脚本小子
约 2260 字大约 8 分钟
2026-01-18
coppersmith
from __future__ import print_function
import time
debug = True
# display matrix picture with 0 and X
def matrix_overview(BB, bound):
for ii in range(BB.dimensions()[0]):
a = ('%02d ' % ii)
for jj in range(BB.dimensions()[1]):
a += '0' if BB[ii,jj] == 0 else 'X'
a += ' '
if BB[ii, ii] >= bound:
a += '~'
print(a)
def coppersmith_howgrave_univariate(pol, modulus, beta, mm, tt, XX):
"""
Coppersmith revisited by Howgrave-Graham
finds a solution if:
* b|modulus, b >= modulus^beta , 0 < beta <= 1
* |x| < XX
"""
#
# init
#
dd = pol.degree()
nn = dd * mm + tt
#
# checks
#
if not 0 < beta <= 1:
raise ValueError("beta should belongs in (0, 1]")
if not pol.is_monic():
raise ArithmeticError("Polynomial must be monic.")
#
# calculate bounds and display them
#
"""
* we want to find g(x) such that ||g(xX)|| <= b^m / sqrt(n)
* we know LLL will give us a short vector v such that:
||v|| <= 2^((n - 1)/4) * det(L)^(1/n)
* we will use that vector as a coefficient vector for our g(x)
* so we want to satisfy:
2^((n - 1)/4) * det(L)^(1/n) < N^(beta*m) / sqrt(n)
so we can obtain ||v|| < N^(beta*m) / sqrt(n) <= b^m / sqrt(n)
(it's important to use N because we might not know b)
"""
if debug:
# t optimized?
print("\n# Optimized t?\n")
print("we want X^(n-1) < N^(beta*m) so that each vector is helpful")
cond1 = RR(XX^(nn-1))
print("* X^(n-1) = ", cond1)
cond2 = pow(modulus, beta*mm)
print("* N^(beta*m) = ", cond2)
print("* X^(n-1) < N^(beta*m) \n-> GOOD" if cond1 < cond2 else "* X^(n-1) >= N^(beta*m) \n-> NOT GOOD")
# bound for X
print("\n# X bound respected?\n")
print("we want X <= N^(((2*beta*m)/(n-1)) - ((delta*m*(m+1))/(n*(n-1)))) / 2 = M")
print("* X =", XX)
cond2 = RR(modulus^(((2*beta*mm)/(nn-1)) - ((dd*mm*(mm+1))/(nn*(nn-1)))) / 2)
print("* M =", cond2)
print("* X <= M \n-> GOOD" if XX <= cond2 else "* X > M \n-> NOT GOOD")
# solution possible?
print("\n# Solutions possible?\n")
detL = RR(modulus^(dd * mm * (mm + 1) / 2) * XX^(nn * (nn - 1) / 2))
print("we can find a solution if 2^((n - 1)/4) * det(L)^(1/n) < N^(beta*m) / sqrt(n)")
cond1 = RR(2^((nn - 1)/4) * detL^(1/nn))
print("* 2^((n - 1)/4) * det(L)^(1/n) = ", cond1)
cond2 = RR(modulus^(beta*mm) / sqrt(nn))
print("* N^(beta*m) / sqrt(n) = ", cond2)
print("* 2^((n - 1)/4) * det(L)^(1/n) < N^(beta*m) / sqrt(n) \n-> SOLUTION WILL BE FOUND" if cond1 < cond2 else "* 2^((n - 1)/4) * det(L)^(1/n) >= N^(beta*m) / sqroot(n) \n-> NO SOLUTIONS MIGHT BE FOUND (but we never know)")
# warning about X
print("\n# Note that no solutions will be found _for sure_ if you don't respect:\n* |root| < X \n* b >= modulus^beta\n")
#
# Coppersmith revisited algo for univariate
#
# change ring of pol and x
polZ = pol.change_ring(ZZ)
x = polZ.parent().gen()
# compute polynomials
gg = []
for ii in range(mm):
for jj in range(dd):
gg.append((x * XX)**jj * modulus**(mm - ii) * polZ(x * XX)**ii)
for ii in range(tt):
gg.append((x * XX)**ii * polZ(x * XX)**mm)
# construct lattice B
BB = Matrix(ZZ, nn)
for ii in range(nn):
for jj in range(ii+1):
BB[ii, jj] = gg[ii][jj]
# display basis matrix
if debug:
matrix_overview(BB, modulus^mm)
# LLL
BB = BB.LLL()
# transform shortest vector in polynomial
new_pol = 0
for ii in range(nn):
new_pol += x**ii * BB[0, ii] / XX**ii
# factor polynomial
potential_roots = new_pol.roots()
print("potential roots:", potential_roots)
# test roots
roots = []
for root in potential_roots:
if root[0].is_integer():
result = polZ(ZZ(root[0]))
if gcd(modulus, result) >= modulus^beta:
roots.append(ZZ(root[0]))
#
return roots
############################################
# Test on Stereotyped Messages
##########################################
print("//////////////////////////////////")
print("// TEST 1")
print("////////////////////////////////")
# RSA gen options (for the demo)
length_N = 1024 # size of the modulus
Kbits = 200 # size of the root
e = 3
# RSA gen (for the demo)
p = next_prime(2^int(round(length_N/2)))
q = next_prime(p)
N = p*q
ZmodN = Zmod(N)
# Create problem (for the demo)
K = ZZ.random_element(0, 2^Kbits)
Kdigits = K.digits(2)
M = [0]*Kbits + [1]*(length_N-Kbits);
for i in range(len(Kdigits)):
M[i] = Kdigits[i]
M = ZZ(M, 2)
C = ZmodN(M)^e
# Problem to equation (default)
P.<x> = PolynomialRing(ZmodN) #, implementation='NTL')
pol = (2^length_N - 2^Kbits + x)^e - C
dd = pol.degree()
# Tweak those
beta = 1 # b = N
epsilon = beta / 7 # <= beta / 7
mm = ceil(beta**2 / (dd * epsilon)) # optimized value
tt = floor(dd * mm * ((1/beta) - 1)) # optimized value
XX = ceil(N**((beta**2/dd) - epsilon)) # optimized value
# Coppersmith
start_time = time.time()
roots = coppersmith_howgrave_univariate(pol, N, beta, mm, tt, XX)
# output
print("\n# Solutions")
print("we want to find:",str(K))
print("we found:", str(roots))
print(("in: %s seconds " % (time.time() - start_time)))
print("\n")
############################################
# Test on Factoring with High Bits Known
##########################################
print("//////////////////////////////////")
print("// TEST 2")
print("////////////////////////////////")
# RSA gen
length_N = 1024
p = next_prime(2^int(round(length_N/2)))
q = next_prime( round(pi.n()*p) )
N = p*q
# qbar is q + [hidden_size_random]
hidden = 200
diff = ZZ.random_element(0, 2^hidden-1)
qbar = q + diff
F.<x> = PolynomialRing(Zmod(N), implementation='NTL')
pol = x - qbar
dd = pol.degree()
# PLAY WITH THOSE:
beta = 0.5 # we should have q >= N^beta
epsilon = beta / 7 # <= beta/7
mm = ceil(beta**2 / (dd * epsilon)) # optimized
tt = floor(dd * mm * ((1/beta) - 1)) # optimized
XX = ceil(N**((beta**2/dd) - epsilon)) # we should have |diff| < X
# Coppersmith
start_time = time.time()
roots = coppersmith_howgrave_univariate(pol, N, beta, mm, tt, XX)
# output
print("\n# Solutions")
print("we want to find:", qbar - q)
print("we found:", roots)
print(("in: %s seconds " % (time.time() - start_time)))boneh_durfee
from __future__ import print_function
import time
############################################
# Config
##########################################
"""
Setting debug to true will display more informations
about the lattice, the bounds, the vectors...
"""
debug = True
"""
Setting strict to true will stop the algorithm (and
return (-1, -1)) if we don't have a correct
upperbound on the determinant. Note that this
doesn't necesseraly mean that no solutions
will be found since the theoretical upperbound is
usualy far away from actual results. That is why
you should probably use `strict = False`
"""
strict = False
"""
This is experimental, but has provided remarkable results
so far. It tries to reduce the lattice as much as it can
while keeping its efficiency. I see no reason not to use
this option, but if things don't work, you should try
disabling it
"""
helpful_only = True
dimension_min = 7 # stop removing if lattice reaches that dimension
############################################
# Functions
##########################################
# display stats on helpful vectors
def helpful_vectors(BB, modulus):
nothelpful = 0
for ii in range(BB.dimensions()[0]):
if BB[ii,ii] >= modulus:
nothelpful += 1
print(nothelpful, "/", BB.dimensions()[0], " vectors are not helpful")
# display matrix picture with 0 and X
def matrix_overview(BB, bound):
for ii in range(BB.dimensions()[0]):
a = ('%02d ' % ii)
for jj in range(BB.dimensions()[1]):
a += '0' if BB[ii,jj] == 0 else 'X'
if BB.dimensions()[0] < 60:
a += ' '
if BB[ii, ii] >= bound:
a += '~'
print(a)
# tries to remove unhelpful vectors
# we start at current = n-1 (last vector)
def remove_unhelpful(BB, monomials, bound, current):
# end of our recursive function
if current == -1 or BB.dimensions()[0] <= dimension_min:
return BB
# we start by checking from the end
for ii in range(current, -1, -1):
# if it is unhelpful:
if BB[ii, ii] >= bound:
affected_vectors = 0
affected_vector_index = 0
# let's check if it affects other vectors
for jj in range(ii + 1, BB.dimensions()[0]):
# if another vector is affected:
# we increase the count
if BB[jj, ii] != 0:
affected_vectors += 1
affected_vector_index = jj
# level:0
# if no other vectors end up affected
# we remove it
if affected_vectors == 0:
print("* removing unhelpful vector", ii)
BB = BB.delete_columns([ii])
BB = BB.delete_rows([ii])
monomials.pop(ii)
BB = remove_unhelpful(BB, monomials, bound, ii-1)
return BB
# level:1
# if just one was affected we check
# if it is affecting someone else
elif affected_vectors == 1:
affected_deeper = True
for kk in range(affected_vector_index + 1, BB.dimensions()[0]):
# if it is affecting even one vector
# we give up on this one
if BB[kk, affected_vector_index] != 0:
affected_deeper = False
# remove both it if no other vector was affected and
# this helpful vector is not helpful enough
# compared to our unhelpful one
if affected_deeper and abs(bound - BB[affected_vector_index, affected_vector_index]) < abs(bound - BB[ii, ii]):
print("* removing unhelpful vectors", ii, "and", affected_vector_index)
BB = BB.delete_columns([affected_vector_index, ii])
BB = BB.delete_rows([affected_vector_index, ii])
monomials.pop(affected_vector_index)
monomials.pop(ii)
BB = remove_unhelpful(BB, monomials, bound, ii-1)
return BB
# nothing happened
return BB
"""
Returns:
* 0,0 if it fails
* -1,-1 if `strict=true`, and determinant doesn't bound
* x0,y0 the solutions of `pol`
"""
def boneh_durfee(pol, modulus, mm, tt, XX, YY):
"""
Boneh and Durfee revisited by Herrmann and May
finds a solution if:
* d < N^delta
* |x| < e^delta
* |y| < e^0.5
whenever delta < 1 - sqrt(2)/2 ~ 0.292
"""
# substitution (Herrman and May)
PR.<u, x, y> = PolynomialRing(ZZ)
Q = PR.quotient(x*y + 1 - u) # u = xy + 1
polZ = Q(pol).lift()
UU = XX*YY + 1
# x-shifts
gg = []
for kk in range(mm + 1):
for ii in range(mm - kk + 1):
xshift = x^ii * modulus^(mm - kk) * polZ(u, x, y)^kk
gg.append(xshift)
gg.sort()
# x-shifts list of monomials
monomials = []
for polynomial in gg:
for monomial in polynomial.monomials():
if monomial not in monomials:
monomials.append(monomial)
monomials.sort()
# y-shifts (selected by Herrman and May)
for jj in range(1, tt + 1):
for kk in range(floor(mm/tt) * jj, mm + 1):
yshift = y^jj * polZ(u, x, y)^kk * modulus^(mm - kk)
yshift = Q(yshift).lift()
gg.append(yshift) # substitution
# y-shifts list of monomials
for jj in range(1, tt + 1):
for kk in range(floor(mm/tt) * jj, mm + 1):
monomials.append(u^kk * y^jj)
# construct lattice B
nn = len(monomials)
BB = Matrix(ZZ, nn)
for ii in range(nn):
BB[ii, 0] = gg[ii](0, 0, 0)
for jj in range(1, ii + 1):
if monomials[jj] in gg[ii].monomials():
BB[ii, jj] = gg[ii].monomial_coefficient(monomials[jj]) * monomials[jj](UU,XX,YY)
# Prototype to reduce the lattice
if helpful_only:
# automatically remove
BB = remove_unhelpful(BB, monomials, modulus^mm, nn-1)
# reset dimension
nn = BB.dimensions()[0]
if nn == 0:
print("failure")
return 0,0
# check if vectors are helpful
if debug:
helpful_vectors(BB, modulus^mm)
# check if determinant is correctly bounded
det = BB.det()
bound = modulus^(mm*nn)
if det >= bound:
print("We do not have det < bound. Solutions might not be found.")
print("Try with highers m and t.")
if debug:
diff = (log(det) - log(bound)) / log(2)
print("size det(L) - size e^(m*n) = ", floor(diff))
if strict:
return -1, -1
else:
print("det(L) < e^(m*n) (good! If a solution exists < N^delta, it will be found)")
# display the lattice basis
if debug:
matrix_overview(BB, modulus^mm)
# LLL
if debug:
print("optimizing basis of the lattice via LLL, this can take a long time")
BB = BB.LLL()
if debug:
print("LLL is done!")
# transform vector i & j -> polynomials 1 & 2
if debug:
print("looking for independent vectors in the lattice")
found_polynomials = False
for pol1_idx in range(nn - 1):
for pol2_idx in range(pol1_idx + 1, nn):
# for i and j, create the two polynomials
PR.<w,z> = PolynomialRing(ZZ)
pol1 = pol2 = 0
for jj in range(nn):
pol1 += monomials[jj](w*z+1,w,z) * BB[pol1_idx, jj] / monomials[jj](UU,XX,YY)
pol2 += monomials[jj](w*z+1,w,z) * BB[pol2_idx, jj] / monomials[jj](UU,XX,YY)
# resultant
PR.<q> = PolynomialRing(ZZ)
rr = pol1.resultant(pol2)
# are these good polynomials?
if rr.is_zero() or rr.monomials() == [1]:
continue
else:
print("found them, using vectors", pol1_idx, "and", pol2_idx)
found_polynomials = True
break
if found_polynomials:
break
if not found_polynomials:
print("no independant vectors could be found. This should very rarely happen...")
return 0, 0
rr = rr(q, q)
# solutions
soly = rr.roots()
if len(soly) == 0:
print("Your prediction (delta) is too small")
return 0, 0
soly = soly[0][0]
ss = pol1(q, soly)
solx = ss.roots()[0][0]
#
return solx, soly
def example():
############################################
# How To Use This Script
##########################################
#
# The problem to solve (edit the following values)
#
# the modulus
N = 0xc2fd2913bae61f845ac94e4ee1bb10d8531dda830d31bb221dac5f179a8f883f15046d7aa179aff848db2734b8f88cc73d09f35c445c74ee35b01a96eb7b0a6ad9cb9ccd6c02c3f8c55ecabb55501bb2c318a38cac2db69d510e152756054aaed064ac2a454e46d9b3b755b67b46906fbff8dd9aeca6755909333f5f81bf74db
# the public exponent
e = 0x19441f679c9609f2484eb9b2658d7138252b847b2ed8ad182be7976ed57a3e441af14897ce041f3e07916445b88181c22f510150584eee4b0f776a5a487a4472a99f2ddc95efdd2b380ab4480533808b8c92e63ace57fb42bac8315fa487d03bec86d854314bc2ec4f99b192bb98710be151599d60f224114f6b33f47e357517
# the hypothesis on the private exponent (the theoretical maximum is 0.292)
delta = .18 # this means that d < N^delta
#
# Lattice (tweak those values)
#
# you should tweak this (after a first run), (e.g. increment it until a solution is found)
m = 4 # size of the lattice (bigger the better/slower)
# you need to be a lattice master to tweak these
t = int((1-2*delta) * m) # optimization from Herrmann and May
X = 2*floor(N^delta) # this _might_ be too much
Y = floor(N^(1/2)) # correct if p, q are ~ same size
#
# Don't touch anything below
#
# Problem put in equation
P.<x,y> = PolynomialRing(ZZ)
A = int((N+1)/2)
pol = 1 + x * (A + y)
#
# Find the solutions!
#
# Checking bounds
if debug:
print("=== checking values ===")
print("* delta:", delta)
print("* delta < 0.292", delta < 0.292)
print("* size of e:", int(log(e)/log(2)))
print("* size of N:", int(log(N)/log(2)))
print("* m:", m, ", t:", t)
# boneh_durfee
if debug:
print("=== running algorithm ===")
start_time = time.time()
solx, soly = boneh_durfee(pol, e, m, t, X, Y)
# found a solution?
if solx > 0:
print("=== solution found ===")
if False:
print("x:", solx)
print("y:", soly)
d = int(pol(solx, soly) / e)
print("private key found:", d)
else:
print("=== no solution was found ===")
if debug:
print(("=== %s seconds ===" % (time.time() - start_time)))
if __name__ == "__main__":
example()