Skip to main content

HTML Logic Form Obfuscating Password

I'm attempting to troubleshoot a login issue on an old system that noone here is very familiar with. We have what we believe to be the admin password, but it isn't working. I'm just grasping, but I thought maybe a browser issue, considering how old the system is, so I tried using Postman to see what kind of response I get, which resulted in a failure.

However, I'm noticing now that they seem to be using some method to obfuscate the password, and I don't really understand what it's doing.

The login form method is this.

<form method="post" name='login' action="/?language=en" onsubmit="document.getElementById('submit').disabled = true; document.getElementById('pass').value = CJMaker.makeString(document.getElementById('pass').value);" >

and the CJMaker file contains this.

function CJMaker(e)
{}function _CJMaker_makeString(val)
{if (val == null)
{return val;}var size = val.length;var retVal = new String();var conVal = new String();for (var i = 0; i < size; i++)
{var current = val.charCodeAt(i);current = current^4;conVal += String.fromCharCode(current);}for (var i = size - 1; i >= 0; i--)
{retVal += conVal.charAt(i);}return retVal;}CJMaker.makeString = _CJMaker_makeString;

So it looks like it's just using char codes, and I suspect that the password in the database isn't the actual password, but instead is whatever this would create.

I'm afraid I just do not understand this well enough to reverse it. I'm sure it's simple to some of you javascript guys though.

Can anyone tell me more about what this is doing?

Via Active questions tagged javascript - Stack Overflow https://ift.tt/2FdjaAW

Comments

Popular posts from this blog

ValueError: X has 10 features, but LinearRegression is expecting 1 features as input

So, I am trying to predict the model but its throwing error like it has 10 features but it expacts only 1. So I am confused can anyone help me with it? more importantly its not working for me when my friend runs it. It works perfectly fine dose anyone know the reason about it? cv = KFold(n_splits = 10) all_loss = [] for i in range(9): # 1st for loop over polynomial orders poly_order = i X_train = make_polynomial(x, poly_order) loss_at_order = [] # initiate a set to collect loss for CV for train_index, test_index in cv.split(X_train): print('TRAIN:', train_index, 'TEST:', test_index) X_train_cv, X_test_cv = X_train[train_index], X_test[test_index] t_train_cv, t_test_cv = t[train_index], t[test_index] reg.fit(X_train_cv, t_train_cv) loss_at_order.append(np.mean((t_test_cv - reg.predict(X_test_cv))**2)) # collect loss at fold all_loss.append(np.mean(loss_at_order)) # collect loss at order plt.plot(np.log(al...

Sorting large arrays of big numeric stings

I was solving bigSorting() problem from hackerrank: Consider an array of numeric strings where each string is a positive number with anywhere from to digits. Sort the array's elements in non-decreasing, or ascending order of their integer values and return the sorted array. I know it works as follows: def bigSorting(unsorted): return sorted(unsorted, key=int) But I didnt guess this approach earlier. Initially I tried below: def bigSorting(unsorted): int_unsorted = [int(i) for i in unsorted] int_sorted = sorted(int_unsorted) return [str(i) for i in int_sorted] However, for some of the test cases, it was showing time limit exceeded. Why is it so? PS: I dont know exactly what those test cases were as hacker rank does not reveal all test cases. source https://stackoverflow.com/questions/73007397/sorting-large-arrays-of-big-numeric-stings

How to load Javascript with imported modules?

I am trying to import modules from tensorflowjs, and below is my code. test.html <!DOCTYPE html> <html lang="en"> <head> <meta charset="UTF-8"> <title>Document</title </head> <body> <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@2.0.0/dist/tf.min.js"></script> <script type="module" src="./test.js"></script> </body> </html> test.js import * as tf from "./node_modules/@tensorflow/tfjs"; import {loadGraphModel} from "./node_modules/@tensorflow/tfjs-converter"; const MODEL_URL = './model.json'; const model = await loadGraphModel(MODEL_URL); const cat = document.getElementById('cat'); model.execute(tf.browser.fromPixels(cat)); Besides, I run the server using python -m http.server in my command prompt(Windows 10), and this is the error prompt in the console log of my browser: Failed to loa...