HTB Cyber Apocalypse CTF 2025: Tales from Eldoria - AI ML - Enchanted Weights

Unearthing the Crystal Runes: Decoding the eldorian_artifact.pth Challenge

In this challenge, we were tasked with exploring the depths of Eldoria's Crystal Archives and deciphering the secrets hidden within a mystical artifact – an enchanted neural crystal named eldorian_artifact.pth. Legends whispered of a hidden incantation, an ancient secret flag, directly woven into its crystalline structure. Our mission: to analyze this neural network model file and extract the elusive flag in the format HTB{REDACTED}.

Initial Exploration: What Lies Within the .pth File?

Armed with only the eldorian_artifact.pth file and minimal context, our first step was to determine the nature of this artifact. Knowing that Python, particularly with the PyTorch library (link: https://pytorch.org/, PyTorch is a popular open-source machine learning framework widely used for tasks like deep learning and neural network development), is a powerful tool for analyzing AI models, we opted for a Python-based investigation.

For those unfamiliar with Machine Learning (ML) or Artificial Intelligence (AI), don't worry! Modern AI development environments like Windsurf or Cursor (AI-powered IDEs) can significantly simplify the process. By simply placing the .pth file in our project directory and leveraging the IDE's AI capabilities, we could begin our analysis.

Attempting Direct Model Loading (and Learning About State Dictionaries):

Our initial instinct was to load the .pth file directly as a PyTorch model:

import torch

model = torch.load('eldorian_artifact.pth')
model.eval()