File size: 2,473 Bytes
2050697 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 | # NYU CTF Bench
This repository hosts the NYU CTF Bench, a collection of CTF challenges from the CSAW CTF competitions, designed for evaluation of LLM agents.
The CTF challenges are dockerized and easily deployable to allow an LLM-based automation framework to interact with the challenge and attempt a solution.
The main benchmark dataset contains 200 challenges across 6 CTF categories: web, binary exploitation (pwn), forensics, reverse engineering (rev), cryptography (crypto), and miscellaneous (misc).
## Benchmark structure
The `test/` folder contains the main benchmark dataset of 200 challenges. A smaller development set of 55 challenges is present in the `development/` folder.
The development set can be treated equivalent to a "train" split and used for building the agent, so that design decisions made to improve the agent do not bias the test scores.
The folder structure is as follows: `<year>/<event>/<category>/<challenge>`.
`<year>` is the year of the competition, `<event>` is either "CSAW-Quals" or "CSAW-Finals", `<category>` is among the 6 categories, and `<challenge>` is the challenge name.
Note that the challenge name may have spaces and single-quotes, so it is advisable to wrap it in double-quotes when using in scripts.
Each challenge contains a `challenge.json` containing the metadata of the challenge, and the corresponding challenge files.
Challenges that require a server to host some challenge files are set up with a docker image, and a `docker-compose.yaml` file.
The docker image is loaded directly using `docker compose up`.
## Setup
Install the python package:
```
pip install nyuctf
```
The repository is automatically cloned when the `CTFDataset` is first instantiated with the `split` argument.
If needed, you can manually clone it by running:
```
python3 -m nyuctf.download
```
## Usage
The following python snippet shows how to load challenge details using the python module:
```
from nyuctf.dataset import CTFDataset
from nyuctf.challenge import CTFChallenge
# Clones the repository for the first time, which takes a while
ds = CTFDataset(split="test")
chal = CTFChallenge(ds.get("2021f-rev-maze"), ds.basedir)
print(chal.name)
print(chal.flag)
print(chal.files)
```
## Tests
Run tests on the challenges, for docker setup and network connection.
Requires the docker network to be setup.
```
cd python
python -m unittest -v test.test_challenges
```
Optionally filter the tests with the unittest `-k` flag.
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