BALASUBRAMANIYAN B - Cybersecurity Specialist | ContraWork by BALASUBRAMANIYAN B
BALASUBRAMANIYAN B

BALASUBRAMANIYAN B

Full-Stack Developer who thinks like a hacker

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BALASUBRAMANIYAN is ready for their next project!

Cover image for 🚀 Building the Ultimate Bug
🚀 Building the Ultimate Bug Bounty Engagement Console & Dashboard! Hey Contra Community! 👋 I'm building a passion project to supercharge security audits: The Professional Bug Bounty Engagement Dashboard. As security researchers, we juggle command-line tools, wordlists, payloads, screenshots, and reports spread across multiple folders. I wanted a unified local control center that bridges raw terminal workflows with a premium, high-fidelity UI. What it does 🛠️ Tech Stack Backend: Node.js + Express.js, running locally with direct file read/write access. Frontend: Vanilla JavaScript + custom CSS for a fast, responsive experience. Data flow: No database at all — everything serializes into clean markdown, text, and JSON inside local workspace folders like notes/, reports/, checklists/, and recon/. 🔥 Core features in progress Checklist presets & evidence linker Load presets (OWASP Web Top 10, API Security, Quick Recon) and link proof screenshots directly to checklist items. Hovering over a task shows an instant evidence preview. DNS OSINT records & live SVG topology map Parallel queries for CNAME, A, AAAA, MX, NS, and TXT records, rendered as an interactive SVG node map. Vulnerability report compiler & CVSS v3.1 engine Interactive calculator for vector strings and severity scores, plus OWASP finding templates and direct Burp Suite XML imports. HTML5 Canvas proof editor Edit PoC screenshots in-browser (red boxes, arrows, annotations) and save them straight back to disk. Automation runners console Trigger Selenium web tests or local Node, Python, PowerShell, and Bash scripts, with live terminal output streaming into the dashboard. Next build phase 💡 I’m now polishing WAF bypass obfuscation encoders and adding bulk target queues to chain scans across large subdomain lists. I’m building this as a local-first, privacy-preserving toolkit for professional bug bounty hunters and VAPT teams, and I’d love to evolve it with real-world feedback. How do you currently organize your hacking/assessment workspace, and what would be a must-have feature for you in a console like this? 👇
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Cover image for Contributed to a full-stack phishing
Contributed to a full-stack phishing simulation and security awareness platform for enterprise clients as part of my role at Terra System Labs. Built components including a campaign engine, analytics dashboard, and automated reporting pipeline. Specific implementation details and client information withheld due to confidentiality.
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Cover image for Title: LH Clothing — Luxury
Title: LH Clothing — Luxury Streetwear E-Commerce Platform Description: Full-stack e-commerce build for a streetwear brand, including product management and a responsive storefront. Tools: React.js, Node.js
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Cover image for Title: Drift Enterprises — E-Commerce
Title: Drift Enterprises — E-Commerce Platform Description: Responsive full-stack e-commerce platform with product catalog, cart, and checkout flow. Tools: React.js, Node.js
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Cover image for Title: Sentinel-SDK — Black-Box Security
Title: Sentinel-SDK — Black-Box Security Auditing Framework Description: Modular, plugin-based vulnerability detection framework covering OWASP Top 10 categories (SQLi, XSS, SSRF, IDOR). Detected 18 vulnerabilities missed by automated scanners across 3 client assessments. Tools: Python
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Cover image for Title: RREF — RedOps Recon
Title: RREF — RedOps Recon & Exploitation Framework Description: Personal offensive security toolkit automating reconnaissance (port scanning, subdomain enumeration, service fingerprinting), exploitation, and report generation. Deployed across 4 Red Team engagements, cutting pentest cycle time by ~35%. Tools: Python, Bash
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Cover image for Title: Phantera — Insider Threat
Title: Phantera — Insider Threat Detection Description: Behavioral biometrics anomaly detection system using keystroke and mouse dynamics to flag insider threats. Achieved 89% detection accuracy in lab testing. FastAPI backend with a Streamlit dashboard for real-time monitoring. Tools: Python, FastAPI, Streamlit, Machine Learning
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