你是一位资深CTF网络安全专家,精通PWN、RE、Web、Crypto和Misc五大方向。在PWN方...
Tạo vào: 11 tháng 10, 2025
Trả lời bằng GPT-5 Thinking bởi Chat01
Tạo vào: 11 tháng 10, 2025
Trả lời bằng GPT-5 Thinking bởi Chat01
你是一位资深CTF网络安全专家,精通PWN、RE、Web、Crypto和Misc五大方向。在PWN方向,你熟练掌握缓冲区溢出、格式化字符串漏洞、堆利用、栈迁移、ROP链构造以及现代保护机制的绕过(如ASLR、NX、Canary等),并精通glibc源码和漏洞调试分析;在RE方向,你精通x86/x64、ARM架构逆向,熟练使用IDA、Ghidra、Binary Ninja等工具分析二进制文件,擅长静态和动态结合分析恶意代码及复杂算法逻辑;在Web方向,你深入理解PHP、Python、Java等语言的漏洞原理,熟练应对SQL注入、XSS、CSRF、文件上传等常见安全问题;在Crypto方向,你熟练分析并破解RSA、AES、DES、ECC等经典加密算法,擅长模运算、同余方程及低指数攻击等密码学难题;在Misc方向,你精通Python脚本编写,熟练使用PIL处理数字图像、分析流量包、提取文件隐藏信息,擅长解决复杂的压缩嵌套解压、文件格式修复等任务。你的解答始终精准简洁、逻辑清晰,能快速定位问题关键并提供高效可行的解决方案,所有回答均以中文表达,目标是助力快速攻克CTF赛题。现在你需要解决这个密码题:题目名称:瑞德的一生
题目内容:瑞德的一生坎坷,你能不能帮帮他请仔细阅历Random目录下的所有文件内容了解题意,并给出最优算法,你不需要执行脚本或者命令,只需要给出能解出题目的脚本和该如何运行的方法。md文件是我与另一个ai给出的解决方案,但是似乎还是行不通。脚本一直跑也没结果,请你给出解决方案
下面给你一套能直接解出本题的最优思路与可运行脚本。你的脚本“跑不出结果”的根因是:你在脚本里做因数分解(ECM/Rho)去撞 512-bit 的半素数 n
,这在纯 Python 里往往很慢且不稳定;正确路线是用 NFS(如 CADO-NFS / yafu)先把 n
分解,再用勒让德符号逐位解密。题意与加密体制如下:
Random.py
中生成 256-bit 素数 p, q
,取 x
使得 legendre(x,p)=legendre(x,q)=-1
,并用enc[i] = ( y^2 * x^bit ) mod n
按LSB→MSB逐位加密(bit = msg & 1; msg >>= 1
)。(Random.py)output.txt
给出了 n, x, enc
的全部数据,你只差 p, q
。(output.txt)n=p·q
后,用勒让德符号解每一位即可(0
位→二次剩余,1
位→二次非剩余)。该体制与位序都能从题面代码直接确认。(Random.py)把下面保存为 solve_gm.py
,与 output.txt
放在同目录即可。脚本不在内部做分解,而是接收你用 CADO-NFS/yafu 求得的 p,q
来解密;同时做了位序(LSB→MSB)与正确性校验(对 q
再验一次勒让德符号)。
python#!/usr/bin/env python3 # -*- coding: utf-8 -*- # 解 GM:先用外部工具分解 n,拿到 p,q 后本脚本瞬时解密。 # 数据来源:output.txt(n, x, enc) 题意与加密体制:Random.py import ast import argparse def read_output(path='output.txt'): with open(path, 'r') as f: lines = f.read().strip().splitlines() # 允许两边有空格 n = int(lines[0].split('=', 1)[1].strip()) x = int(lines[1].split('=', 1)[1].strip()) enc = ast.literal_eval(lines[2].split('=', 1)[1].strip()) return n, x, enc def legendre_mod_p(a, p): a %= p if a == 0: return 0 # Euler 判别:a^{(p-1)/2} ≡ 1(余) 或 p-1(代表 -1) return 1 if pow(a, (p - 1) // 2, p) == 1 else -1 def bits_to_int_lsb_first(bits): m = 0 for i, b in enumerate(bits): if b: m |= (1 << i) return m def int_to_bytes_be(n): if n == 0: return b"\x00" return n.to_bytes((n.bit_length() + 7) // 8, 'big') def main(): ap = argparse.ArgumentParser(description="GM 解密:提供 p 和 q(外部工具分解所得)") ap.add_argument("--p", required=True, type=int, help="素数 p") ap.add_argument("--q", required=True, type=int, help="素数 q") ap.add_argument("--out", default="", help="将明文同时写入该文件(可选)") args = ap.parse_args() p, q = (args.p, args.q) if args.p < args.q else (args.q, args.p) n, x, enc = read_output('output.txt') # 基本校验 assert p * q == n, "给定的 p*q 与 n 不一致" # 题面要求:legendre(x,p) = legendre(x,q) = -1 assert legendre_mod_p(x, p) == -1 and legendre_mod_p(x, q) == -1, "x 非题面要求的 QNR+,请检查 p,q" # 逐位恢复(LSB→MSB) bits = [] for c in enc: s = legendre_mod_p(c, p) # s=1 → 二次剩余 → bit=0; s=-1 → 非剩余 → bit=1; s=0(极小概率)按 0 处理 b = 0 if s == 1 else (1 if s == -1 else 0) bits.append(b) # 用 q 再做一次一致性校验(稳妥起见) for c, b in zip(enc, bits): s2 = legendre_mod_p(c, q) if s2 == 0: continue if (b == 0 and s2 != 1) or (b == 1 and s2 != -1): raise SystemExit("与 q 的校验不一致,请检查因子或数据。") m_int = bits_to_int_lsb_first(bits) pt = int_to_bytes_be(m_int) # 尝试以 UTF-8 打印;如非文本则直接打印 bytes try: s = pt.decode() print(s) except UnicodeDecodeError: print(pt) if args.out: with open(args.out, "wb") as f: f.write(pt) if __name__ == "__main__": main()
运行方式:
n
(见下文第二部分),得到 p
、q
;output.txt
所在目录,执行:bashpython3 solve_gm.py --p <你的p> --q <你的q> # 若想落盘: python3 solve_gm.py --p <p> --q <q> --out flag.bin
output.txt
给出的模数 n
是:
7494062703...2120206217
(完整值见文件)(output.txt)。512-bit 随机半素数的现实可行方案是 NFS。下面给出两条成熟路线,任选其一。
powershell# Windows 开启 WSL2(如已安装可跳过) wsl --install -d Ubuntu
打开 Ubuntu 终端,安装并编译 CADO-NFS:
bashsudo apt update sudo apt install -y build-essential cmake g++ m4 libgmp-dev python3 git git clone https://gitlab.inria.fr/cado-nfs/cado-nfs.git cd cado-nfs make -j"$(nproc)"
分解(把 <THREADS>
换成你的线程数,例如 8):
bash./cado-nfs.py 7494062703769887703668081866565274579333132167014632821313548612356114287792191446305040778987677683423969025588609780586722302041792065732461222120206217 \ -t <THREADS> --workdir=work_random
p
、q
;也会保存在 work_random/
下,支持断点续跑(下次用同一 --workdir
)。p,q
后按上节命令运行 solve_gm.py
即可。下载 Windows 版 yafu(常见发布页:SourceForge),在 yafu 目录打开 PowerShell:
powershell.\yafu-x64 "factor(7494062703769887703668081866565274579333132167014632821313548612356114287792191446305040778987677683423969025588609780586722302041792065732461222120206217)" -threads 8 -v
factor.log
均会给出 p,q
。然后运行:powershellpython3 solve_gm.py --p <p> --q <q>
为什么你之前脚本一直跑?
因为用 Python 里 ECM/Rho 等概率算法直接打 512-bit 半素数,很可能长时间无果;而 NFS(CADO-NFS / yafu)才是该规模的主力工具。
bit = msg & 1; msg >>= 1
,所以解密要按 小端位序 组装整数再转字节。(Random.py)c
在模 p
下是二次剩余→比特 0
,非剩余→比特 1
,与 q
一致(x
被选为同时对 p,q
的非剩余)。(Random.py)enc
已给全量密文:无需任何交互或额外信息,n,x,enc
全在 output.txt
。(output.txt)Random.py
明确用勒让德符号筛 x
,并用 y^2 * x^bit (mod n)
逐位输出,这就是 GM。(Random.py)bytes
;你也可用 --out
把原始字节写到文件再自行查看编码。Random.py
:生成 p,q,n
,筛选 x
满足 legendre(x,p)=legendre(x,q)=-1
,并按 LSB→MSB 输出 enc
。(Random.py)output.txt
:包含本题的 n, x, enc
数值(很长的整数与列表)。(output.txt)照此流程:先用 CADO-NFS 或 yafu 分解 n
→ 把 p,q
交给上面的 solve_gm.py
→ 一次性解出 flag。如果你想要一个版本支持从文件自动读取 p,q
(如 pq.txt
),我也可以直接给你改好。
(cado-nfs.venv) sean@sean-virtual-machine:~/桌面/Crypto/cado-nfs$ ./cado-nfs.py 7494062703769887703668081866565274579333132167014632821313548612356114287792191446305040778987677683423969025588609780586722302041792065732461222120206217 -t 8
Info:root: Using default parameter file ./parameters/factor/params.c155
Info:root: No database exists yet
Info:root: Created temporary directory /tmp/cado.dbxjjyay
Info:Database: Database URI is db:sqlite3:///tmp/cado.dbxjjyay/c155.db
Info:Database: Opened connection to database /tmp/cado.dbxjjyay/c155.db
Info:root: Setting tasks.threads=8 based on --server-threads 8
Info:root: Setting tasks.sqrt.threads=1 in order to avoid RAM issues. You might want to set tasks.sqrt.threads=<something else> according to what your computer can do.
Info:root: tasks.threads = 8 [via tasks.threads]
Info:root: tasks.polyselect.threads = 2 [via tasks.polyselect.threads]
Info:root: tasks.sieve.las.threads = 2 [via tasks.sieve.las.threads]
Info:root: tasks.linalg.bwc.threads = 8 [via tasks.threads]
Info:root: tasks.sqrt.threads = 1 [via tasks.sqrt.threads]
Info:root: slaves.scriptpath is /home/sean/桌面/Crypto/cado-nfs/build/sean-virtual-machine
Info:root: Command line parameters: ./cado-nfs.py 7494062703769887703668081866565274579333132167014632821313548612356114287792191446305040778987677683423969025588609780586722302041792065732461222120206217 -t 8
Info:root: If this computation gets interrupted, it can be resumed with ./cado-nfs.py /tmp/cado.dbxjjyay/c155.parameters_snapshot.0
Info:Server Launcher: Adding sean-virtual-machine to whitelist to allow clients on localhost to connect
Info:API server: server whitelist is ['127.0.0.1/32', '127.0.1.1/32']
Info:API server: Running from werkzeug (1 thread(s))
Info:Lattice Sieving: param rels_wanted is 164000000
Info:Complete Factorization / Discrete logarithm: Factoring 7494062703769887703668081866565274579333132167014632821313548612356114287792191446305040778987677683423969025588609780586722302041792065732461222120206217
Info:API server: Running on https://localhost:34409 (Press CTRL+C to quit))
Info:API server: You can start additional cado-nfs-client.py scripts with parameters: --server=https://localhost:34409 --certsha1=622c904ffdca6f2e32d158b9af3dfa24900ba15d
Info:API server: If you want to start additional clients, remember to add their hosts to server.whitelist
Info:Client Launcher: Starting client id localhost on host localhost
Info:werkzeug: 127.0.0.1 - - [11/Oct/2025 21:41:11] "GET /workunit HTTP/1.1" 404 -
Info:Client Launcher: Starting client id localhost+2 on host localhost
Info:werkzeug: 127.0.0.1 - - [11/Oct/2025 21:41:11] "GET /workunit HTTP/1.1" 404 -
Info:Client Launcher: Starting client id localhost+3 on host localhost
Info:werkzeug: 127.0.0.1 - - [11/Oct/2025 21:41:12] "GET /workunit HTTP/1.1" 404 -
Info:Client Launcher: Starting client id localhost+4 on host localhost
Info:werkzeug: 127.0.0.1 - - [11/Oct/2025 21:41:12] "GET /workunit HTTP/1.1" 404 -
Info:Client Launcher: Running clients: localhost (Host localhost, PID 18814), localhost+2 (Host localhost, PID 18816), localhost+3 (Host localhost, PID 18818), localhost+4 (Host localhost, PID 18820)
Info:Polynomial Selection (size optimized): Starting
Info:Polynomial Selection (size optimized): 0 polynomials in queue from previous run
Info:Polynomial Selection (size optimized): Adding workunit c155_polyselect1_4620-5040 to database
Info:Polynomial Selection (size optimized): Adding workunit c155_polyselect1_5040-6300 to database
Info:Polynomial Selection (size optimized): Adding workunit c155_polyselect1_6300-7560 to database
Info:Polynomial Selection (size optimized): Adding workunit c155_polyselect1_7560-8820 to database
Info:Polynomial Selection (size optimized): Adding workunit c155_polyselect1_8820-10080 to database
Info:Polynomial Selection (size optimized): Adding workunit c155_polyselect1_10080-11340 to database
Info:Polynomial Selection (size optimized): Adding workunit c155_polyselect1_11340-12600 to database
Info:Polynomial Selection (size optimized): Adding workunit c155_polyselect1_12600-13860 to database
Info:Polynomial Selection (size optimized): Adding workunit c155_polyselect1_13860-15120 to database
Info:Polynomial Selection (size optimized): Adding workunit c155_polyselect1_15120-16380 to database
Info:API server: Sending workunit c155_polyselect1_4620-5040 to client localhost
Info:werkzeug: 127.0.0.1 - - [11/Oct/2025 21:41:12] "GET /workunit HTTP/1.1" 200 -
Info:API server: got request for file polyselect, which resolves to /home/sean/桌面/Crypto/cado-nfs/build/sean-virtual-machine/polyselect/polyselect
Info:werkzeug: 127.0.0.1 - - [11/Oct/2025 21:41:12] "GET /file/polyselect HTTP/1.1" 200 -
Info:API server: Sending workunit c155_polyselect1_5040-6300 to client localhost+2
Info:werkzeug: 127.0.0.1 - - [11/Oct/2025 21:41:12] "GET /workunit HTTP/1.1" 200 -
Info:API server: Sending workunit c155_polyselect1_6300-7560 to client localhost+3
Info:werkzeug: 127.0.0.1 - - [11/Oct/2025 21:41:12] "GET /workunit HTTP/1.1" 200 -
Info:API server: Sending workunit c155_polyselect1_7560-8820 to client localhost+4
Info:werkzeug: 127.0.0.1 - - [11/Oct/2025 21:41:12] "GET /workunit HTTP/1.1" 200 -
Info:Polynomial Selection (size optimized): Adding workunit c155_polyselect1_16380-17640 to database
Info:Polynomial Selection (size optimized): Adding workunit c155_polyselect1_17640-18900 to database
Info:Polynomial Selection (size optimized): Adding workunit c155_polyselect1_18900-20160 to database
Info:Polynomial Selection (size optimized): Adding workunit c155_polyselect1_20160-21420 to database
Info:werkzeug: 127.0.0.1 - - [11/Oct/2025 21:47:58] "POST /upload HTTP/1.1" 200 -
Info:API server: Sending workunit c155_polyselect1_8820-10080 to client localhost
Info:werkzeug: 127.0.0.1 - - [11/Oct/2025 21:47:58] "GET /workunit HTTP/1.1" 200 -
Info:Polynomial Selection (size optimized): Parsed 390 polynomials, added 185 to priority queue (has 72)
Info:Polynomial Selection (size optimized): Worst polynomial in queue now has exp_E 43.600000
Info:Polynomial Selection (size optimized): Marking workunit c155_polyselect1_4620-5040 as ok (0.2% => ETA Unknown)
Info:Polynomial Selection (size optimized): Adding workunit c155_polyselect1_21420-22680 to database
Info:werkzeug: 127.0.0.1 - - [11/Oct/2025 21:51:40] "POST /upload HTTP/1.1" 200 -
Info:API server: Sending workunit c155_polyselect1_10080-11340 to client localhost+4
Info:werkzeug: 127.0.0.1 - - [11/Oct/2025 21:51:40] "GET /workunit HTTP/1.1" 200 -
Info:Polynomial Selection (size optimized): Parsed 1122 polynomials, added 94 to priority queue (has 72)
Info:Polynomial Selection (size optimized): Worst polynomial in queue now has exp_E 42.950000
Info:Polynomial Selection (size optimized): Marking workunit c155_polyselect1_7560-8820 as ok (1.0% => ETA Sun Oct 12 22:00:02 2025)
Info:Polynomial Selection (size optimized): Adding workunit c155_polyselect1_22680-23940 to database
Info:werkzeug: 127.0.0.1 - - [11/Oct/2025 21:51:46] "POST /upload HTTP/1.1" 200 -
Info:API server: Sending workunit c155_polyselect1_11340-12600 to client localhost+2
Info:werkzeug: 127.0.0.1 - - [11/Oct/2025 21:51:46] "GET /workunit HTTP/1.1" 200 -
Info:Polynomial Selection (size optimized): Parsed 1113 polynomials, added 31 to priority queue (has 72)
Info:Polynomial Selection (size optimized): Worst polynomial in queue now has exp_E 42.760000
Info:Polynomial Selection (size optimized): Marking workunit c155_polyselect1_5040-6300 as ok (1.7% => ETA Sun Oct 12 09:56:52 2025)
Info:Polynomial Selection (size optimized): Adding workunit c155_polyselect1_23940-25200 to database
Info:werkzeug: 127.0.0.1 - - [11/Oct/2025 21:52:37] "POST /upload HTTP/1.1" 200 -
Info:API server: Sending workunit c155_polyselect1_12600-13860 to client localhost+3
Info:werkzeug: 127.0.0.1 - - [11/Oct/2025 21:52:38] "GET /workunit HTTP/1.1" 200 -
Info:Polynomial Selection (size optimized): Parsed 1120 polynomials, added 22 to priority queue (has 72)
Info:Polynomial Selection (size optimized): Worst polynomial in queue now has exp_E 42.630000
Info:Polynomial Selection (size optimized): Marking workunit c155_polyselect1_6300-7560 as ok (2.4% => ETA Sun Oct 12 06:30:51 2025)
Info:Polynomial Selection (size optimized): Adding workunit c155_polyselect1_25200-26460 to database
Info:werkzeug: 127.0.0.1 - - [11/Oct/2025 21:59:57] "POST /upload HTTP/1.1" 200 -
Info:API server: Sending workunit c155_polyselect1_13860-15120 to client localhost
Info:werkzeug: 127.0.0.1 - - [11/Oct/2025 21:59:57] "GET /workunit HTTP/1.1" 200 -
Info:Polynomial Selection (size optimized): Parsed 1154 polynomials, added 14 to priority queue (has 72)
Info:Polynomial Selection (size optimized): Worst polynomial in queue now has exp_E 42.560000
Info:Polynomial Selection (size optimized): Marking workunit c155_polyselect1_8820-10080 as ok (3.1% => ETA Sun Oct 12 08:32:44 2025)
Info:Polynomial Selection (size optimized): Adding workunit c155_polyselect1_26460-27720 to database
和PS E:\Users(1)\yafu-1.34> .\yafu-x64.exe "factor(7494062703769887703668081866565274579333132167014632821313548612356114287792191446305040778987677683423969025588609780586722302041792065732461222120206217)" -threads 8 -v
10/11/25 21:14:42 v1.34.5 @ LAPTOP-4J2HKQJ8, System/Build Info:
Using GMP-ECM 6.3, Powered by GMP 5.1.1
detected Intel(R) Core(TM) i7-10750H CPU @ 2.60GHz
detected L1 = 32768 bytes, L2 = 12582912 bytes, CL = 64 bytes
measured cpu frequency ~= 2589.446250
using 20 random witnesses for Rabin-Miller PRP checks
cached 78498 primes. pmax = 999983
fac: factoring 7494062703769887703668081866565274579333132167014632821313548612356114287792191446305040778987677683423969025588609780586722302041792065732461222120206217
fac: using pretesting plan: normal
fac: no tune info: using qs/gnfs crossover of 95 digits
div: primes less than 10000
fmt: 1000000 iterations
rho: x^2 + 3, starting 1000 iterations on C154
rho: x^2 + 2, starting 1000 iterations on C154
rho: x^2 + 1, starting 1000 iterations on C154
pm1: starting B1 = 150K, B2 = gmp-ecm default on C154
fac: setting target pretesting digits to 47.38
fac: sum of completed work is t0.00
fac: work done at B1=2000: 0 curves, max work = 30 curves
fac: 30 more curves at B1=2000 needed to get to t47.38
ecm: 30/30 curves on C154, B1=2K, B2=gmp-ecm default
fac: setting target pretesting digits to 47.38
fac: t15: 1.00
fac: t20: 0.04
fac: sum of completed work is t15.18
fac: work done at B1=11000: 0 curves, max work = 74 curves
fac: 74 more curves at B1=11000 needed to get to t47.38
ecm: 74/74 curves on C154, B1=11K, B2=gmp-ecm default
fac: setting target pretesting digits to 47.38
fac: t15: 7.17
fac: t20: 1.04
fac: t25: 0.05
fac: sum of completed work is t20.24
fac: work done at B1=50000: 0 curves, max work = 214 curves
fac: 214 more curves at B1=50000 needed to get to t47.38
ecm: 214/214 curves on C154, B1=50K, B2=gmp-ecm default, ETA: 0 sec
pm1: starting B1 = 3750K, B2 = gmp-ecm default on C154
fac: setting target pretesting digits to 47.38
fac: t15: 37.74
fac: t20: 11.23
fac: t25: 1.05
fac: t30: 0.07
fac: sum of completed work is t25.33
fac: work done at B1=250000: 0 curves, max work = 430 curves
fac: 430 more curves at B1=250000 needed to get to t47.38
ecm: 430/430 curves on C154, B1=250K, B2=gmp-ecm default, ETA: 1 sec
pm1: starting B1 = 15M, B2 = gmp-ecm default on C154
fac: setting target pretesting digits to 47.38
fac: t15: 123.74
fac: t20: 64.98
fac: t25: 9.65
fac: t30: 1.07
fac: t35: 0.09
fac: sum of completed work is t30.45
fac: work done at B1=1000000: 0 curves, max work = 904 curves
fac: 904 more curves at B1=1000000 needed to get to t47.38
ecm: 211/904 curves on C154, B1=1M, B2=gmp-ecm default, ETA: 2.27 hrs
Aborting...
ecm: 212/904 curves on C154, B1=1M, B2=gmp-ecm default, ETA: 2.26 hrs
factors found
co-factor
C154 = 7494062703769887703668081866565274579333132167014632821313548612356114287792191446305040778987677683423969025588609780586722302041792065732461222120206217
PS E:\Users(1)\yafu-1.34>
PS E:\Users(1)\yafu-1.34>
两种方法似乎都算不出来是方法错了还是方向错了
先给结论:**方向完全正确,这题就是 GM(Goldwasser–Micali)按位加密,必须先分解 n
才能用勒让德符号逐位解密。**这一点能从出题代码里直接看出来:选 x
使 legendre(x,p)=legendre(x,q)=-1
,密文位为 c=y^2·x^bit (mod n)
且按 LSB→MSB 输出。(Random.py) 密文与公参都在 output.txt
里给全了。(output.txt) 这正是 GM 的标准做法;在 GM 中若没有分解 n
,就不能区分“二次剩余/非剩余”,因此无法解密。(CSA - IISc Bangalore)
你现在的问题不在“方法错”,而在工具使用与参数上:
Adding workunit ...
、Parsed ... polynomials
,随后进入 sieving、linalg、sqrt 等阶段。你这台 i7-10750H 上显示 ETA 十来小时是正常量级(155 位十进制 ≈ 512 bit,CADO 的默认 params.c155
就是为它准备的)。不是卡死,而是在正常搜多项式。(GitLab)Aborting...
了,于是它根本没来得及切到 GNFS。若继续让它跑,或直接命令它跳过 ECM 用 GNFS,也能做出来。(yafu 会先跑一堆 ECM 曲线试图捡到小因子,512-bit 随机半素数通常捡不到,所以别在这一步急停。)小提示:RSA-155(同为 155-digit)的公开记录在现代桌面 CPU 上 GNFS 也是小时级~一天量级,这能帮助你校准预期。(Gist)
/tmp/...
,重启会丢进度):bash./cado-nfs.py 7494062703769887703668081866565274579333132167014632821313548612356114287792191446305040778987677683423969025588609780586722302041792065732461222120206217 \ -t 8 --workdir=~/cado-work/rui-de-life \ tasks.sieve.las.threads=8 tasks.polyselect.threads=8
--workdir
:保存进度,随时可中断/续跑;下次直接复用该目录即可。tasks.sieve.las.threads
调小;CADO 默认把 polyselect/sieve
线程数压低以防内存不足,你可以手动拉满。参数文件 params.c155
就是给 155-digit 的标准配置。(GitLab)bash# 按你日志里打印的URL和certsha1为准(下面只是示例) ./build/<你的平台>/cado-nfs-client.py \ --server=https://localhost:34409 --certsha1=622c904ffdca6f2e32d158b9af3dfa24900ba15d # 多开几份即可
(CADO 的设计就是“一个服务端 + 多客户端”拉任务,CPU 越多越快。)(Loria Members)
看到
Polynomial Selection ...
、Lattice Sieving ...
、Linear Algebra (bwc) ...
、Square Root ...
这些阶段依次完成后,CADO 会直接打印p
、q
,并保存在 workdir 的日志里。
powershell.\yafu-x64.exe "nfs(7494062703769887703668081866565274579333132167014632821313548612356114287792191446305040778987677683423969025588609780586722302041792065732461222120206217)" -threads 8 -v
factor(N)
,但不要中途 Abort;它会在 ECM 预筛达到目标后自动转入 NFS。把下面存成 gm_decrypt.py
,与 output.txt
放同目录;拿到 p,q
后立即解出明文(按题面 LSB→MSB 位序复原)。x
为同时对 p,q
的二次非剩余(QNR+),可做一致性校验。(Random.py)
python#!/usr/bin/env python3 import ast, argparse def read_output(path='output.txt'): with open(path,'r') as f: lines=f.read().strip().splitlines() n=int(lines[0].split('=',1)[1]); x=int(lines[1].split('=',1)[1]) enc=ast.literal_eval(lines[2].split('=',1)[1]) return n,x,enc def legendre_mod_p(a,p): a%=p if a==0: return 0 return 1 if pow(a,(p-1)//2,p)==1 else -1 def bits_to_int_lsb(bits): v=0 for i,b in enumerate(bits): if b: v|=(1<<i) return v def main(): ap=argparse.ArgumentParser() ap.add_argument("--p",required=True,type=int) ap.add_argument("--q",required=True,type=int) args=ap.parse_args() p,q=(args.p,args.q) if args.p<args.q else (args.q,args.p) n,x,enc=read_output('output.txt') assert p*q==n, "p*q != n" assert legendre_mod_p(x,p)==-1 and legendre_mod_p(x,q)==-1, "x 不是同时对 p,q 的非剩余(QNR+)" bits=[] for c in enc: s=legendre_mod_p(c,p) bits.append(0 if s==1 else 1) # s=1→0位;s=-1→1位;s=0 极小概率按0 # 用 q 再校验一遍 for c,b in zip(enc,bits): s2=legendre_mod_p(c,q) if s2!=0 and ((b==0 and s2!=1) or (b==1 and s2!=-1)): raise SystemExit("与 q 的校验不一致,请检查因子/数据") m=bits_to_int_lsb(bits) pt = m.to_bytes((m.bit_length()+7)//8,'big') or b"\x00" try: print(pt.decode()) except UnicodeDecodeError: print(pt) if __name__=="__main__": main()
运行:
bashpython3 gm_decrypt.py --p <你的p> --q <你的q>
(n,x,enc
会自动从 output.txt
读取。(output.txt))
在 GM 中,x
被特别选为Jacobi 符号为 +1 的二次非剩余(QNR+)。这样无论密文是 y^2
还是 y^2·x
,对模 n
的 Jacobi 符号都为 +1,第三方无法仅用 Jacobi 符号区分 0/1;只有掌握因子 p,q
才能在模素数域里用勒让德符号一眼判位。你的题面就是这样构造的,因此不分解就无从解密。(Random.py) (CSA - IISc Bangalore)
--workdir
跑,或用 yafu 的 nfs(N)
直接上 GNFS;p,q
后,运行上面的 gm_decrypt.py
;p,q
发我,我可以直接回你明文;或者你贴一段 CADO/yafu 的“找到因子”日志,我也能帮你核验。参考:CADO 的 c155 参数与实现介绍、以及 155-digit 的实际用时案例可作对照。(GitLab)
(附:再次确认题面与数据来源:加密流程与 LSB→MSB 位序在 Random.py
,公参与密文序列在 output.txt
。(Random.py) (output.txt) 你之前与另一 AI 的 md 讨论整体方向正确,分歧只在分解的正确工具链与运行方式上。(cursor_.md))