Hey there 👋, in this second blog post I will be going over a few other challenges from the Braeker CTF
Eye doctor #
deconvolute an image, wrong answers only
Before I start this one, yes I know there was a tool perfectly tailored to solve the problem.
Now that this is out of the way let me tell you the cheap and dirty way I did it :
Since the emboss function in gimp is using sort of the same principle I started by adjusting the angle of the convolution (finding 30 degrees) and then played around with the depth and elevation :
Once I got to the point where it looked like it could be somewhat readable (remember “steganography” is just a spiffingly posh synonym of “guessing”) I played with saturation and contrast to try to make it as distinguishable as possible and settled on :
You may say :
- That’s ugly
- That’s as unscientific a method as possible
- That color choice is currently burning my eyes
And you’d be completely right, yet with this picture we managed to get the flag :
brck{4ppr04ch1tfr0M4D1ff3r3ntAngl3}although as the following screenshot of our team’s conversation may convey, the guessing was still very much present :
Thus spoke machine #
This cryptic challenge description tells us about a bot that reads books (hint !!!) and speaks in ciphers. We are given a flag with the following cipher :
brck{1746200913432170593.11_1740398198542172490.3_789837700517945346.13}
It does not take much time to guess that this is a book cipher.
A book cipher is a way of encrypting data by substituting words based on a common book/text. An example of book cipher would be for the people trying to secretly communicate to pick a very common book (like the scriptures for instance) and send to one another pairs of (page, word). For instance if the first “word” wad (123,42), the receiver would go the page 123 and pick the 42nd word.
Now comes the problem, we don’t know the book, and when looking at the values, the second looks like a word number (between 0 and 15) but the first is probably not one (unless perhaps considering books such as the Library of babel which I would have though at first).
As one of our team member (who also happens to be my dear and beloved wife) randomly searched on the internet for the string she realized it produces an X result, as these look a lot like statuses IDs.
Starting there it was just a matter of inputting the number and using Qwant’s site: syntax we were able to find directly all the links and get the three words that form the flag :
Ex : for the first one we run this search : https://www.qwant.com/?client=ext-firefox-sb&q=site%3A%22twitter.com%22+1746200913432170593&t=web (search : site:"twitter.com" 1746200913432170593). With this we find the following tweet :
— Journal of Astrological Big Data Ecology (@JABDE6) January 13, 2024