前言
25年开始以来打的最高质量的密码了?
题目
Division
抢了个血,怪不好意思的 task.py
import randomprint('----Welcome to my division calc----')print('''menu: [1] Division calc [2] Get flag''')while True: choose = input(': >>> ') if choose == '1': try: denominator = int(input('input the denominator: >>> ')) except: print('INPUT NUMBERS') continue nominator = random.getrandbits(32) if denominator == '0': print('NO YOU DONT') continue else: print(f'{nominator}//{denominator} = {nominator//denominator}') elif choose == '2': try: ans = input('input the answer: >>> ') rand1 = random.getrandbits(11000) rand2 = random.getrandbits(10000) correct_ans = rand1 // rand2 if correct_ans == int(ans): print('WOW') with open('flag', 'r') as f: print(f'Here is your flag: {f.read()}') else: print(f'NOPE, the correct answer is {correct_ans}') except: print('INPUT NUMBERS') else: print('Invalid choice')
第一时间拿到就知道是mt19937
预测随机数,1里面输入1就可以拿到每次的随机数从而预测了
exp.py
from pwn import remoteimport randomfrom randcrack import RandCrackrc = RandCrack()conn = remote('8.147.132.32', 22975)outputs = []
for _ in range(624): conn.recvuntil(b': >>> ') conn.sendline(b'1') conn.recvuntil(b'input the denominator: >>> ') conn.sendline(b'1') line = conn.recvline().decode().strip() nominator = int(line.split('=')[1].strip()) outputs.append(nominator) rc.submit(nominator)
rand1 =rc.predict_getrandbits(11000)rand2 = rc.predict_getrandbits(10000)correct_ans = rand1 // rand2print(correct_ans)conn.recvuntil(b': >>> ')conn.sendline(b'2')conn.recvuntil(b'input the answer: >>>')conn.sendline(correct_ans)conn.interactive()
Complex_signin
task.py
from Crypto.Util.number import *from Crypto.Cipher import ChaCha20import hashlibfrom secret import flag
class Complex: def __init__(self, re, im): self.re = re self.im = im
def __mul__(self, c): re_ = self.re * c.re - self.im * c.im im_ = self.re * c.im + self.im * c.re return Complex(re_, im_)
def __eq__(self, c): return self.re == c.re and self.im == c.im
def __rshift__(self, m): return Complex(self.re >> m, self.im >> m)
def __lshift__(self, m): return Complex(self.re << m, self.im << m)
def __str__(self): if self.im == 0: return str(self.re) elif self.re == 0: if abs(self.im) == 1: return f"{'-' if self.im < 0 else ''}i" else: return f"{self.im}i" else: return f"{self.re} {'+' if self.im > 0 else '-'} {abs(self.im)}i"
def tolist(self): return [self.re, self.im]
def complex_pow(c, exp, n): result = Complex(1, 0) while exp > 0: if exp & 1: result = result * c result.re = result.re % n result.im = result.im % n c = c * c c.re = c.re % n c.im = c.im % n exp >>= 1 return result
bits = 128p = getPrime(1024)q = getPrime(1024)n = p * qm = Complex(getRandomRange(1, n), getRandomRange(1, n))e = 3c = complex_pow(m, e, n)print(f"n = {n}")print(f"mh = {(m >> bits << bits).tolist()}")print(f"C = {c.tolist()}")print(f"enc = {ChaCha20.new(key=hashlib.sha256(str(m.re + m.im).encode()).digest(), nonce=b'Pr3d1ctmyxjj').encrypt(flag)}")
首先是个复数域的东西,然后是个m高位攻击,注意到复数域和实数域处理方法不一样 {{< raw >}}
{{</ raw >}}
那么我们现在有了实部和虚部,m>>bits
时可以类比于实部虚部一块位移,但是还是不能分开高位计算,那么就有了两个未知,bits
是满足copper的,直接打就行了
exp.py
import itertoolsfrom Crypto.Util.number import *from tqdm import *def small_roots(f, bounds, m=1, d=None): if not d: d = f.degree() R = f.base_ring() N = R.cardinality() f /= f.coefficients().pop(0) f = f.change_ring(ZZ) G = Sequence([], f.parent()) for i in range(m + 1): base = N ^ (m - i) * f ^ i for shifts in itertools.product(range(d), repeat=f.nvariables()): g = base * prod(map(power, f.variables(), shifts)) G.append(g) B, monomials = G.coefficient_matrix() monomials = vector(monomials) factors = [monomial(*bounds) for monomial in monomials] for i, factor in enumerate(factors): B.rescale_col(i, factor) B = B.dense_matrix().LLL() B = B.change_ring(QQ) for i, factor in enumerate(factors): B.rescale_col(i, 1 / factor) H = Sequence([], f.parent().change_ring(QQ)) for h in filter(None, B * monomials): H.append(h) I = H.ideal() if I.dimension() == -1: H.pop() elif I.dimension() == 0: roots = [] for root in I.variety(ring=ZZ): root = tuple(R(root[var]) for var in f.variables()) roots.append(root) return roots return []
k = 128n =mh =C =
# 定义多项式环PR.<x, y> = PolynomialRing(Zmod(n))
a = mh[0] + x # x 是 alb = mh[1] + y # y 是 blf1 = (a^3 - 3*a*b^2) - C[0] # 实部方程f2 = (3*a^2*b - b^3) - C[1] # 虚部方程res = small_roots(f1,bounds=(2^128,2^128),m=1,d=3)if res != []: print(res)
x=y=
from Crypto.Cipher import ChaCha20import hashlib
n =mh =enc =x=y=s = (mh[0]) + (mh[1])+x+ykey = hashlib.sha256(str(s).encode()).digest()cipher = ChaCha20.new(key=key, nonce=b'Pr3d1ctmyxjj')flag = cipher.decrypt(enc)print(flag)
勒索病毒
拉下来发现是个exe,猜是python打包出来的,扔到https://pyinstxtractor-web.netlify.app 拆开,拿到pyc和pub.key和enc,然后pyc转py https://www.lddgo.net/string/pyc-compile-decompile
task.py
# Visit https://www.lddgo.net/string/pyc-compile-decompile for more information# Version : Python 3.8
'''Created on Sun Mar 30 18:25:08 2025
@author: Crypto0
import reimport base64import osimport sysfrom gmssl import sm4from Crypto.Util.Padding import padimport binasciifrom random import shuffle, randrange
N = 49p = 3q = 128d = 3assert q > (6 * d + 1) * pR.<x> = ZZ[]def generate_T(d1, d2): assert N >= d1 + d2 s = [1] * d1 + [-1] * d2 + [0] * (N - d1 - d2) shuffle(s) return R(s)
def invert_mod_prime(f, p): Rp = R.change_ring(Integers(p)).quotient(x^N - 1) return R(lift(1 / Rp(f)))
def convolution(f, g): return (f * g) % (x^N - 1)
def lift_mod(f, q): return R([((f[i] + q // 2) % q) - q // 2 for i in range(N)])
def poly_mod(f, q): return R([f[i] % q for i in range(N)])
def invert_mod_pow2(f, q): assert q.is_power_of(2) g = invert_mod_prime(f, 2) while True: r = lift_mod(convolution(g, f), q) if r == 1: return g g = lift_mod(convolution(g, 2 - r), q)
def generate_message(): return R([randrange(p) - 1 for _ in range(N)])
def generate_key(): while True: try: f = generate_T(d + 1, d) g = generate_T(d, d) Fp = poly_mod(invert_mod_prime(f, p), p) Fq = poly_mod(invert_mod_pow2(f, q), q) break except: continue h = poly_mod(convolution(Fq, g), q) return h, (f, g)
def encrypt_message(m, h): e = lift_mod(p * convolution(h, generate_T(d, d)) + m, q) return e
def save_ntru_keys(): h, secret = generate_key() with open("pub_key.txt", "w") as f: f.write(str(h)) m = generate_message() with open("priv_key.txt", "w") as f: f.write(str(m)) e = encrypt_message(m, h) with open("enc.txt", "w") as f: f.write(str(e))
def terms(poly_str): terms = [] pattern = r\'([+-]?\\s*x\\^?\\d*|[-+]?\\s*\\d+)\' matches = re.finditer(pattern, poly_str.replace(\' \', \'\'))
for match in matches: term = match.group() if term == \'+x\' or term == \'x\': terms.append(1) elif term == \'-x\': terms.append(-1) elif \'x^\' in term: coeff_part = term.split(\'x^\')[0] exponent = int(term.split(\'x^\')[1]) if not coeff_part or coeff_part == \'+\': coeff = 1 elif coeff_part == \'-\': coeff = -1 else: coeff = int(coeff_part) terms.append(coeff * exponent) elif \'x\' in term: coeff_part = term.split(\'x\')[0] if not coeff_part or coeff_part == \'+\': terms.append(1) elif coeff_part == \'-\': terms.append(-1) else: terms.append(int(coeff_part)) else: if term == \'+1\' or term == \'1\': terms.append(0) terms.append(-0) return terms
def gen_key(poly_terms): binary = [0] * 128 for term in poly_terms: exponent = abs(term) if term > 0 and exponent <= 127: binary[127 - exponent] = 1 binary_str = \'\'.join(map(str, binary)) hex_key = hex(int(binary_str, 2))[2:].upper().zfill(32) return hex_key
def read_polynomial_from_file(filename): with open(filename, \'r\') as file: return file.read().strip()
def sm4_encrypt(key, plaintext): assert len(key) == 16, "SM4 key must be 16 bytes" cipher = sm4.CryptSM4() cipher.set_key(key, sm4.SM4_ENCRYPT) padded_plaintext = pad(plaintext, 16) return cipher.crypt_ecb(padded_plaintext)
def sm4_encrypt_file(input_path, output_path, key): with open(input_path, \'rb\') as f: plaintext = f.read()
ciphertext = sm4_encrypt(key, plaintext)
with open(output_path, \'wb\') as f: f.write(ciphertext)
def resource_path(relative_path): if getattr(sys, \'frozen\', False): base_path = sys._MEIPASS else: base_path = os.path.abspath(".") return os.path.join(base_path, relative_path)
def encrypt_directory(directory, sm4_key, extensions=[".txt"]): if not os.path.exists(directory): print(f"Directory does not exist: {directory}") return
for root, _, files in os.walk(directory): for file in files: if any(file.endswith(ext) for ext in extensions): input_path = os.path.join(root, file) output_path = input_path + ".enc"
try: sm4_encrypt_file(input_path, output_path, sm4_key) os.remove(input_path) print(f"Encrypted: {input_path} -> {output_path}") except Exception as e: print(f"Error encrypting {input_path}: {str(e)}")
def main(): try: save_ntru_keys() poly_str = read_polynomial_from_file("priv_key.txt") poly_terms = terms(poly_str) sm4_key = binascii.unhexlify(poly_terms) user_name = os.getlogin() target_dir = os.path.join("C:\\Users", user_name, "Desktop", "test_files")
if not os.path.exists(target_dir): os.makedirs(target_dir, exist_ok=True) print(f"Created directory: {target_dir}") return
txt_files = [f for f in os.listdir(target_dir) if f.endswith(\'.txt\') and os.path.isfile(os.path.join(target_dir, f))]
if not txt_files: print("No .txt files found in directory") return
for txt_file in txt_files: file_path = os.path.join(target_dir, txt_file) try: with open(file_path, \'rb\') as f: test_data = f.read()
ciphertext = sm4_encrypt(sm4_key, test_data) encrypted_path = file_path + \'.enc\'
with open(encrypted_path, \'wb\') as f: f.write(ciphertext) except Exception as e: print(f"Error processing {txt_file}: {str(e)}")
except Exception as e: print(f"Fatal error: {str(e)}")
if __name__ == "__main__": main()'''
enc.txt
e =-x^48 - x^46 + x^45 + x^43 - x^42 + x^41 + x^40 + x^36 - x^35 + x^34 - x^33 + x^32 - x^30 + x^29 - x^28 - x^27 - x^26 - x^25 - x^23 - x^22 + x^21 + x^20 + x^19 + x^18 - x^17 - x^16 - x^15 - x^14 - x^12 + x^9 - x^7 - x^6 - x^5 - x^4 + x^3 - x + 1
pub_key.txt
h =
其中enc里面的第二个多项式就是m 非预期exp.py
import binasciiimport refrom Crypto.Util.number import *from gmssl import sm4
# 解析多项式字符串,提取每一项的次数 * 系数def terms(poly_str): terms = [] # 匹配 x^n、x、常数项等 pattern = r'([+-]?x\^?\d*|[-+]?\d+)' matches = re.finditer(pattern, poly_str.replace(' ', ''))
for match in matches: term = match.group() if term in ('+x', 'x'): terms.append(1) elif term == '-x': terms.append(-1) elif 'x^' in term: coeff_part, exponent = term.split('x^') exponent = int(exponent) if not coeff_part or coeff_part == '+': coeff = 1 elif coeff_part == '-': coeff = -1 else: coeff = int(coeff_part) terms.append(coeff * exponent) elif 'x' in term: coeff_part = term.split('x')[0] if not coeff_part or coeff_part == '+': terms.append(1) elif coeff_part == '-': terms.append(-1) else: terms.append(int(coeff_part)) else: # 常数项 x^0,不影响密钥,但加入 0 是为了保留结构 if term == '+1' or term == '1': terms.append(0) elif term == '-1': terms.append(-0) return terms
# 根据解析到的多项式项生成128位密钥(用于 SM4)def gen_key(poly_terms): binary = [0] * 128 for term in poly_terms: exponent = abs(term) if term > 0 and exponent <= 127: binary[127 - exponent] = 1 # 最高位是 x^127 binary_str = ''.join(map(str, binary)) hex_key = hex(int(binary_str, 2))[2:].upper().zfill(32) return binascii.unhexlify(hex_key)
# SM4 解密函数(ECB 模式)def sm4_decrypt(key, ciphertext): assert len(key) == 16, "SM4 key must be 16 bytes" cipher = sm4.CryptSM4() cipher.set_key(key, sm4.SM4_DECRYPT) return cipher.crypt_ecb(ciphertext)
# 多项式字符串poly_str = "-x^48-x^46+x^45+x^43-x^42+x^41+x^40+x^36-x^35+x^34-x^33+x^32-" \ "x^30+x^29-x^28-x^27-x^26-x^25-x^23-x^22+x^21+x^20+x^19+x^18-" \ "x^17-x^16-x^15-x^14-x^12+x^9-x^7-x^6-x^5-x^4+x^3-x+1"
# 提取多项式项poly_terms = terms(poly_str)print("多项式项:", poly_terms)
# 生成 SM4 密钥(16 字节)sm4_key = gen_key(poly_terms)print("SM4 密钥(hex):", sm4_key.hex())
# 密文(注意这里原代码是 hex 字符串,需要转换成字节)ciphertext_hex = ( "bf0cb5cc6bea6146e9c1f109df953a57" "daa416d38a8ffba6438e7e599613e01f" "3b9a53dace4ccd55cd3e55ef88e0b835")ciphertext = binascii.unhexlify(ciphertext_hex)
# 解密plaintext = sm4_decrypt(sm4_key, ciphertext)print("解密结果:", plaintext)
预期的话应该是求出m这个私钥,可以参考https://0xffff.one/d/1424/2
reed
task.py
import stringimport randomfrom secret import flag
assert flag.startswith('XYCTF{') and flag.endswith('}')flag = flag.rstrip('}').lstrip('XYCTF{')
table = string.ascii_letters + string.digitsassert all(i in table for i in flag)r = random.Random()
class PRNG: def __init__(self, seed): self.a = 1145140 self.b = 19198100 random.seed(seed)
def next(self): x = random.randint(self.a, self.b) random.seed(x ** 2 + 1) return x
def round(self, k): for _ in range(k): x = self.next() return x
def encrypt(msg, a, b): c = [(a * table.index(m) + b) % 19198111 for m in msg] return c
seed = int(input('give me seed: '))prng = PRNG(seed)a = prng.round(r.randrange(2**16))b = prng.round(r.randrange(2**16))enc = encrypt(flag, a, b)print(enc)
输入seed
,然后产生,赛后听别的师傅说类似lcg,可以通过循环去限定ab的范围,然后排列组合爆破出来,啧,没想到,但是直接暴力匹配也可以
exp.py
from string import ascii_letters, digitsimport refrom typing import List, Tuple, Optional, Set
CHAR_TABLE = ascii_letters + digitsMOD = 19198111ENC = []def extended_gcd(a: int, b: int) -> Tuple[int, int, int]: if a == 0: return b, 0, 1 g, y, x = extended_gcd(b % a, a) return g, x - (b // a) * y, y
def modinv(a: int, m: int) -> Optional[int]: g, x, _ = extended_gcd(a, m) return x % m if g == 1 else None
def is_readable(s: str) -> bool: return bool(re.search(r'[A-Za-z]{2,}', s)) and len(re.findall(r'\d', s)) < 5
def solve() -> List[str]: candidates: Set[str] = set() processed: Set[Tuple[int, int]] = set() table_len = len(CHAR_TABLE)
cipher_pairs = [(i, j) for i in range(len(ENC)) for j in range(i + 1, len(ENC)) if ENC[i] != ENC[j]]
for idx1, idx2 in cipher_pairs: c1, c2 = ENC[idx1], ENC[idx2] delta_c = (c1 - c2) % MOD
for i1 in range(table_len): p1 = ord(CHAR_TABLE[i1]) for i2 in range(table_len): if i1 == i2: continue p2 = ord(CHAR_TABLE[i2])
delta_i = (p1 - p2) % MOD inv = modinv(delta_i, MOD) if inv is None: continue
a = (delta_c * inv) % MOD b = (c1 - a * p1) % MOD
if (a, b) in processed: continue processed.add((a, b))
a_inv = modinv(a, MOD) if a_inv is None: continue
flag = [] for c in ENC: i = ((c - b) * a_inv) % MOD if not (0 <= i < table_len): break flag.append(CHAR_TABLE[i]) else: candidate = ''.join(flag) if len(candidate) == len(ENC): candidates.add(candidate)
readable = [f for f in candidates if is_readable(f)] return readable if readable else list(candidates)
def main(): results = solve() print(f"Found {len(results)} possible candidates:") for idx, flag in enumerate(results, 1): print(f"#{idx}: XYCTF{{{flag}}}")
if __name__ == "__main__": main()
复复复数
task.py
class ComComplex: def __init__(self, value=[0,0,0,0]): self.value = value def __str__(self): s = str(self.value[0]) for k,i in enumerate(self.value[1:]): if i >= 0: s += '+' s += str(i) +'ijk'[k] return s def __add__(self,x): return ComComplex([i+j for i,j in zip(self.value,x.value)]) def __mul__(self,x): a = self.value[0]*x.value[0]-self.value[1]*x.value[1]-self.value[2]*x.value[2]-self.value[3]*x.value[3] b = self.value[0]*x.value[1]+self.value[1]*x.value[0]+self.value[2]*x.value[3]-self.value[3]*x.value[2] c = self.value[0]*x.value[2]-self.value[1]*x.value[3]+self.value[2]*x.value[0]+self.value[3]*x.value[1] d = self.value[0]*x.value[3]+self.value[1]*x.value[2]-self.value[2]*x.value[1]+self.value[3]*x.value[0] return ComComplex([a,b,c,d]) def __mod__(self,x): return ComComplex([i % x for i in self.value]) def __pow__(self, x, n=None): tmp = ComComplex(self.value) a = ComComplex([1,0,0,0]) while x: if x & 1: a *= tmp tmp *= tmp if n: a %= n tmp %= n x >>= 1 return a
from Crypto.Util.number import *from secret import flag, hint
p = getPrime(256)q = getPrime(256)r = getPrime(256)n = p * q * r
P = getPrime(512)assert len(hint) == 20hints = ComComplex([bytes_to_long(hint[i:i+5]) for i in range(0,20,5)])keys = ComComplex([0, p, q, r])print('hint =',hints)print('gift =',hints*keys%P)print('P =',P)
e = 65547m = ComComplex([bytes_to_long(flag[i:i+len(flag)//4+1]) for i in range(0,len(flag),len(flag)//4+1)])c = pow(m, e, n)print('n =', n)print('c =', c)
感觉ai比我懂,优先去回复pqr,代码定义的乘法就是四元数,那么就是三个线性同余式,可以直接去求解,让ai帮我搓个代码
from Crypto.Util.number import long_to_bytes, inverse, bytes_to_long, getPrimefrom sympy import Matriximport sys
class ComComplex: def __init__(self, value=[0,0,0,0]): self.value = value def __str__(self): s = str(self.value[0]) for k,i in enumerate(self.value[1:]): if i >= 0: s += '+' s += str(i) +'ijk'[k] return s def __add__(self,x): return ComComplex([i+j for i,j in zip(self.value,x.value)]) def __mul__(self,x): a = self.value[0]*x.value[0]-self.value[1]*x.value[1]-self.value[2]*x.value[2]-self.value[3]*x.value[3] b = self.value[0]*x.value[1]+self.value[1]*x.value[0]+self.value[2]*x.value[3]-self.value[3]*x.value[2] c = self.value[0]*x.value[2]-self.value[1]*x.value[3]+self.value[2]*x.value[0]+self.value[3]*x.value[1] d = self.value[0]*x.value[3]+self.value[1]*x.value[2]-self.value[2]*x.value[1]+self.value[3]*x.value[0] return ComComplex([a,b,c,d]) def __mod__(self,x): return ComComplex([i % x for i in self.value]) def __pow__(self, x, n=None): tmp = ComComplex(self.value) a = ComComplex([1,0,0,0]) while x: if x & 1: a *= tmp tmp *= tmp if n: a %= n tmp %= n x >>= 1 return a
A = 375413371936B = 452903063925C = 418564633198D = 452841062207
G0 =G1 =G2 =G3 =
P =
M = Matrix([ [-B, -C, -D], [ A, -D, C], [ D, A, -B]])v = Matrix([G0 % P, G1 % P, G2 % P])
M_inv = M.inv_mod(P)solution = M_inv * vp1 = int(solution[0] % P)q1 = int(solution[1] % P)r1 = int(solution[2] % P)print(p1,q1,r1)
然后本来想直接求flag,发现e=65547
,不互素,拿个以前的crt直接用就行了
from Crypto.Util.number import *from Crypto.Util.number import GCD as gcdclass ComComplex: def __init__(self, value=[0,0,0,0]): self.value = value def __str__(self): s = str(self.value[0]) for k,i in enumerate(self.value[1:]): if i >= 0: s += '+' s += str(i) +'ijk'[k] return s def __add__(self,x): return ComComplex([i+j for i,j in zip(self.value,x.value)]) def __mul__(self,x): a = self.value[0]*x.value[0]-self.value[1]*x.value[1]-self.value[2]*x.value[2]-self.value[3]*x.value[3] b = self.value[0]*x.value[1]+self.value[1]*x.value[0]+self.value[2]*x.value[3]-self.value[3]*x.value[2] c = self.value[0]*x.value[2]-self.value[1]*x.value[3]+self.value[2]*x.value[0]+self.value[3]*x.value[1] d = self.value[0]*x.value[3]+self.value[1]*x.value[2]-self.value[2]*x.value[1]+self.value[3]*x.value[0] return ComComplex([a,b,c,d]) def __mod__(self,x): return ComComplex([i % x for i in self.value]) def __pow__(self, x, n=None): tmp = ComComplex(self.value) a = ComComplex([1,0,0,0]) while x: if x & 1: a *= tmp tmp *= tmp if n: a %= n tmp %= n x >>= 1 return ap,q,r=e=65547c = ComComplex([])pqr=[p,q,r]def inver_d(s): phi_s = s *(s-1)*(s**2-1) g_s = gcd(e, phi_s) phi_prime = phi_s // g_s d = inverse(e, phi_prime) return d
dp=inver_d(p)dq=inver_d(q)dr=inver_d(r)
cp=c%pcq=c%qcr=c%r
mp=pow(cp,dp,p)mq=pow(cq,dq,q)mr=pow(cr,dr,r)def crt(shares): """中国剩余定理合并多个四元复数的每一分量""" res = [] for i in range(4): # 提取每个四元数第 i 位的值 和对应模数 a = [s.value[i] for s, _ in shares] m = [mod for _, mod in shares] # 使用单分量CRT合并结果 res.append(int(crt_r(a, m))) return ComComplex(res)
def crt_r(a, m): """中国剩余定理求解单个整数分量""" M = 1 for mi in m: M *= mi # 总模数 M 是所有模数的乘积
res = 0 for ai, mi in zip(a, m): Mi = M // mi inv = inverse(Mi, mi) res = (res + ai * Mi * inv) % M
return res
m=crt([(mp,p),(mq,q),(mr,r)])
mm = []for ms in m.value: mm.append(long_to_bytes(ms))
flag = b''.join(mm)print(flag.decode())
choice
choice.py
from Crypto.Util.number import bytes_to_longfrom random import Randomfrom secret import flag
assert flag.startswith(b'XYCTF{') and flag.endswith(b'}')flag = flag[6:-1]
msg = bytes_to_long(flag)rand = Random()test = bytes([i for i in range(255, -1, -1)])open('output.py', 'w').write(f'enc = {msg ^ rand.getrandbits(msg.bit_length())}\nr = {[rand.choice(test) for _ in range(2496)]}')
random.py
"""Random variable generators.
bytes ----- uniform bytes (values between 0 and 255)
integers -------- uniform within range
sequences --------- pick random element pick random sample pick weighted random sample generate random permutation
distributions on the real line: ------------------------------ uniform triangular normal (Gaussian) lognormal negative exponential gamma beta pareto Weibull
distributions on the circle (angles 0 to 2pi) --------------------------------------------- circular uniform von Mises
discrete distributions ---------------------- binomial
General notes on the underlying Mersenne Twister core generator:
* The period is 2**19937-1.* It is one of the most extensively tested generators in existence.* The random() method is implemented in C, executes in a single Python step, and is, therefore, threadsafe.
"""
# Translated by Guido van Rossum from C source provided by# Adrian Baddeley. Adapted by Raymond Hettinger for use with# the Mersenne Twister and os.urandom() core generators.
from warnings import warn as _warnfrom math import log as _log, exp as _exp, pi as _pi, e as _e, ceil as _ceilfrom math import sqrt as _sqrt, acos as _acos, cos as _cos, sin as _sinfrom math import tau as TWOPI, floor as _floor, isfinite as _isfinitefrom math import lgamma as _lgamma, fabs as _fabs, log2 as _log2from os import urandom as _urandomfrom _collections_abc import Sequence as _Sequencefrom operator import index as _indexfrom itertools import accumulate as _accumulate, repeat as _repeatfrom bisect import bisect as _bisectimport os as _osimport _random
try: # hashlib is pretty heavy to load, try lean internal module first from _sha2 import sha512 as _sha512except ImportError: # fallback to official implementation from hashlib import sha512 as _sha512
__all__ = [ "Random", "SystemRandom", "betavariate", "binomialvariate", "choice", "choices", "expovariate", "gammavariate", "gauss", "getrandbits", "getstate", "lognormvariate", "normalvariate", "paretovariate", "randbytes", "randint", "random", "randrange", "sample", "seed", "setstate", "shuffle", "triangular", "uniform", "vonmisesvariate", "weibullvariate",]
NV_MAGICCONST = 4 * _exp(-0.5) / _sqrt(2.0)LOG4 = _log(4.0)SG_MAGICCONST = 1.0 + _log(4.5)BPF = 53 # Number of bits in a floatRECIP_BPF = 2 ** -BPF_ONE = 1
class Random(_random.Random): """Random number generator base class used by bound module functions.
Used to instantiate instances of Random to get generators that don't share state.
Class Random can also be subclassed if you want to use a different basic generator of your own devising: in that case, override the following methods: random(), seed(), getstate(), and setstate(). Optionally, implement a getrandbits() method so that randrange() can cover arbitrarily large ranges.
"""
VERSION = 3 # used by getstate/setstate
def __init__(self, x=None): """Initialize an instance.
Optional argument x controls seeding, as for Random.seed(). """
self.seed(x) self.gauss_next = None
def seed(self, a=None, version=2): """Initialize internal state from a seed.
The only supported seed types are None, int, float, str, bytes, and bytearray.
None or no argument seeds from current time or from an operating system specific randomness source if available.
If *a* is an int, all bits are used.
For version 2 (the default), all of the bits are used if *a* is a str, bytes, or bytearray. For version 1 (provided for reproducing random sequences from older versions of Python), the algorithm for str and bytes generates a narrower range of seeds.
"""
if version == 1 and isinstance(a, (str, bytes)): a = a.decode('latin-1') if isinstance(a, bytes) else a x = ord(a[0]) << 7 if a else 0 for c in map(ord, a): x = ((1000003 * x) ^ c) & 0xFFFFFFFFFFFFFFFF x ^= len(a) a = -2 if x == -1 else x
elif version == 2 and isinstance(a, (str, bytes, bytearray)): if isinstance(a, str): a = a.encode() a = int.from_bytes(a + _sha512(a).digest())
elif not isinstance(a, (type(None), int, float, str, bytes, bytearray)): raise TypeError('The only supported seed types are: None,\n' 'int, float, str, bytes, and bytearray.')
super().seed(a) self.gauss_next = None
def getstate(self): """Return internal state; can be passed to setstate() later.""" return self.VERSION, super().getstate(), self.gauss_next
def setstate(self, state): """Restore internal state from object returned by getstate().""" version = state[0] if version == 3: version, internalstate, self.gauss_next = state super().setstate(internalstate) elif version == 2: version, internalstate, self.gauss_next = state # In version 2, the state was saved as signed ints, which causes # inconsistencies between 32/64-bit systems. The state is # really unsigned 32-bit ints, so we convert negative ints from # version 2 to positive longs for version 3. try: internalstate = tuple(x % (2 ** 32) for x in internalstate) except ValueError as e: raise TypeError from e super().setstate(internalstate) else: raise ValueError("state with version %s passed to " "Random.setstate() of version %s" % (version, self.VERSION))
## ------------------------------------------------------- ## ---- Methods below this point do not need to be overridden or extended ## ---- when subclassing for the purpose of using a different core generator.
## -------------------- pickle support -------------------
# Issue 17489: Since __reduce__ was defined to fix #759889 this is no # longer called; we leave it here because it has been here since random was # rewritten back in 2001 and why risk breaking something. def __getstate__(self): # for pickle return self.getstate()
def __setstate__(self, state): # for pickle self.setstate(state)
def __reduce__(self): return self.__class__, (), self.getstate()
## ---- internal support method for evenly distributed integers ----
def __init_subclass__(cls, /, **kwargs): """Control how subclasses generate random integers.
The algorithm a subclass can use depends on the random() and/or getrandbits() implementation available to it and determines whether it can generate random integers from arbitrarily large ranges. """
for c in cls.__mro__: if '_randbelow' in c.__dict__: # just inherit it break if 'getrandbits' in c.__dict__: cls._randbelow = cls._randbelow_with_getrandbits break if 'random' in c.__dict__: cls._randbelow = cls._randbelow_without_getrandbits break
def _randbelow_with_getrandbits(self, n): "Return a random int in the range [0,n). Defined for n > 0."
getrandbits = self.getrandbits k = n.bit_length() - 1 r = getrandbits(k) # 0 <= r < 2**k while r >= n: r = getrandbits(k) return r
def _randbelow_without_getrandbits(self, n, maxsize=1<<BPF): """Return a random int in the range [0,n). Defined for n > 0.
The implementation does not use getrandbits, but only random. """
random = self.random if n >= maxsize: _warn("Underlying random() generator does not supply \n" "enough bits to choose from a population range this large.\n" "To remove the range limitation, add a getrandbits() method.") return _floor(random() * n) rem = maxsize % n limit = (maxsize - rem) / maxsize # int(limit * maxsize) % n == 0 r = random() while r >= limit: r = random() return _floor(r * maxsize) % n
_randbelow = _randbelow_with_getrandbits
## -------------------------------------------------------- ## ---- Methods below this point generate custom distributions ## ---- based on the methods defined above. They do not ## ---- directly touch the underlying generator and only ## ---- access randomness through the methods: random(), ## ---- getrandbits(), or _randbelow().
## -------------------- bytes methods ---------------------
def randbytes(self, n): """Generate n random bytes.""" return self.getrandbits(n * 8).to_bytes(n, 'little')
## -------------------- integer methods -------------------
def randrange(self, start, stop=None, step=_ONE): """Choose a random item from range(stop) or range(start, stop[, step]).
Roughly equivalent to ``choice(range(start, stop, step))`` but supports arbitrarily large ranges and is optimized for common cases.
"""
# This code is a bit messy to make it fast for the # common case while still doing adequate error checking. istart = _index(start) if stop is None: # We don't check for "step != 1" because it hasn't been # type checked and converted to an integer yet. if step is not _ONE: raise TypeError("Missing a non-None stop argument") if istart > 0: return self._randbelow(istart) raise ValueError("empty range for randrange()")
# Stop argument supplied. istop = _index(stop) width = istop - istart istep = _index(step) # Fast path. if istep == 1: if width > 0: return istart + self._randbelow(width) raise ValueError(f"empty range in randrange({start}, {stop})")
# Non-unit step argument supplied. if istep > 0: n = (width + istep - 1) // istep elif istep < 0: n = (width + istep + 1) // istep else: raise ValueError("zero step for randrange()") if n <= 0: raise ValueError(f"empty range in randrange({start}, {stop}, {step})") return istart + istep * self._randbelow(n)
def randint(self, a, b): """Return random integer in range [a, b], including both end points. """
return self.randrange(a, b+1)
## -------------------- sequence methods -------------------
def choice(self, seq): """Choose a random element from a non-empty sequence."""
# As an accommodation for NumPy, we don't use "if not seq" # because bool(numpy.array()) raises a ValueError. if not len(seq): raise IndexError('Cannot choose from an empty sequence') return seq[self._randbelow(len(seq))]
def shuffle(self, x): """Shuffle list x in place, and return None."""
randbelow = self._randbelow for i in reversed(range(1, len(x))): # pick an element in x[:i+1] with which to exchange x[i] j = randbelow(i + 1) x[i], x[j] = x[j], x[i]
def sample(self, population, k, *, counts=None): """Chooses k unique random elements from a population sequence.
Returns a new list containing elements from the population while leaving the original population unchanged. The resulting list is in selection order so that all sub-slices will also be valid random samples. This allows raffle winners (the sample) to be partitioned into grand prize and second place winners (the subslices).
Members of the population need not be hashable or unique. If the population contains repeats, then each occurrence is a possible selection in the sample.
Repeated elements can be specified one at a time or with the optional counts parameter. For example:
sample(['red', 'blue'], counts=[4, 2], k=5)
is equivalent to:
sample(['red', 'red', 'red', 'red', 'blue', 'blue'], k=5)
To choose a sample from a range of integers, use range() for the population argument. This is especially fast and space efficient for sampling from a large population:
sample(range(10000000), 60)
"""
# Sampling without replacement entails tracking either potential # selections (the pool) in a list or previous selections in a set.
# When the number of selections is small compared to the # population, then tracking selections is efficient, requiring # only a small set and an occasional reselection. For # a larger number of selections, the pool tracking method is # preferred since the list takes less space than the # set and it doesn't suffer from frequent reselections.
# The number of calls to _randbelow() is kept at or near k, the # theoretical minimum. This is important because running time # is dominated by _randbelow() and because it extracts the # least entropy from the underlying random number generators.
# Memory requirements are kept to the smaller of a k-length # set or an n-length list.
# There are other sampling algorithms that do not require # auxiliary memory, but they were rejected because they made # too many calls to _randbelow(), making them slower and # causing them to eat more entropy than necessary.
if not isinstance(population, _Sequence): raise TypeError("Population must be a sequence. " "For dicts or sets, use sorted(d).") n = len(population) if counts is not None: cum_counts = list(_accumulate(counts)) if len(cum_counts) != n: raise ValueError('The number of counts does not match the population') total = cum_counts.pop() if not isinstance(total, int): raise TypeError('Counts must be integers') if total <= 0: raise ValueError('Total of counts must be greater than zero') selections = self.sample(range(total), k=k) bisect = _bisect return [population[bisect(cum_counts, s)] for s in selections] randbelow = self._randbelow if not 0 <= k <= n: raise ValueError("Sample larger than population or is negative") result = [None] * k setsize = 21 # size of a small set minus size of an empty list if k > 5: setsize += 4 ** _ceil(_log(k * 3, 4)) # table size for big sets if n <= setsize: # An n-length list is smaller than a k-length set. # Invariant: non-selected at pool[0 : n-i] pool = list(population) for i in range(k): j = randbelow(n - i) result[i] = pool[j] pool[j] = pool[n - i - 1] # move non-selected item into vacancy else: selected = set() selected_add = selected.add for i in range(k): j = randbelow(n) while j in selected: j = randbelow(n) selected_add(j) result[i] = population[j] return result
def choices(self, population, weights=None, *, cum_weights=None, k=1): """Return a k sized list of population elements chosen with replacement.
If the relative weights or cumulative weights are not specified, the selections are made with equal probability.
""" random = self.random n = len(population) if cum_weights is None: if weights is None: floor = _floor n += 0.0 # convert to float for a small speed improvement return [population[floor(random() * n)] for i in _repeat(None, k)] try: cum_weights = list(_accumulate(weights)) except TypeError: if not isinstance(weights, int): raise k = weights raise TypeError( f'The number of choices must be a keyword argument: {k=}' ) from None elif weights is not None: raise TypeError('Cannot specify both weights and cumulative weights') if len(cum_weights) != n: raise ValueError('The number of weights does not match the population') total = cum_weights[-1] + 0.0 # convert to float if total <= 0.0: raise ValueError('Total of weights must be greater than zero') if not _isfinite(total): raise ValueError('Total of weights must be finite') bisect = _bisect hi = n - 1 return [population[bisect(cum_weights, random() * total, 0, hi)] for i in _repeat(None, k)]
## -------------------- real-valued distributions -------------------
def uniform(self, a, b): """Get a random number in the range [a, b) or [a, b] depending on rounding.
The mean (expected value) and variance of the random variable are:
E[X] = (a + b) / 2 Var[X] = (b - a) ** 2 / 12
""" return a + (b - a) * self.random()
def triangular(self, low=0.0, high=1.0, mode=None): """Triangular distribution.
Continuous distribution bounded by given lower and upper limits, and having a given mode value in-between.
http://en.wikipedia.org/wiki/Triangular_distribution
The mean (expected value) and variance of the random variable are:
E[X] = (low + high + mode) / 3 Var[X] = (low**2 + high**2 + mode**2 - low*high - low*mode - high*mode) / 18
""" u = self.random() try: c = 0.5 if mode is None else (mode - low) / (high - low) except ZeroDivisionError: return low if u > c: u = 1.0 - u c = 1.0 - c low, high = high, low return low + (high - low) * _sqrt(u * c)
def normalvariate(self, mu=0.0, sigma=1.0): """Normal distribution.
mu is the mean, and sigma is the standard deviation.
""" # Uses Kinderman and Monahan method. Reference: Kinderman, # A.J. and Monahan, J.F., "Computer generation of random # variables using the ratio of uniform deviates", ACM Trans # Math Software, 3, (1977), pp257-260.
random = self.random while True: u1 = random() u2 = 1.0 - random() z = NV_MAGICCONST * (u1 - 0.5) / u2 zz = z * z / 4.0 if zz <= -_log(u2): break return mu + z * sigma
def gauss(self, mu=0.0, sigma=1.0): """Gaussian distribution.
mu is the mean, and sigma is the standard deviation. This is slightly faster than the normalvariate() function.
Not thread-safe without a lock around calls.
""" # When x and y are two variables from [0, 1), uniformly # distributed, then # # cos(2*pi*x)*sqrt(-2*log(1-y)) # sin(2*pi*x)*sqrt(-2*log(1-y)) # # are two *independent* variables with normal distribution # (mu = 0, sigma = 1). # (Lambert Meertens) # (corrected version; bug discovered by Mike Miller, fixed by LM)
# Multithreading note: When two threads call this function # simultaneously, it is possible that they will receive the # same return value. The window is very small though. To # avoid this, you have to use a lock around all calls. (I # didn't want to slow this down in the serial case by using a # lock here.)
random = self.random z = self.gauss_next self.gauss_next = None if z is None: x2pi = random() * TWOPI g2rad = _sqrt(-2.0 * _log(1.0 - random())) z = _cos(x2pi) * g2rad self.gauss_next = _sin(x2pi) * g2rad
return mu + z * sigma
def lognormvariate(self, mu, sigma): """Log normal distribution.
If you take the natural logarithm of this distribution, you'll get a normal distribution with mean mu and standard deviation sigma. mu can have any value, and sigma must be greater than zero.
""" return _exp(self.normalvariate(mu, sigma))
def expovariate(self, lambd=1.0): """Exponential distribution.
lambd is 1.0 divided by the desired mean. It should be nonzero. (The parameter would be called "lambda", but that is a reserved word in Python.) Returned values range from 0 to positive infinity if lambd is positive, and from negative infinity to 0 if lambd is negative.
The mean (expected value) and variance of the random variable are:
E[X] = 1 / lambd Var[X] = 1 / lambd ** 2
""" # we use 1-random() instead of random() to preclude the # possibility of taking the log of zero.
return -_log(1.0 - self.random()) / lambd
def vonmisesvariate(self, mu, kappa): """Circular data distribution.
mu is the mean angle, expressed in radians between 0 and 2*pi, and kappa is the concentration parameter, which must be greater than or equal to zero. If kappa is equal to zero, this distribution reduces to a uniform random angle over the range 0 to 2*pi.
""" # Based upon an algorithm published in: Fisher, N.I., # "Statistical Analysis of Circular Data", Cambridge # University Press, 1993.
# Thanks to Magnus Kessler for a correction to the # implementation of step 4.
random = self.random if kappa <= 1e-6: return TWOPI * random()
s = 0.5 / kappa r = s + _sqrt(1.0 + s * s)
while True: u1 = random() z = _cos(_pi * u1)
d = z / (r + z) u2 = random() if u2 < 1.0 - d * d or u2 <= (1.0 - d) * _exp(d): break
q = 1.0 / r f = (q + z) / (1.0 + q * z) u3 = random() if u3 > 0.5: theta = (mu + _acos(f)) % TWOPI else: theta = (mu - _acos(f)) % TWOPI
return theta
def gammavariate(self, alpha, beta): """Gamma distribution. Not the gamma function!
Conditions on the parameters are alpha > 0 and beta > 0.
The probability distribution function is:
x ** (alpha - 1) * math.exp(-x / beta) pdf(x) = -------------------------------------- math.gamma(alpha) * beta ** alpha
The mean (expected value) and variance of the random variable are:
E[X] = alpha * beta Var[X] = alpha * beta ** 2
"""
# Warning: a few older sources define the gamma distribution in terms # of alpha > -1.0 if alpha <= 0.0 or beta <= 0.0: raise ValueError('gammavariate: alpha and beta must be > 0.0')
random = self.random if alpha > 1.0:
# Uses R.C.H. Cheng, "The generation of Gamma # variables with non-integral shape parameters", # Applied Statistics, (1977), 26, No. 1, p71-74
ainv = _sqrt(2.0 * alpha - 1.0) bbb = alpha - LOG4 ccc = alpha + ainv
while True: u1 = random() if not 1e-7 < u1 < 0.9999999: continue u2 = 1.0 - random() v = _log(u1 / (1.0 - u1)) / ainv x = alpha * _exp(v) z = u1 * u1 * u2 r = bbb + ccc * v - x if r + SG_MAGICCONST - 4.5 * z >= 0.0 or r >= _log(z): return x * beta
elif alpha == 1.0: # expovariate(1/beta) return -_log(1.0 - random()) * beta
else: # alpha is between 0 and 1 (exclusive) # Uses ALGORITHM GS of Statistical Computing - Kennedy & Gentle while True: u = random() b = (_e + alpha) / _e p = b * u if p <= 1.0: x = p ** (1.0 / alpha) else: x = -_log((b - p) / alpha) u1 = random() if p > 1.0: if u1 <= x ** (alpha - 1.0): break elif u1 <= _exp(-x): break return x * beta
def betavariate(self, alpha, beta): """Beta distribution.
Conditions on the parameters are alpha > 0 and beta > 0. Returned values range between 0 and 1.
The mean (expected value) and variance of the random variable are:
E[X] = alpha / (alpha + beta) Var[X] = alpha * beta / ((alpha + beta)**2 * (alpha + beta + 1))
""" ## See ## http://mail.python.org/pipermail/python-bugs-list/2001-January/003752.html ## for Ivan Frohne's insightful analysis of why the original implementation: ## ## def betavariate(self, alpha, beta): ## # Discrete Event Simulation in C, pp 87-88. ## ## y = self.expovariate(alpha) ## z = self.expovariate(1.0/beta) ## return z/(y+z) ## ## was dead wrong, and how it probably got that way.
# This version due to Janne Sinkkonen, and matches all the std # texts (e.g., Knuth Vol 2 Ed 3 pg 134 "the beta distribution"). y = self.gammavariate(alpha, 1.0) if y: return y / (y + self.gammavariate(beta, 1.0)) return 0.0
def paretovariate(self, alpha): """Pareto distribution. alpha is the shape parameter.""" # Jain, pg. 495
u = 1.0 - self.random() return u ** (-1.0 / alpha)
def weibullvariate(self, alpha, beta): """Weibull distribution.
alpha is the scale parameter and beta is the shape parameter.
""" # Jain, pg. 499; bug fix courtesy Bill Arms
u = 1.0 - self.random() return alpha * (-_log(u)) ** (1.0 / beta)
## -------------------- discrete distributions ---------------------
def binomialvariate(self, n=1, p=0.5): """Binomial random variable.
Gives the number of successes for *n* independent trials with the probability of success in each trial being *p*:
sum(random() < p for i in range(n))
Returns an integer in the range: 0 <= X <= n
The mean (expected value) and variance of the random variable are:
E[X] = n * p Var[x] = n * p * (1 - p)
""" # Error check inputs and handle edge cases if n < 0: raise ValueError("n must be non-negative") if p <= 0.0 or p >= 1.0: if p == 0.0: return 0 if p == 1.0: return n raise ValueError("p must be in the range 0.0 <= p <= 1.0")
random = self.random
# Fast path for a common case if n == 1: return _index(random() < p)
# Exploit symmetry to establish: p <= 0.5 if p > 0.5: return n - self.binomialvariate(n, 1.0 - p)
if n * p < 10.0: # BG: Geometric method by Devroye with running time of O(np). # https://dl.acm.org/doi/pdf/10.1145/42372.42381 x = y = 0 c = _log2(1.0 - p) if not c: return x while True: y += _floor(_log2(random()) / c) + 1 if y > n: return x x += 1
# BTRS: Transformed rejection with squeeze method by Wolfgang Hörmann # https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.47.8407&rep=rep1&type=pdf assert n*p >= 10.0 and p <= 0.5 setup_complete = False
spq = _sqrt(n * p * (1.0 - p)) # Standard deviation of the distribution b = 1.15 + 2.53 * spq a = -0.0873 + 0.0248 * b + 0.01 * p c = n * p + 0.5 vr = 0.92 - 4.2 / b
while True:
u = random() u -= 0.5 us = 0.5 - _fabs(u) k = _floor((2.0 * a / us + b) * u + c) if k < 0 or k > n: continue
# The early-out "squeeze" test substantially reduces # the number of acceptance condition evaluations. v = random() if us >= 0.07 and v <= vr: return k
# Acceptance-rejection test. # Note, the original paper erroneously omits the call to log(v) # when comparing to the log of the rescaled binomial distribution. if not setup_complete: alpha = (2.83 + 5.1 / b) * spq lpq = _log(p / (1.0 - p)) m = _floor((n + 1) * p) # Mode of the distribution h = _lgamma(m + 1) + _lgamma(n - m + 1) setup_complete = True # Only needs to be done once v *= alpha / (a / (us * us) + b) if _log(v) <= h - _lgamma(k + 1) - _lgamma(n - k + 1) + (k - m) * lpq: return k
## ------------------------------------------------------------------## --------------- Operating System Random Source ------------------
class SystemRandom(Random): """Alternate random number generator using sources provided by the operating system (such as /dev/urandom on Unix or CryptGenRandom on Windows).
Not available on all systems (see os.urandom() for details).
"""
def random(self): """Get the next random number in the range 0.0 <= X < 1.0.""" return (int.from_bytes(_urandom(7)) >> 3) * RECIP_BPF
def getrandbits(self, k): """getrandbits(k) -> x. Generates an int with k random bits.""" if k < 0: raise ValueError('number of bits must be non-negative') numbytes = (k + 7) // 8 # bits / 8 and rounded up x = int.from_bytes(_urandom(numbytes)) return x >> (numbytes * 8 - k) # trim excess bits
def randbytes(self, n): """Generate n random bytes.""" # os.urandom(n) fails with ValueError for n < 0 # and returns an empty bytes string for n == 0. return _urandom(n)
def seed(self, *args, **kwds): "Stub method. Not used for a system random number generator." return None
def _notimplemented(self, *args, **kwds): "Method should not be called for a system random number generator." raise NotImplementedError('System entropy source does not have state.') getstate = setstate = _notimplemented
# ----------------------------------------------------------------------# Create one instance, seeded from current time, and export its methods# as module-level functions. The functions share state across all uses# (both in the user's code and in the Python libraries), but that's fine# for most programs and is easier for the casual user than making them# instantiate their own Random() instance.
_inst = Random()seed = _inst.seedrandom = _inst.randomuniform = _inst.uniformtriangular = _inst.triangularrandint = _inst.randintchoice = _inst.choicerandrange = _inst.randrangesample = _inst.sampleshuffle = _inst.shufflechoices = _inst.choicesnormalvariate = _inst.normalvariatelognormvariate = _inst.lognormvariateexpovariate = _inst.expovariatevonmisesvariate = _inst.vonmisesvariategammavariate = _inst.gammavariategauss = _inst.gaussbetavariate = _inst.betavariatebinomialvariate = _inst.binomialvariateparetovariate = _inst.paretovariateweibullvariate = _inst.weibullvariategetstate = _inst.getstatesetstate = _inst.setstategetrandbits = _inst.getrandbitsrandbytes = _inst.randbytes
## ------------------------------------------------------## ----------------- test program -----------------------
def _test_generator(n, func, args): from statistics import stdev, fmean as mean from time import perf_counter
t0 = perf_counter() data = [func(*args) for i in _repeat(None, n)] t1 = perf_counter()
xbar = mean(data) sigma = stdev(data, xbar) low = min(data) high = max(data)
print(f'{t1 - t0:.3f} sec, {n} times {func.__name__}{args!r}') print('avg %g, stddev %g, min %g, max %g\n' % (xbar, sigma, low, high))
def _test(N=10_000): _test_generator(N, random, ()) _test_generator(N, normalvariate, (0.0, 1.0)) _test_generator(N, lognormvariate, (0.0, 1.0)) _test_generator(N, vonmisesvariate, (0.0, 1.0)) _test_generator(N, binomialvariate, (15, 0.60)) _test_generator(N, binomialvariate, (100, 0.75)) _test_generator(N, gammavariate, (0.01, 1.0)) _test_generator(N, gammavariate, (0.1, 1.0)) _test_generator(N, gammavariate, (0.1, 2.0)) _test_generator(N, gammavariate, (0.5, 1.0)) _test_generator(N, gammavariate, (0.9, 1.0)) _test_generator(N, gammavariate, (1.0, 1.0)) _test_generator(N, gammavariate, (2.0, 1.0)) _test_generator(N, gammavariate, (20.0, 1.0)) _test_generator(N, gammavariate, (200.0, 1.0)) _test_generator(N, gauss, (0.0, 1.0)) _test_generator(N, betavariate, (3.0, 3.0)) _test_generator(N, triangular, (0.0, 1.0, 1.0 / 3.0))
## ------------------------------------------------------## ------------------ fork support ---------------------
if hasattr(_os, "fork"): _os.register_at_fork(after_in_child=_inst.seed)
if __name__ == '__main__': _test()
可以看到出题人自己搓了一个随机数脚本,其实就是getrandbits(8)
?这个是真的不太懂,黄博士哪里有现成的脚本,直接梭哈出来78个256位的,也就和624个32位的等价了,然后extend_mt19937_predictor
去恢复前一个随机数,但是会发现和enc的位数不太对,爆破一下就好了
exp.py
from Crypto.Util.number import *from random import *from tqdm import *from sage.all import *r= []rr = [255- x for x in r]
n=2496D=rr[:n]rng=Random()def getRows(rng): #这一部分根据题目实际编写,必须和题目实际比特获取顺序和方式完全一致,且确保比特数大于19937,并且请注意zfill。 row=[] for i in range(n): row+=list(map(int, (bin(rng.getrandbits(8))[2:].zfill(8)))) return rowM=[]for i in range(19968):#这一部分为固定套路,具体原因已经写在注释中了 state = [0]*624 temp = "0"*i + "1"*1 + "0"*(19968-1-i) for j in range(624): state[j] = int(temp[32*j:32*j+32],2) rng.setstate((3,tuple(state+[624]),None)) #这个setstate也是固定格式,已于2025.1.21测试 M.append(getRows(rng))M=Matrix(GF(2),M)y=[]for i in range(n): y+=list(map(int, (bin(D[i])[2:].zfill(8))))y=vector(GF(2),y)s=M.solve_left(y)#print(s)G=[]for i in range(624): C=0 for j in range(32): C<<=1 C|=int(s[32*i+j]) G.append(C)import randomRNG1 = random.Random()for i in range(624): G[i]=int(G[i])RNG1.setstate((int(3),tuple(G+[int(624)]),None))
print([RNG1.getrandbits(256)for _ in range(78)])
import randomfrom extend_mt19937_predictor import ExtendMT19937Predictorfrom Crypto.Util.number import long_to_bytesrrr=[]enc = 5042764371819053176884777909105310461303359296255297n = enc.bit_length()
predictor = ExtendMT19937Predictor()
for i in range(78): predictor.setrandbits(rrr[i], 256)
_ = [predictor.backtrack_getrandbits(256) for _ in range(78)]
#爆破到3出来了xx = predictor.backtrack_getrandbits(n + 3)
mm = enc ^ xxprint(long_to_bytes(mm))
prng_xxxx
等官方wp吧
总结
质量真的挺高的,不愧是大恶人出的题(,但是队友的发挥,害,算了,吐槽到不想吐槽了