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This writeup outlines a solution for Locking Mechanism and also my thought process in solving it. This was my first time doing a CTF and my very first solve ever.

We're given a Dockerized Rust application, but I decided to build the code directly on my machine since I already had Cargo installed and up to date. I'm not that well-versed in Rust yet (I solved a few Advent of Code problems in it), but I know enough to poke around. The first thing I tried was to just cargo build and cargo run the application and see what happens. It didn't halt, so upon looking at the code it looked like it was expecting an input of comma-separated integers.

In the file I noticed that there was a list of 104 integers, particularly this:

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#include <Arduino.h>

struct BCD {
  int eights;
  int fours;
  int twos;
  int ones;
};

struct BCD decimal_to_BCD(int value) {

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import random

import networkx as nx


def random_graph() -> nx.Graph:
    num_nodes = 20
    probability = 0.2

    return nx.erdos_renyi_graph(num_nodes, probability)

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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("t5-base")
model = AutoModelForSeq2SeqLM.from_pretrained("t5-base")

example_input = "This sentence is false"
input_ids = tokenizer("translate English to German: "+example_input, return_tensors="pt").input_ids
outputs = model.generate(input_ids)
decoded = tokenizer.decode(outputs[0], skip_special_tokens=True)

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import requests
import os
from datetime import datetime, timezone

# Fetch the current time
# Using a trailing "Z" is preferred over the "+00:00" format
now = datetime.now(timezone.utc).isoformat().replace("+00:00", "Z")

# Required fields that each post must include
post = {

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import os
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.models as models
import torchvision.transforms as transforms
import torchvision.datasets as datasets

# Load pre-trained ResNet-18
model = models.resnet18(pretrained=True)

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import pygame

# Initialize Pygame
pygame.init()

# Set up display
width, height = 800, 600
screen = pygame.display.set_mode((width, height))
pygame.display.set_caption("Pygame Glide Transformation Animation")

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from langchain.document_loaders import OnlinePDFLoader
from langchain.indexes import VectorstoreIndexCreator

loader = OnlinePDFLoader("https://arxiv.org/pdf/1606.06353.pdf")
documents = loader.load()
index = VectorstoreIndexCreator().from_documents(documents)

print(index.query("What are the main results of this paper?"))

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from langchain.document_loaders import MastodonTootsLoader
from langchain.indexes import VectorstoreIndexCreator
from langchain.vectorstores.utils import filter_complex_metadata

loader = MastodonTootsLoader(mastodon_accounts=["@vsaraph@mathstodon.xyz"], number_toots=300)
documents = loader.load()
documents = filter_complex_metadata(documents)
index = VectorstoreIndexCreator().from_documents(documents)

print(index.query("What kinds of interests does this user have?"))

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import numpy as np
import torch
from datetime import datetime


if __name__ == '__main__':
    num_dims = 3
    dim = 1024

    for _ in range(10):