> For the complete documentation index, see [llms.txt](https://mainekhacker-1.gitbook.io/mainekhacker/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://mainekhacker-1.gitbook.io/mainekhacker/aboutme/about-me.md).

# About Me

An engineer building toward a career in Vulnerability Assessment and Penetration Testing (VAPT). I focus on finding real bugs, building my own security tooling, and documenting the process so other learners can follow along.

**What I do:**

* Vulnerability Assessment & Penetration Testing (VAPT)
* Firmware and IoT security research
* Bug bounty hunting 
* Building security tools

**A few numbers:**

* Top 1% ranked on TryHackMe
* Found Responsible disclosures to hardware vendors 
* Self-built security tools, actively growing

**Certifications:** Google Cybersecurity Professional · Cisco Ethical Hacker · Certified Red Team Operations Management

**Find me elsewhere:** \[[Substack](https://mainekhacker.substack.com/)]  · \[[Medium](https://mainekhacker.medium.com/)] · \[[GitHub](https://github.com/chaitanya-hack1O1)] 

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## Projects

A living collection of tools and systems I'm building - this list grows as new projects get finished.

***

### FirmSec — Firmware Vulnerability Scanner

**Problem** Router and IoT firmware often ships with hardcoded credentials, outdated libraries, and exposed keys — but most people don't have an easy way to check a firmware image before or after it's deployed.

**What I built** FirmSec is a Python-based static analysis tool that unpacks firmware images and scans them for known-vulnerable components, embedded secrets, and weak configurations. Version 3.0 added a five-tier confidence scoring system, atomic cache writes, and expanded CPE mappings for more accurate CVE matching.

**Tech stack** Python, binwalk, CVE/NVD lookups, custom CPE mapping engine

**What I found** Tested against real firmware from Netgear (R6260, R6350), TP-Link (Archer C6, TL-WR845N), Xiaomi MiWiFi, and Tenda AC10. What I found inside it included embedded private keys, root password hashes, outdated libraries with known CVEs, and plaintext credential transmission in multiple images. Two of these findings led to responsible disclosures.

**Links:** [GitHub repo](https://github.com/chaitanya-hack1O1/Firmsec-IoT-Firmware-Vulnerability-Scanner)

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### SOC\_DashB — SIEM Dashboard with ML-Based Detection

**Problem:** Most beginner SOC projects just parse logs and show a table. I wanted something closer to a real SIEM  one that actually flags anomalies instead of just displaying data.

**What I built:** A SIEM dashboard with real rsyslog-based log ingestion, a forensic rules engine (10 detection rules), MITRE ATT\&CK-mapped alerting, an Isolation Forest ML model for anomaly detection, and PCAP upload/analysis via Scapy.

**Tech stack:** Python, rsyslog, Scikit-learn (Isolation Forest), Scapy, MITRE ATT\&CK framework

**What I found/proved:** Built and validated the detection pipeline end-to-end — log ingestion, rule-based alerting, ML-based anomaly scoring, and MITRE technique mapping — across two development phases (foundation, then ML engine).

**Links** GitHub repo

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### HASHHOUND 

**What I built: Hash**Hound is a lightweight command-line tool for identifying and cracking common unsalted hashes with a local wordlist. It can process a single hash or a file containing many mixed hash types, and it can also detect and decode Base64-encoded text.

**Tech stack** Python

**What I found/proved** 

* Identifies common hash types by digest length
* Cracks hashes using a local wordlist
* Supports single-hash mode and batch file mode
* Handles mixed hash files such as MD5, SHA1, SHA256, and SHA512 together
* Detects and decodes Base64 text
* Skips blank lines in hash files
* Shows line numbers for batch results
* Uses constant-time comparison with `hmac.compare_digest`

**Links** [GitHub repo](https://github.com/chaitanya-hack1O1/Hash-Hound-)

***