Project Sekai
🔒 UMDCTF 2023 / ✅-ml-elgyems-password
for i in range(10000):
output = model(input_tensor)
loss = lossfunc(output, target_output)
loss.backward()
optimizer.step()
optimizer.zero_grad()
if torch.allclose(target_output, output, rtol=1e-10, atol=1e-10):
break
print("Input:", tensor_to_ascii(input_tensor), "Loss: ", loss.item()) rn i get it going to loss of inf 
def tensor_to_ascii(t):
data = ""
for x in t:
try:
data += chr(int(x.item()))
except:
data += "\x00"
return data 
output = open("model (1).txt").read()
outputs = open("outputs (2).txt").read()
def rm_empty(l):
if '' in l:
l.remove('')
return l
from fractions import Fraction
def parse_data(d):
dn = rm_empty(d.split("\n"))
lr = dn[0]
d2 = ("\n".join(dn[1:])).split("weights")
d2 = rm_empty(d2)
biases = d2[0]
biases = rm_empty(biases.split("\n"))[1:][0]
biases = eval('[Fraction("' + biases.replace(" ", '"), Fraction("') + '")]')
weights = rm_empty(d2[1].split("\n"))
for i in range(len(weights)):
weights[i] = eval('[Fraction("' + weights[i].replace(" ", '"), Fraction("') + '")]')
return lr, biases, weights
weights = []
biases = []
for x in range(len(data)):
_, lrbiases, lrweights = parse_data(data[x])
weights.append(lrweights)
biases.append(lrbiases)
target_output = eval('[Fraction("' + outputs.replace(" ", '"), Fraction("') + '")]')
prefix = "UMDCTF{"
suffix = "}"
input_tensor = torch.zeros(22, dtype=torch.float64)
for i in range(len(prefix)):
input_tensor[i] = ord(prefix[i])
input_tensor[-1] = ord(suffix)
for i in range(len(prefix), len(input_tensor) - 1):
input_tensor[i] = torch.randint(low=32, high=127, size=(1,), dtype=torch.float64)
input_tensor.requires_grad = True
MSE = nn.MSELoss()
from torch.optim import *
for x in range(0,10):
optimizer = RMSprop([input_tensor], lr=0.1**x)
for i in range(1000):
print(loss)
output = model(input_tensor)
loss = MSE(output, target_output)
loss.backward()
optimizer.step()
optimizer.zero_grad()
if torch.allclose(output, target_output, rtol=1e-10, atol=1e-10):
break
print(tensor_to_ascii(input_tensor)) 
output = open("model (1).txt").read()
outputs = open("outputs (2).txt").read()
data = output.split("layer")
import torch
import numpy as np
def rm_empty(l):
if '' in l:
l.remove('')
return l
def tensor_to_ascii(t):
data = ""
for x in t:
try:
data += chr(int(x.item()))
except:
data += "\x00"
return data
data = rm_empty(data)
from torch import tensor
def parse_data(d):
dn = rm_empty(d.split("\n"))
lr = dn[0]
d2 = ("\n".join(dn[1:])).split("weights")
d2 = rm_empty(d2)
biases = d2[0]
biases = rm_empty(biases.split("\n"))[1:][0]
biases = tensor(eval('[np.float64(' + biases.replace(" ", '), np.float64(').replace('/', ')/np.float64(') + ')]'), dtype=torch.float64)
weights = rm_empty(d2[1].split("\n"))
for i in range(len(weights)):
weights[i] = eval('[np.float64(' + weights[i].replace(" ", '), np.float64(').replace('/', ')/np.float64(') + ')]')
return lr, biases, tensor(weights, dtype=torch.float64)
weights = []
biases = []
for x in range(len(data)):
_, lrbiases, lrweights = parse_data(data[x])
weights.append(lrweights)
biases.append(lrbiases)
from torch import nn
model = nn.Sequential(
nn.Linear(22, 69),
nn.Linear(69, 420),
nn.Linear(420, 800),
nn.Linear(800, 85),
nn.Linear(85, 13),
nn.Linear(13, 37)
)
def tensor_to_ascii(t):
data = ""
for x in t:
try:
data += chr(round(x.item()))
except:
data += "\x00"
return data
for i, layer in enumerate(model):
if isinstance(layer, nn.Linear):
layer.weight = nn.Parameter(weights[i])
layer.biases = nn.Parameter(biases[i])
target_output = tensor(eval('[np.float64(' + outputs.replace(" ", '), np.float64(').replace('/', ')/np.float64(') + ')]'), dtype=torch.float64) 
output = open("model (1).txt").read()
outputs = open("outputs (2).txt").read()
data = output.split("layer")
import torch
import numpy as np
def rm_empty(l):
if '' in l:
l.remove('')
return l
def tensor_to_ascii(t):
data = ""
for x in t:
try:
data += chr(int(x.item()))
except:
data += "\x00"
return data
data = rm_empty(data)
from torch import tensor
def parse_data(d):
dn = rm_empty(d.split("\n"))
lr = dn[0]
d2 = ("\n".join(dn[1:])).split("weights")
d2 = rm_empty(d2)
biases = d2[0]
biases = rm_empty(biases.split("\n"))[1:][0]
biases = tensor(eval('[np.float64(' + biases.replace(" ", '), np.float64(').replace('/', ')/np.float64(') + ')]'), dtype=torch.float64)
weights = rm_empty(d2[1].split("\n"))
for i in range(len(weights)):
weights[i] = eval('[np.float64(' + weights[i].replace(" ", '), np.float64(').replace('/', ')/np.float64(') + ')]')
return lr, biases, tensor(weights, dtype=torch.float64)
weights = []
biases = []
for x in range(len(data)):
_, lrbiases, lrweights = parse_data(data[x])
weights.append(lrweights)
biases.append(lrbiases)
from torch import nn
model = nn.Sequential(
nn.Linear(22, 69),
nn.Linear(69, 420),
nn.Linear(420, 800),
nn.Linear(800, 85),
nn.Linear(85, 13),
nn.Linear(13, 37)
)
def tensor_to_ascii(t):
data = ""
for x in t:
try:
data += chr(round(x.item()))
except:
data += "\x00"
return data
for i, layer in enumerate(model):
if isinstance(layer, nn.Linear):
layer.weight = nn.Parameter(weights[i])
layer.biases = nn.Parameter(biases[i])
target_output = tensor(eval('[np.float64(' + outputs.replace(" ", '), np.float64(').replace('/', ')/np.float64(') + ')]'), dtype=torch.float64) target_output = tensor(eval('[np.float64(' + outputs.replace(" ", '), np.float64(').replace('/', ')/np.float64(') + ')]'), dtype=torch.float64) target_output = eval('[np.float64(' + outputs.replace(" ", '), np.float64(').replace('/', ')/np.float64(') + ')]')
W6(W5(W4(W3(W2(W1x+b1)+b2)+b3)+b4+b5)+b6=output
just expand it, get it to Ax=b then solve W6W5W4W3W2W1x+W6W5W4W3W2b1+W6W5W4W3b2+W6W5W4b3+W6W5b4+W6b5+b6=output sympy.matrices.common.NonInvertibleMatrixError: Matrix det == 0; not invertible. 
[[ 50.42092424]
[101.03269245]
[ 26.71849702]
[-36.88773564]
[-76.61594413]
[ 44.39960158]
[ 68.71135152]
[ 36.33306337]
[ 4.41593796]
[119.26109294]
[ 58.48153408]
[ 85.9846144 ]
[ 41.13197924]
[ 27.54137431]
[ 32.35543824]
[ 8.57326218]
[ 22.02081603]
[-24.8457248 ]
[ 77.91928158]
[ 68.94507925]
[ 97.63596957]
[ 47.37988401]] [[ 50.42092424]
[101.03269245]
[ 26.71849702]
[-36.88773564]
[-76.61594413]
[ 44.39960158]
[ 68.71135152]
[ 36.33306337]
[ 4.41593796]
[119.26109294]
[ 58.48153408]
[ 85.9846144 ]
[ 41.13197924]
[ 27.54137431]
[ 32.35543824]
[ 8.57326218]
[ 22.02081603]
[-24.8457248 ]
[ 77.91928158]
[ 68.94507925]
[ 97.63596957]
[ 47.37988401]] 
