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...

Erstellt am: 11. Oktober 2025

Beantwortet mit GPT-5 Thinking von Chat01

Frage

cursor_.md
output.txt
Random.py

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

Gedanke

Exploring an approach for factorization

I’m considering the idea of testing the difference between p and q using Fermat's method, since their primes might not be specially structured. Using getPrime(256) yields random primes, but I wonder if their difference might just happen to be small enough for Fermat's method to work. However, they aren’t likely to be small enough due to their size. Still, I'm thinking through every possibility, like how they compute powers and updates.

Considering y's behavior in mod p

If y is a multiple of p, then y^2 mod p would be 0 and the ciphertext might follow a similar pattern, but that chance is minuscule due to how small p is. As y increases by small amounts, it cycles. The probability of a collision is incredibly small, so we can pretty much ignore this. However, I'm wondering if we can leverage differences in ciphertext using GCD.

Exploring Legendre symbols in ciphertexts

I’m thinking of trying to compute ri=(ci+1×ci1)modnr_i = (c_{i+1} \times c_i^{-1}) \mod n, using the difference between ciphertexts. But, this leads to complications, with Legendre symbols and modular arithmetic. Eventually, it seems like the Legendre symbol of the ratio will always simplify to 1, making things more predictable than I expected. Interesting!

Exploring XOR with Legendre and Jacobi symbols

I’m looking into how the Legendre symbol of rir_i relates to the difference of bits bi+1bib_{i+1} - b_i. It seems the difference gives an XOR relation, which ties directly to the parity of the bits. A cool realization: this implies we can use the Jacobi symbol for certain calculations, bypassing some complexities.

Thought for 2m 22s

Antwort

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:

  • 题目在 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 nLSB→MSB逐位加密(bit = msg & 1; msg >>= 1)。(Random.py)
  • 输出文件 output.txt 给出了 n, x, enc 的全部数据,你只差 p, q。(output.txt)
  • 这正是 Goldwasser–Micali(GM)按位加密。分解出 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)

一、解题脚本(不做大数分解,专注“解密”本身)

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:

  1. 先用外部工具分解 n(见下文第二部分),得到 pq
  2. 回到 output.txt 所在目录,执行:
bash
python3 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

二、因数分解(只做一次):推荐 CADO-NFS / yafu

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。

路线 A(速度/稳定性最佳):WSL2 + Ubuntu + CADO-NFS

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:

bash
sudo 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
  • 结束后终端会直接打印 pq;也会保存在 work_random/ 下,支持断点续跑(下次用同一 --workdir)。
  • 取到 p,q 后按上节命令运行 solve_gm.py 即可。

路线 B(Windows 原生最省事):yafu

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
  • 终端与 factor.log 均会给出 p,q。然后运行:
powershell
python3 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。


三、关键点与避坑

  • 位序是 LSB→MSB:加密时 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)
  • 判位依据:分解后,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)
  • 题型判定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)
  • 若输出不是可见字符串,脚本会直接打印 bytes;你也可用 --out 把原始字节写到文件再自行查看编码。

四、题面引用(供核对)

  • 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)
  • 你与另一 AI 在 md 中的讨论要点与现有思路(GM + 先分解 n)总体方向正确,但执行路径要换成 NFS 工具 + 外部分解。(cursor_.md)

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。

Frage

(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

===============================================================
======= Welcome to YAFU (Yet Another Factoring Utility) =======
======= [email protected] =======
======= Type help at any time, or quit to quit =======

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

Gedanke

Guidance on speeding up NFS

Even though we didn’t find the final factors yet, we’ve confirmed that the direction is correct and CADO is working. To speed things up, try using --workdir to spawn more clients, ensure GMP-ECM is installed, and adjust threads for sieving and polyselect. You could also explore using a preselected polynomial or, if necessary, skip certain optimization steps.

Tip for speeding up NFS

Instead of running ECM with YAFU, try using the nfs() command directly, like: "yafu-x64 nfs(<N>) -threads 8". This skips ECM pretesting, which can be faster for 512-bit numbers. You can also set "pretest_ratio=0.0" to skip ECM entirely. Additionally, for CADO-NFS, skipping the "size-optimized" stage using polyselect.adrange and polyselect.P can help as well.

Thought for 2m 32s

Antwort

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:


你的两次尝试发生了什么

  • CADO-NFS 日志:已进入 polyselect 阶段(挑多项式),这是 GNFS 的第一步;它会显示很多 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)
  • yafu 日志:你在 ECM 预筛(pretesting)还没做完时手动 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)


正确、高效的操作方式

A. 用 CADO-NFS(推荐;支持断点续跑与多客户端)

  1. 用持久 workdir 重开(你这次跑在 /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。
  • 显存/内存紧张就把 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)
  1. 多客户端并行(可显著提速):日志里已给出了 API 地址与证书指纹;在同机/其他机上多开客户端:
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 pq,并保存在 workdir 的日志里。

B. 用 yafu(Windows 最省事)

  • 直接跳 GNFS,避免 ECM 预筛拖时
powershell
.\yafu-x64.exe "nfs(7494062703769887703668081866565274579333132167014632821313548612356114287792191446305040778987677683423969025588609780586722302041792065732461222120206217)" -threads 8 -v
  • 或者继续用 factor(N),但不要中途 Abort;它会在 ECM 预筛达到目标后自动转入 NFS。

分解出 p,q 之后:一键解密脚本

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:

bash
python3 gm_decrypt.py --p <你的p> --q <你的q>

n,x,enc 会自动从 output.txt 读取。(output.txt))


关键辨析(为什么不能“免分解”)

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 nJacobi 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)


小结(照做即可)

  1. CADO-NFS--workdir 跑,或用 yafu 的 nfs(N) 直接上 GNFS;
  2. 拿到 p,q 后,运行上面的 gm_decrypt.py
  3. 如果愿意把 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运行方式上。(cursor_.md))

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