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Introduction
Deep Eye is an open-source, AI-driven vulnerability scanner engineered for bug bounty hunters and pentesters, featuring 45+ built-in attack modules and multi-AI provider support (OpenAI, , Grok, Ollama). It revolutionizes automated reconnaissance and vulnerability discovery by generating context-aware payloads and producing professional-grade reports without manual scripting. At its core, Deep Eye demonstrates how large language models are transforming offensive security—turning hours of manual testing into minutes of intelligent automation while maintaining human-level contextual understanding of complex web and API flaws.
Learning Objectives
- Understand Deep Eye’s architecture, multi-AI integration capabilities, and 45+ attack vector coverage (SQLi, XSS, SSRF, API BOLA, auth bypass, SSTI, GraphQL, business logic flaws).
- Deploy and configure Deep Eye on Linux with multiple LLM backends (OpenAI, Anthropic , Grok, local Ollama) for automated scanning pipelines.
- Execute reconnaissance workflows combining passive OSINT, subdomain enumeration, GitHub leaks, and AI-powered payload generation.
- Interpret automated vulnerability reports and triage findings for bug bounty submissions or remediation guidance.
You Should Know
- Core Architecture: How Deep Eye Orchestrates AI for Web Attacks
Deep Eye is a Python-based scanner that structures its workflow into three primary phases: reconnaissance, attack execution, and reporting. The reconnaissance layer integrates passive OSINT techniques including Google Dorks, DNS enumeration, certificate transparency log scanning (CRT.sh), and GitHub/Pastebin monitoring for leaked credentials or API keys. Once a target’s attack surface is mapped, the AI coordination engine determines which of the 45+ attack modules to deploy based on the detected technology stack (e.g., Django, Spring, React). The AI then generates context-specific payloads—for example, crafting time-based blind SQL injection strings tailored to a MySQL backend or encoding XSS vectors to bypass WAF rules.
This context-aware approach distinguishes Deep Eye from traditional vulnerability scanners, which rely on static signature databases. During testing, Deep Eye integrates with LLM providers dynamically, switching between OpenAI, , Grok, or locally deployed Ollama models depending on the vulnerability class being explored.
Step‑by‑Step Guide: Installing and Configuring Deep Eye on Kali Linux
1. Clone the repository and set up Python environment git clone https://github.com/zakirkun/deep-eye.git cd deep-eye python3 -m venv venv source venv/bin/activate <ol> <li>Install dependencies (requires Python 3.10+) pip install -r requirements.txt</p></li> <li><p>Configure API keys for LLM providers (edit config.yaml) cp config.example.yaml config.yaml nano config.yaml Add your OpenAI API key, Anthropic key, or configure local Ollama endpoint Example: openai: api_key: "sk-xxxxxxxx" model: "gpt-4-turbo" ollama: endpoint: "http://localhost:11434" model: "llama3.1:latest"</p></li> <li><p>Launch the web interface (UI mode) or CLI mode python app.py --host 0.0.0.0 --port 5000 Alternatively, CLI scan: python deep_eye.py -u https://target.com -o report.html
Verifying Installation and Running a Test Scan
Use the built-in test target (DVWA or deliberately vulnerable app) python deep_eye.py -u http://testphp.vulnweb.com --recon-only Full scan with all 45+ modules enabled python deep_eye.py -u https://target.com --all-modules --ai-assist Monitor real-time AI decision-making python deep_eye.py -u https://target.com --debug --log-level DEBUG
- Multi‑AI Orchestration: Dynamic Payload Generation for OWASP Top 10 Vulnerabilities
The intelligence layer of Deep Eye lies in its ability to query LLMs for on‑the‑fly payloads that adapt to target responses. Rather than maintaining a static payload list (which quickly becomes obsolete), Deep Eye sends HTTP response snippets to the AI and asks: “What malicious payload would exploit an SQL injection vulnerability given this database error message?” The AI returns custom-crafted payloads, which the scanner injects into parameters, headers, or JSON bodies. This approach covers critical OWASP API Top 10 flaws including BOLA (API1) – where the AI attempts to modify object identifiers in authenticated API calls – and BOPLA (API3), a sophisticated authorization bypass at the property level.
GraphQL endpoints receive special handling: Deep Eye introspects the GraphQL schema automatically, then instructs the AI to generate nested mutation attacks or recursive query bombs. For REST APIs, the scanner cycles through HTTP methods (GET, POST, PUT, PATCH, DELETE) while the LLM adjusts payloads accordingly, testing for authentication bypasses and mass assignment vulnerabilities.
Step‑by‑Step Guide: Customizing AI Prompts and Testing API Authentication Flaws
1. Run API-focused scan with AI payload generation python deep_eye.py -u https://api.target.com/v1 -a --api-mode --auth-token "Bearer $TOKEN" <ol> <li>Test for BOLA by polymorphic object ID manipulation (AI will attempt IDOR sequence) The AI automatically detects ID patterns in URLs or JSON bodies and mutates them python deep_eye.py -u https://api.target.com/users/123/profile --test-bola --ai-bypass</p></li> <li><p>Force GraphQL introspection and attack python deep_eye.py -u https://api.target.com/graphql --graphql --ai-payloads</p></li> <li><p>Custom prompt injection for Cloudflare WAF bypass (modify prompt in config) Example config.yaml addition: custom_prompts: sqli_bypass: "Generate a SQL injection payload that evades Cloudflare WAF's pattern matching but still terminates the query with UNION SELECT."
3. Reconnaissance Pipeline: Passive OSINT to Active Scanning
Before launching attack modules, Deep Eye performs a comprehensive reconnaissance phase that rivals dedicated OSINT toolkits. It begins with passive DNS resolution and subdomain enumeration using crt.sh certificate transparency logs, followed by Google Dorks (automated search queries targeting exposed admin panels, error messages, and test environments), and finally GitHub code monitoring for accidentally committed secrets or internal documentation. This pipeline can be extended with external APIs like Shodan or SecurityTrails via plugin hooks.
Step‑by‑Step Guide: Execution and Integration Commands
1. Run full reconnaissance pipeline python deep_eye.py -u target.com --recon --subdomain-enum --dns-bruteforce <ol> <li>Export findings for manual review or integration with other tools Subdomain list saved to outputs/target.com/subdomains.txt GitHub leaks saved to outputs/target.com/github_leaks.json</p></li> <li><p>Combine Deep Eye with traditional reconnaissance tools (e.g., naabu for port scanning) naabu -host target.com -top-ports 1000 -o ports.txt python deep_eye.py -u target.com --input-ports ports.txt --crawl</p></li> <li><p>Enable continuous monitoring mode (scans every 6 hours, reports on new findings) python deep_eye.py -u target.com --monitor --interval 21600
- Extending Deep Eye with Custom Payload Modules and Automation Plugins
Deep Eye’s plugin architecture allows security testers to write custom modules for proprietary vulnerability classes or internal infrastructure. Plugins are Python classes that inherit from the `BaseModule` parent, defining `attack()` and `validate()` methods. The AI engine can invoke these custom modules when it detects specific technology fingerprints (e.g., a custom CMS or legacy banking API). The scanning framework also supports integration with Burp Suite via a shared proxy configuration, enabling testers to route traffic through Burp while Deep Eye performs AI-driven fuzzing.
Step‑by‑Step Guide: Writing a Custom Plugin
custom_plugin.py
from modules.base import BaseModule
class MyCustomAuthBypass(BaseModule):
name = "Custom Auth Bypass"
severity = "Critical"
def attack(self, url, session):
AI-assisted payload generation
prompt = f"URL: {url}\nGenerate an authentication bypass token for a JWT-based API."
payload = self.ai.query(prompt)
response = session.get(url, headers={"Authorization": f"Bearer {payload}"})
return response
def validate(self, response):
return "admin" in response.text and response.status_code == 200
Register and run custom plugin python deep_eye.py -u https://target.com --load-plugin custom_plugin.py --module "Custom Auth Bypass"
For evasion tactics against WAF or IDS systems, Deep Eye incorporates request mutation and IP rotation through proxy chains. The AI layer selects mutation strategies based on the detection messages returned by the WAF, essentially performing a black-box bypass loop that mimics human intuition.
5. Automated Reporting and Bug Bounty Workflow Integration
Upon completing a scan, Deep Eye generates detailed HTML, JSON, or PDF reports containing:
– Vulnerability title and OWASP classification (including API Top 10 2023 labels)
– Request/Response evidence (screenshots and HTTP transcripts)
– AI-generated explanation of the vulnerability’s root cause
– Remediation steps contextualized to the discovered technology stack
– CVSS v3.1 scoring automatically calculated
Step‑by‑Step Guide: Generating Reports and Integrating with Bug Bounty Platforms
Generate actionable bug bounty report python deep_eye.py -u https://target.com --report-format bugcrowd --output report.html Upload findings to HackerOne or Bugcrowd via API (use webhook integration) python deep_eye.py --webhook https://api.bugcrowd.com/v1/submissions --api-key $BC_API_KEY Generate markdown report for manual submission python deep_eye.py -u https://target.com --report-format markdown --output findings.md
6. Defensive Countermeasures and Hardening Against AI‑Powered Scanners
While Deep Eye empowers penetration testers, defenders must understand how to detect and mitigate automated AI‑powered scanning.
– Rate limiting with progressive delays (not static thresholds) frustrates AI‑driven timing‑based detection.
– Behavioral analysis at the WAF layer – detecting LLM‑specific patterns (e.g., prompt injection strings containing “You are a security scanner”) – can block AI agents.
– API schema enforcement and strict input validation reduce BOLA and property manipulation attacks, which remain the most commonly successful AI‑facilitated exploits.
Step‑by‑Step Guide: Hardening an API Against Deep Eye‑Style Scans (Linux Commands for Nginx + ModSecurity)
1. Configure rate limiting for API endpoints (Nginx)
/etc/nginx/sites-available/api
limit_req_zone $binary_remote_addr zone=api_zone:10m rate=10r/m;
location /api/ {
limit_req zone=api_zone burst=5 nodelay;
WAF rule for prompt injection detection
modsecurity_rules '
SecRule ARGS "You are a(?:n)? (?:security|penetration|vulnerability) scanner" "id:1001,deny,status:403"
';
}
<ol>
<li>Implement JSON schema validation on API endpoints (Python example with Flask)
from jsonschema import validate
schema = {"type": "object", "properties": {"user_id": {"type": "integer"}}, "additionalProperties": False}
validate(instance=request.json, schema=schema) Rejects payloads with unexpected fields</p></li>
<li><p>Test your patches using Deep Eye in ""safe"" mode
python deep_eye.py -u https://your-api.com --test-defences
What Undercode Say
- AI in Offensive Security Has Crossed the Tipping Point – Tools like Deep Eye demonstrate that LLMs can now perform legitimate vulnerability discovery without human guidance, as the 70 new AI penetration testing tools cataloged in 2026 prove.
- Automation Multiplies, Not Replaces, Human Expertise – Deep Eye handles reconnaissance and initial payload generation, but skilled bug bounty hunters still outperform pure automation on business logic flaws and chained exploits. The tool reduces time-to-first-bounty but does not eliminate critical thinking.
- Defenders Must Adapt Offensive AI Defenses – Traditional WAF rules fail against context‑aware AI payloads. Behavioral anomaly detection and LLM‑specific rule sets are becoming essential components of API security postures in 2026.
Prediction
- By 2028, autonomous AI pentesting agents will outnumber human penetration testers in routine security operations, shifting human roles to threat modeling, complex exploit chaining, and remediation architecture.
- Enterprises will mandatorily adopt “red vs. blue AI” systems—where defensive AI monitors for offensive AI scanning patterns—creating an entirely new product category within DevSecOps.
- Regulatory bodies will likely mandate disclosure of AI‑assisted vulnerability discovery in bug bounty reports, influencing how platforms like HackerOne and Bugcrowd structure their submissions and payout models.
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IT/Security Reporter URL:
Reported By: Syed Muneeb – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
