dict, Any
from dataclasses import dataclass

@dataclass
class SearchResult:
    ip: str
    port: int
    hostname: str = None
    service: str = None
    banner: str = None
    vulnerabilities: List[str] = None
    metadata: Dict[str, Any] = None
    source_engine: str = None

class BaseSearchEngine(ABC):
    """Abstract base class for all search engine integrations."""
    
    def __init__(self, api_key: str, rate_limit: float, timeout: int):
        self.api_key = api_key
        self.rate_limit = rate_limit  # queries per second
        self.timeout = timeout
        
    @abstractmethod
    def search(self, query: str, max_results: int = 100) -> List[SearchResult]:
        """
        Execute a search query and return results.
        
        Args:
            query: Search query string
            max_results: Maximum number of results to return
            
        Returns:
            List of SearchResult objects
            
        Raises:
            APIException: If API call fails
            RateLimitException: If rate limit exceeded
        """
        pass
    
    @abstractmethod
    def validate_credentials(self) -> bool:
        """Validate API credentials."""
        pass
    
    def _rate_limit_wait(self):
        """Implement rate limiting."""
        import time
        time.sleep(1.0 / self.rate_limit)

Vulnerability Assessor Interface:

# core/vulnerability_assessor.py

from typing import List, Dict
from dataclasses import dataclass

@dataclass
class Vulnerability:
    check_name: str
    severity: str  # CRITICAL, HIGH, MEDIUM, LOW, INFO
    cvss_score: float
    description: str
    evidence: Dict[str, Any] = None
    remediation: str = None

class VulnerabilityAssessor:
    """Performs vulnerability checks on discovered targets."""
    
    def __init__(self, config: Dict[str, Any]):
        self.config = config
        self.passive_only = config.get('passive_only', True)
        
    def assess(self, target: SearchResult) -> List[Vulnerability]:
        """
        Run all applicable vulnerability checks on a target.
        
        Args:
            target: SearchResult object to assess
            
        Returns:
            List of Vulnerability objects
        """
        vulnerabilities = []
        
        # Run checks
        vulnerabilities.extend(self._check_authentication(target))
        vulnerabilities.extend(self._check_information_disclosure(target))
        vulnerabilities.extend(self._check_dangerous_functionality(target))
        vulnerabilities.extend(self._check_network_exposure(target))
        
        return vulnerabilities
    
    def _check_authentication(self, target: SearchResult) -> List[Vulnerability]:
        """Check for authentication issues."""
        # Implementation details
        pass
    
    def _check_information_disclosure(self, target: SearchResult) -> List[Vulnerability]:
        """Check for exposed sensitive information."""
        # Implementation details
        pass

CLI Interface Specification:

# main.py

import click
from rich.console import Console
from rich.table import Table

@click.group()
@click.version_option(version='1.0.0')
def cli():
    """AI Agent Security Reconnaissance Tool (AASRT)"""
    pass

@cli.command()
@click.option('--engine', '-e', multiple=True, 
              type=click.Choice(['shodan', 'censys', 'zoomeye', 'github', 'all']),
              default=['all'], help='Search engines to use')
@click.option('--query', '-q', help='Custom search query')
@click.option('--template', '-t', help='Use predefined query template')
@click.option('--max-results', '-m', default=100, help='Max results per engine')
@click.option('--output', '-o', type=click.Path(), help='Output file path')
@click.option('--format', '-f', 
              type=click.Choice(['json', 'csv', 'html', 'pdf', 'markdown']),
              default='json', help='Output format')
@click.option('--verbose', '-v', is_flag=True, help='Verbose output')
def scan(engine, query, template, max_results, output, format, verbose):
    """Perform a security reconnaissance scan."""
    console = Console()
    
    # Display legal disclaimer
    if not click.confirm('Have you read and agreed to the terms of use?'):
        console.print('[red]Scan aborted. You must agree to terms of use.[/red]')
        return
    
    # Execute scan
    console.print('[green]Starting scan...[/green]')
    # Implementation...

@cli.command()
@click.argument('scan_id')
def report(scan_id):
    """Generate a report from a previous scan."""
    # Implementation...

@cli.command()
def configure():
    """Interactive configuration wizard."""
    # Implementation...

@cli.command()
@click.option('--enable/--disable', default=True)
def monitor(enable):
    """Enable or disable continuous monitoring."""
    # Implementation...

if __name__ == '__main__':
    cli()

6.4 Error Handling Strategy

# Custom Exceptions

class AASRTException(Exception):
    """Base exception for AASRT."""
    pass

class APIException(AASRTException):
    """Raised when API call fails."""
    pass

class RateLimitException(AASRTException):
    """Raised when rate limit is exceeded."""
    pass

class ConfigurationException(AASRTException):
    """Raised when configuration is invalid."""
    pass

class ValidationException(AASRTException):
    """Raised when input validation fails."""
    pass

# Error Handling Pattern

def search_with_retry(func, max_retries=3):
    """Decorator for API calls with retry logic."""
    import time
    from functools import wraps
    
    @wraps(func)
    def wrapper(*args, **kwargs):
        for attempt in range(max_retries):
            try:
                return func(*args, **kwargs)
            except RateLimitException:
                if attempt < max_retries - 1:
                    wait_time = 2 ** attempt  # Exponential backoff
                    logger.warning(f"Rate limit hit. Retrying in {wait_time}s...")
                    time.sleep(wait_time)
                else:
                    raise
            except APIException as e:
                logger.error(f"API error on attempt {attempt + 1}: {e}")
                if attempt == max_retries - 1:
                    raise
        
    return wrapper

7. SECURITY & COMPLIANCE

7.1 Ethical Use Policy

CRITICAL REQUIREMENTS:

  1. Passive Reconnaissance Only

    • No exploitation of discovered vulnerabilities
    • No brute force attacks
    • No unauthorized access attempts
    • No modification of target systems

  2. Responsible Disclosure

    • Mandatory 90-day disclosure window
    • Attempt to contact affected parties
    • Anonymize sensitive details in public reports
    • Coordinate with CERTs when appropriate

  3. Legal Compliance

    • CFAA compliance (US)
    • GDPR compliance (EU)
    • Computer Misuse Act compliance (UK)
    • Local jurisdiction laws

  4. User Agreement

    • Explicit acceptance of terms before use
    • Logging of user acknowledgment
    • Clear documentation of permitted uses

7.2 Data Privacy

Personal Data Handling:

  • Do not collect personal information beyond what's publicly indexed
  • Anonymize IP addresses in shared reports (option to mask last octet)
  • Do not store API responses containing PII indefinitely
  • Provide data deletion capabilities
  • GDPR right-to-be-forgotten support

API Key Security:

  • Never log API keys
  • Encrypt stored credentials (AES-256)
  • Support environment variable injection
  • Warn on insecure configurations
  • Key rotation reminders

7.3 Audit Trail

Required Logging:

# Audit log format
{
    "timestamp": "ISO-8601",
    "user": "username_or_id",
    "action": "scan_initiated",
    "engines": ["shodan", "censys"],
    "query": "product:ClawdBot",
    "results_count": 42,
    "ip_address": "user_ip",
    "acknowledged_terms": true
}

Log Retention:

  • Audit logs: 1 year minimum
  • Scan results: Configurable (default 90 days)
  • Error logs: 30 days

8. DEPLOYMENT & OPERATIONS

8.1 Installation Methods

Method 1: pip install (PyPI)

pip install ai-agent-scanner
aasrt configure  # Interactive setup
aasrt scan --template clawdbot

Method 2: From source

git clone https://github.com/yourusername/ai-agent-scanner.git
cd ai-agent-scanner
pip install -r requirements.txt
python -m src.main scan --help

Method 3: Docker

docker pull yourusername/aasrt:latest
docker run -it \
  -e SHODAN_API_KEY=your_key \
  -v $(pwd)/reports:/app/reports \
  aasrt:latest scan --template clawdbot

Method 4: Standalone binary

# PyInstaller-generated executable
./aasrt-linux-x64 scan --help

8.2 Docker Configuration

Dockerfile:

FROM python:3.11-slim

WORKDIR /app

# Install dependencies
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

# Copy application
COPY src/ ./src/
COPY queries/ ./queries/
COPY config.yaml.example ./config.yaml

# Create volumes for persistent data
VOLUME ["/app/reports", "/app/data", "/app/logs"]

# Environment variables
ENV PYTHONUNBUFFERED=1

ENTRYPOINT ["python", "-m", "src.main"]
CMD ["--help"]

docker-compose.yml:

version: '3.8'

services:
  aasrt:
    build: .
    environment:
      - SHODAN_API_KEY=${SHODAN_API_KEY}
      - CENSYS_API_ID=${CENSYS_API_ID}
      - CENSYS_SECRET=${CENSYS_SECRET}
    volumes:
      - ./reports:/app/reports
      - ./data:/app/data
      - ./logs:/app/logs
      - ./config.yaml:/app/config.yaml
    command: scan --template clawdbot --output /app/reports/scan.json
  
  postgres:
    image: postgres:16
    environment:
      - POSTGRES_DB=aasrt
      - POSTGRES_USER=aasrt
      - POSTGRES_PASSWORD=${DB_PASSWORD}
    volumes:
      - postgres_data:/var/lib/postgresql/data
    ports:
      - "5432:5432"

volumes:
  postgres_data:

8.3 CI/CD Pipeline

GitHub Actions Workflow:

name: CI/CD Pipeline

on:
  push:
    branches: [main]
  pull_request:
    branches: [main]

jobs:
  test:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3
      - uses: actions/setup-python@v4
        with:
          python-version: '3.11'
      
      - name: Install dependencies
        run: |
          pip install -r requirements.txt
          pip install pytest pytest-cov
      
      - name: Run tests
        run: pytest --cov=src --cov-report=xml
      
      - name: Upload coverage
        uses: codecov/codecov-action@v3
  
  build:
    needs: test
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3
      
      - name: Build Docker image
        run: docker build -t aasrt:${{ github.sha }} .
      
      - name: Push to registry
        run: |
          echo ${{ secrets.DOCKER_PASSWORD }} | docker login -u ${{ secrets.DOCKER_USERNAME }} --password-stdin
          docker push aasrt:${{ github.sha }}

9. TESTING STRATEGY

9.1 Unit Tests

Coverage Requirements:

  • Core functions: 90%+ coverage
  • Engine modules: 80%+ coverage
  • Utilities: 95%+ coverage

Example Test Cases:

# tests/test_vulnerability_assessor.py

import pytest
from src.core.vulnerability_assessor import VulnerabilityAssessor
from src.engines.base import SearchResult

def test_authentication_check_no_auth():
    """Test detection of unauthenticated endpoints."""
    assessor = VulnerabilityAssessor({'passive_only': True})
    
    target = SearchResult(
        ip="192.0.2.1",
        port=8080,
        service="http",
        metadata={"http_status": 200, "auth_required": False}
    )
    
    vulns = assessor._check_authentication(target)
    
    assert len(vulns) == 1
    assert vulns[0].severity == "CRITICAL"
    assert vulns[0].check_name == "no_authentication"

def test_api_key_exposure_detection():
    """Test detection of exposed API keys."""
    assessor = VulnerabilityAssessor({'passive_only': True})
    
    target = SearchResult(
        ip="192.0.2.1",
        port=8080,
        banner="sk-ant-api03-abc123..."
    )
    
    vulns = assessor._check_information_disclosure(target)
    
    assert any(v.check_name == "api_key_exposure" for v in vulns)

9.2 Integration Tests

# tests/test_integration.py

@pytest.mark.integration
def test_end_to_end_scan(mock_shodan_api):
    """Test complete scan workflow."""
    from src.main import scan_command
    
    # Setup
    mock_shodan_api.return_value = [
        {"ip_str": "192.0.2.1", "port": 8080, "data": "ClawdBot"}
    ]
    
    # Execute
    result = scan_command(
        engines=['shodan'],
        query='product:ClawdBot',
        max_results=10
    )
    
    # Verify
    assert result['total_results'] > 0
    assert 'findings' in result
    assert result['findings'][0]['risk_score'] >= 0

9.3 Security Tests

  • Input validation tests (SQL injection, XSS, path traversal)
  • API key encryption verification
  • Rate limiting enforcement tests
  • Credential leakage tests (grep logs for secrets)

10. DOCUMENTATION REQUIREMENTS

10.1 User Documentation

README.md:

  • Installation instructions
  • Quick start guide
  • CLI command reference
  • Configuration examples
  • Troubleshooting guide

USAGE.md:

  • Detailed usage scenarios
  • Query template guide
  • Custom query creation
  • Report interpretation
  • Best practices

LEGAL.md:

  • Terms of service
  • Responsible use guidelines
  • Legal disclaimers
  • Compliance requirements

10.2 Developer Documentation

CONTRIBUTING.md:

  • Code style guide (PEP 8)
  • Testing requirements
  • Pull request process
  • Engine plugin development guide

API_REFERENCE.md:

  • Class and method documentation
  • Data models
  • Configuration schema
  • Extension points

11. ROADMAP & FUTURE ENHANCEMENTS

Phase 1 (MVP) - Months 1-2

  • Core engine with Shodan + Censys integration
  • Basic vulnerability assessment
  • JSON/CSV reporting
  • CLI interface

Phase 2 - Months 3-4

  • GitHub Code Search integration
  • HTML/PDF reporting
  • Data enrichment (WHOIS, geolocation)
  • Docker deployment

Phase 3 - Months 5-6

  • Continuous monitoring
  • Alert notifications
  • False positive filtering
  • Web UI (optional)

Phase 4 - Future

  • Machine learning for anomaly detection
  • Distributed scanning (multi-node)
  • Blockchain integration for immutable audit logs
  • Custom CVE tracking
  • Integration with SIEM platforms

12. SUCCESS CRITERIA

12.1 Acceptance Criteria

Minimum Viable Product (MVP):

  • ✅ Successfully discover ClawdBot instances on Shodan
  • ✅ Identify at least 3 vulnerability categories
  • ✅ Generate JSON report with risk scores
  • ✅ Complete scan in < 2 minutes for 100 results
  • ✅ Zero API key leaks in logs
  • ✅ Pass all unit tests (80%+ coverage)

Production Ready:

  • ✅ Support 5+ search engines
  • ✅ Multi-format reporting (JSON, CSV, HTML, PDF)
  • ✅ Data enrichment functional
  • ✅ False positive rate < 15%
  • ✅ Comprehensive documentation
  • ✅ Docker deployment tested
  • ✅ Responsible disclosure workflow implemented

12.2 Performance Benchmarks

  • Scan 1000 results in < 5 minutes
  • Memory usage stays under 500MB
  • Report generation < 10 seconds
  • Zero data loss on interruptions
  • API rate limits never exceeded

13. APPENDICES

Appendix A: Query Template Examples

ClawdBot Detection:

# queries/clawdbot.yaml

name: ClawdBot Vulnerability Scan
description: Detect exposed ClawdBot instances

queries:
  shodan:
    - 'product:"ClawdBot"'
    - 'http.title:"ClawdBot Dashboard"'
    - 'http.html:"anthropic" http.html:"api_key"'
  
  censys:
    - 'services.http.response.body: "ClawdBot"'
    - 'services.http.response.html_title: "ClawdBot"'
  
  github:
    - '"ClawdBot" filename:.env "ANTHROPIC_API_KEY"'
    - 'path:config.json "clawdbot" "api_key"'

vulnerability_checks:
  - no_authentication
  - api_key_exposure
  - shell_access
  - debug_mode

risk_threshold: 7.0
alert_on_discovery: true

Appendix B: Sample Output

JSON Report (Abbreviated):

{
  "scan_metadata": {
    "scan_id": "550e8400-e29b-41d4-a716-446655440000",
    "timestamp": "2026-02-08T14:30:00Z",
    "engines_used": ["shodan", "censys"],
    "total_results": 42,
    "duration_seconds": 87.3
  },
  "summary": {
    "critical_findings": 12,
    "high_findings": 18,
    "medium_findings": 10,
    "low_findings": 2,
    "average_risk_score": 7.8
  },
  "findings": [
    {
      "finding_id": "finding-001",
      "target_ip": "192.0.2.1",
      "target_port": 8080,
      "target_hostname": "clawdbot.example.com",
      "service": "http",
      "risk_score": 9.2,
      "vulnerabilities": [
        {
          "check_name": "no_authentication",
          "severity": "CRITICAL",
          "cvss_score": 9.1,
          "description": "Dashboard accessible without authentication",
          "evidence": {"http_status": 200, "auth_header": null}
        },
        {
          "check_name": "api_key_exposure",
          "severity": "CRITICAL",
          "cvss_score": 10.0,
          "description": "Anthropic API key visible in source code",
          "evidence": {"api_key_preview": "sk-ant-api03-..."}
        }
      ],
      "enrichment": {
        "geolocation": {"country": "US", "city": "San Francisco"},
        "whois": {"registrar": "GoDaddy"},
        "threat_intel": {"greynoise": "benign"}
      },
      "first_seen": "2026-02-08T14:32:15Z",
      "status": "new"
    }
  ]
}

Appendix C: CVSS Score Mapping

Vulnerability CVSS Score Severity
No Authentication 9.1 CRITICAL
API Key Exposure 10.0 CRITICAL
Shell Access Enabled 9.9 CRITICAL
Debug Mode Enabled 7.5 HIGH
Default Credentials 8.5 HIGH
Information Disclosure 6.5 MEDIUM
Missing HTTPS 5.3 MEDIUM
Outdated Software 4.0 LOW

Appendix D: Responsible Disclosure Template

# Security Vulnerability Disclosure

**To:** security@example.com  
**Subject:** Security Issue: Exposed AI Agent Instance  
**Severity:** Critical  
**Disclosure Timeline:** 90 days (until May 9, 2026)

---

## Summary

I have discovered a security vulnerability affecting your AI agent deployment that could lead to unauthorized access and data exposure.

## Affected System

- **URL:** https://clawdbot.example.com:8080
- **IP Address:** 192.0.2.1
- **Service:** ClawdBot Dashboard
- **Discovery Date:** February 8, 2026

## Vulnerability Details

### 1. Unauthenticated Access (CVSS 9.1)
The ClawdBot dashboard is accessible without any authentication mechanism.

**Steps to Reproduce:**
1. Navigate to https://clawdbot.example.com:8080
2. Observe full dashboard access without login

### 2. API Key Exposure (CVSS 10.0)
Anthropic API keys are visible in client-side JavaScript.

**Evidence:**
- File: `/static/js/config.js`
- Line 12: `ANTHROPIC_API_KEY: "sk-ant-api03-..."`

### 3. Shell Command Execution (CVSS 9.9)
The `/api/execute` endpoint allows arbitrary command execution.

## Impact

- Unauthorized access to all conversations and data
- Theft of API credentials leading to financial impact
- Potential for lateral movement within your infrastructure
- Reputational damage

## Recommended Remediation

**Immediate Actions:**
1. Disable public access to the instance
2. Rotate all exposed API keys
3. Review access logs for unauthorized activity

**Short-term Actions:**
1. Implement authentication (OAuth 2.0 recommended)
2. Move API keys to server-side environment variables
3. Disable or properly secure shell execution endpoints

**Long-term Actions:**
1. Conduct security audit of all AI agent deployments
2. Implement security hardening guidelines
3. Set up monitoring for similar issues

## Disclosure Timeline

- **Day 0 (Feb 8):** Initial discovery
- **Day 1 (Feb 9):** Disclosure to security team
- **Day 30 (Mar 10):** Follow-up if no response
- **Day 60 (Apr 9):** Limited disclosure to CERT
- **Day 90 (May 9):** Public disclosure if not remediated

## Contact Information

I am available to provide additional details or assistance.

**Researcher:** [Your Name]  
**Email:** [Your Email]  
**PGP Key:** [Key Fingerprint]

---

*This disclosure follows coordinated vulnerability disclosure practices and is intended to help improve your security posture.*

14. CONCLUSION

This Product Requirements Document provides a comprehensive specification for building an AI Agent Security Reconnaissance Tool. The tool will enable security researchers to discover and assess vulnerable AI agent deployments at scale while maintaining ethical standards and legal compliance.

Key Takeaways:

  1. Passive reconnaissance only - No exploitation
  2. Multi-source intelligence - Leverage Shodan, Censys, GitHub, etc.
  3. Automated vulnerability assessment - Risk scoring and categorization
  4. Comprehensive reporting - Multiple formats for different audiences
  5. Responsible disclosure - Built-in ethical workflow
  6. Production-ready - Docker, CI/CD, monitoring capabilities

Next Steps:

  1. Review and approve this PRD
  2. Set up development environment
  3. Implement MVP (Phase 1) features
  4. Conduct security review
  5. Beta testing with trusted researchers
  6. Public release with documentation

Document Approval:

Role Name Signature Date
Product Owner AGK _______ _____
Technical Lead _______ _______ _____
Security Lead _______ _______ _____
Legal Counsel _______ _______ _____

Document Revision History:

Version Date Author Changes
1.0 2026-02-08 AGK Initial comprehensive PRD

END OF DOCUMENT

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