Security Engineer


Sunny Mishra is an passionate geek who loves to break stuff and then make it again, with interests in cloud infrastructure, network security, reverse engineering, malware analysis and exploit development.

  • Exploit Development
  • Reverse Engineering
  • Network Pentesting
  • Web Application Pentesting
  • Cloud Infrastructure
  • M.Sc Computer Science, 2022

    South Asian University

  • B.Sc. Hons Computer Science, 2020

    College of Vocational Studies, University of Delhi


Exploit Development
Reverse Engineering
Cloud Computing


Security Researcher
Jan 2022 – Sep 2022 New Delhi, India
DevSecOps Engineer
Oct 2022 – Nov 2023 New Delhi, India
Platform Security Engineer
Nov 2023 – Present New Delhi, India


Offensive Security Certified Professional (OSCP)
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Blue belt in hacking @
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Hacking and Patching
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Architecting with Google Compute Engine Specialization
See certificate
Kubernetes in the Google Cloud
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All 12 Active Badges
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A collection of docker images which can be used to spin up an exploit development environment based on the libc version quickly to test and develop binary exploitation ctf challenges.


A simple cli application to download pdf of research papers from sci-hub

Corona Stats

A basic cli to get live corona stats written in go

RTB CTF Framework

An framework written in python flask created to help organizing and managing Root the box style CTF’s

Train Enquiry System

A Desktop Application written in java using Swing UI, to get live status of trains in India using API provided by

Xor Encryptor

A Basic file encryptor written using C++ with UI provided by QT framework

Recent Publications

Explainable Fuzzy AI Challenge 2022: Winner’s Approach to a Computationally Efficient and Explainable Solution
An explainable artificial intelligence (XAI) agent is an autonomous agent that uses a fundamental XAI model at its core to perceive its environment and suggests actions to be performed. One of the significant challenges for these XAI agents is performing their operation efficiently, which is governed by the underlying inference and optimization system. Along similar lines, an Explainable Fuzzy AI Challenge (XFC 2022) competition was launched, whose principal objective was to develop a fully autonomous and optimized XAI algorithm that could play the Python arcade game “Asteroid Smasher”. This research first investigates inference models to implement an efficient (XAI) agent using rule-based fuzzy systems. We also discuss the proposed approach (which won the competition) to attain efficiency in the XAI algorithm. We have explored the potential of the widely used Mamdani- and TSK-based fuzzy inference systems and investigated which model might have a more optimized implementation. Even though the TSK-based model outperforms Mamdani in several applications, no empirical evidence suggests this will also be applicable in implementing an XAI agent. The experimentations are then performed to find a better-performing inference system in a fast-paced environment. The thorough analysis recommends more robust and efficient TSK-based XAI agents than Mamdani-based fuzzy inference systems.