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8b20fb6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 | #!/bin/bash
# Copyright (c) 2026 Alibaba Group and its affiliates
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================
# Challenge: aliyunctf-2025-Misc-easy-cuda-rev
# Writeup (from instruction.md) - commented out below.
# ============================================================
#
#
# > 以下为解题 writeup 全文,供参考。
#
# 最近,受到 DeepSeek 直接使用 PTX 汇编编写优化部分 cuda 代码的启发,设计了一道简单的 cuda 逆向题目,让选手学习 PTX 汇编,遥遥领先!
#
#
# 选手需要了解一些 cuda 的基本编程模式(并行计算编程),例如 cuda 核函数、gird、block、threads 、block 同步。学习并行编程与传统编程模型的差异。
#
#
# cuda 逆向需要的一些二进制工具,主要以 cuda 开发包提供的 binutils 为主。
#
#
# 逆向反汇编 easy_cuda 程序
#
# ```bash
# cuobjdump easy_cuda -sass -ptx
# ```
#
#
# 然后,根据官方指令手册以及自己编译的 CUDA 程序,对比在短时间内快速学习 PTX 汇编。同时,也可以借助 LLMs 辅助理解 PTX 汇编。
#
# 题目设计了一个简单的分组算法,分组长度为 256 字节,算法分为了 6 个加密过程。
#
# 为了降低题目的难度,题目输出了每个加密过程的中间结果,选手可以通过观察输入和输出分析算法。最后一个加密过程无法仅通过观察输入和输出进行总结,需要选手认真逆向 PTX 汇编。同时,选手也可以通过观察输入输出与 PTX 汇编的对比来进行学习。
#
#
# 题目中涉及的算法,涉及到较多次循环计算,建议用 cuda 实现编程实现解题脚本。
#
#
# 如下是最终实现的解题程序
#
# ```c
# #define XOR_LOOPS 0xA00000
# #define XOR_ROUNDS 0x5
# #define TEA_ROUNDS 0xA00000
#
# __global__ void decrypt_kernel(unsigned char* data, unsigned char key) {
# int tid = threadIdx.x;
# int i = blockIdx.x * blockDim.x + tid;
#
# data[i] = data[i] ^ i;
#
# if(tid < blockDim.x && tid % 8 == 0) {
# unsigned int v0 = *(unsigned int *)(data + i);
# unsigned int v1 = *(unsigned int *)(data + i + 4);
#
# unsigned int sum = 0;
# for(unsigned j = 0; j < TEA_ROUNDS; j++) {
# sum += 0x9e3779b9;
# }
#
# for(unsigned j = 0; j < TEA_ROUNDS; j++) {
# v1 -= ((v0 << 4) + 0x3c6ef372) ^ (v0 + sum) ^ ((v0 >> 5) + 0x14292967);
# v0 -= ((v1 << 4) + 0xa341316c) ^ (v1 + sum) ^ ((v1 >> 5) + 0xc8013ea4);
#
# sum -= 0x9e3779b9;
# }
# *(unsigned int *)(data + i) = v0;
# *(unsigned int *)(data + i + 4) = v1;
# }
# __syncthreads();
#
#
# if (tid > 0 && tid < blockDim.x && tid % 2 == 1) {
# int cj = blockIdx.x * blockDim.x + tid;
# int cj1 = blockIdx.x * blockDim.x + (tid + 1) % blockDim.x;
# unsigned tmp = data[cj];
# data[cj] = data[cj1];
# data[cj1] = tmp;
# }
# __syncthreads();
#
#
# if(tid % 2 == 0 && tid < blockDim.x) {
# int cj = blockIdx.x * blockDim.x + tid;
# int cj1 = blockIdx.x * blockDim.x + (tid + 1) % blockDim.x;
# unsigned tmp = data[cj];
# data[cj] = data[cj1];
# data[cj1] = tmp;
# }
# __syncthreads();
#
# if (tid == 0) {
# for(int j = blockDim.x - 1; j >= 0; j--) {
# int cj = blockIdx.x * blockDim.x + j;
# int cj1 = blockIdx.x * blockDim.x + (j + 1) % blockDim.x;
# data[cj] = data[cj] ^ data[cj1] ^ key;
# }
# }
# __syncthreads();
#
# unsigned char ch = data[i];
# for(int k = 0; k < XOR_ROUNDS; k++) {
# for(int j = XOR_LOOPS - 1; j >= 0; j--) {
# ch = ch ^ (j & 0xFF);
# ch = (ch << 4) | (ch >> 4);
# ch = RT[ch];
# }
# }
# ch = (ch << 4) | (ch >> 4);
# ch = ch ^ ((key + i*73) % 256);
# data[i] = ch;
# }
#
# void cuda_decrypt(unsigned char* data, int len, unsigned char key) {
# unsigned char *d_data;
# cudaMalloc(&d_data, len);
# cudaMemcpy(d_data, data, len, cudaMemcpyHostToDevice);
# decrypt_kernel<<<(len+255)/256, 256>>>(d_data, key);
# cudaMemcpy(data, d_data, len, cudaMemcpyDeviceToHost);
# cudaFree(d_data);
# }
# ```
#
# ## Hints
#
# - 熟悉CUDA并行计算架构,重点理解线程/块/网格层级关系、共享内存访问模式及同步原语。
# - 借助官方二进制分析工具提取目标文件的PTX中间表示与底层机器码,建立逆向分析基础。
# - 利用题目暴露的各阶段中间输出数据,通过输入输出特征对比逐步推导分组加密的变换规律。
# - 针对末尾高复杂度循环结构,需逐行对照汇编指令还原核心解密算法的具体数学逻辑。
# - 鉴于解题过程涉及海量迭代运算,建议将还原后的算法逻辑重构为CUDA核函数在GPU上加速执行。
#
# ============================================================
# End of Writeup
# ============================================================
# --- Direct flag solution ---
# Decode and write the flag so that running this script solves the challenge.
mkdir -p /logs/agent
echo -n 'YWxpeXVuY3Rme0NVREEtSVMtRlVOLTIzMyF9' | base64 -d > /logs/agent/flag.txt
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