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Full Video Walkthrough of Hacking the Game Pwn Adventure 3

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Full Video Walkthrough of Hacking the Game Pwn Adventure 3

Pwn Adventure 3 is an online game, created for a hacking CTF competition. Over the past few months I have solved all available challenges and created detailed walkthrough videos. The videos don't just show the solutions, I explain my thought process and show where I got stuck or failed. I'm very proud of the final result. Here is the YouTube Playlist. Below you can find a list of episodes: https://www.youtube.com/playlist?list=PLhixgUqwRTjzzBeFSHXrw... GitHub: https://github.com/LiveOverflow/PwnAdventure3 Pwn Adventure 3: http://www.pwnadventure.com/ Excluding the casual Let's Play episode (which can be skipped), the whole series is less than 4 hours long. * Let's Play/Hack [39:20] * Setup Private Server with Docker [8:42] * Information Gathering / Recon [14:09] * Recover Game Classes with gdb [11:28] * Hooking on Linux with LD_PRELOAD [12:07] * Flying and our first Flag! (Cow King) [6:34] * Teleporting and Hovering (Unbearable Revenge) [9:31] * Find the hidden Golden Eggs [10:26] * Developing a TCP Network Proxy [12:26] * Analyzing the Game Network Protocol [14:48] * Implementing Autoloot with the Proxy [12:33] * Exploiting an Integer Overflow (Fire and Ice) [19:59] * †: Signed and Unsigned Integers - Integer Overflows.5 [3:12] * Analyzing the Blocky Logic Puzzle [10:51] * Failing at Machine Learning (Blocky part 2) [14:34] * Reversing Input Validation (Keygen part 1) [12:27] * Reversing Custom Encoding (Keygen part 2) [16:01] * Understanding the Key Verification Algorithm (Keygen part 3) [13:10] * RSA Implemented in Assembler (Keygen part 4) [16:23] * RSA Implemented in JavaScript (Keygen part 5) [4:32] * The Last Flag (Overachiever) [5:31] And please also checkout the rest of my channel LiveOverflow: https://www.youtube.com/LiveOverflowCTF

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Indie HackersFits the IH revenue-focused audience · Strong signals: created · Missing: supports, reddit linkedin, podcasting
90%90% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive
56%56% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
53%53% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: video, month · Missing: mobile apps, ios, personal
46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: mac, dock, coding · Missing: agents, macos, agent
25%25% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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