垃圾字符 (Junk Code) 注入如何干扰静态分析工具？设计一种高效的垃圾字符过滤算法。 - 智猿学院-前后端，数据库，人工智能，云计算等领域前沿技术讲座

各位观众老爷，大家好！我是今天的讲师，江湖人称“Bug终结者”。今天咱不聊风花雪月，直接上干货，聊聊静态分析工具的克星——垃圾字符注入，以及如何用算法把这些“牛皮癣”给铲干净。

开场白：静态分析工具的烦恼

静态分析工具，那可是程序猿的得力助手，能在代码运行前揪出潜在的Bug、安全漏洞，简直就是“代码界的X光机”。但是，再牛逼的X光机，也怕被乱七八糟的东西干扰，影响成像质量。这个“乱七八糟的东西”，就是我们今天要说的——垃圾字符。

垃圾字符注入是个啥？

简单来说，垃圾字符注入就是在代码中插入一些对程序逻辑没有影响，但是能迷惑静态分析工具的字符或者代码。这些字符就像代码里的“隐身衣”，让静态分析工具难以准确识别代码的真实意图，从而漏掉真正的Bug。

垃圾字符注入的花样

垃圾字符注入的手法多种多样，简直是“十八般武艺样样精通”。常见的有以下几种：

注释干扰： 在注释中加入大量无意义的字符，让静态分析工具花费大量时间解析注释，影响分析效率。
字符串拼接： 将字符串拆分成多个部分，然后用无意义的字符拼接起来，让静态分析工具难以识别字符串的真实内容。
条件分支混淆： 插入一些永远不会执行的条件分支，或者让条件分支的判断条件变得非常复杂，让静态分析工具难以判断代码的执行路径。
死代码插入： 插入一些永远不会被执行的代码块，让静态分析工具花费时间分析这些无用的代码。
等价代码替换： 使用等价但是更复杂的代码替换原有的简单代码，增加静态分析工具的分析难度。
控制流平坦化： 将代码的控制流打乱，让代码的执行顺序变得难以预测，增加静态分析工具的分析难度。
操作码替换： 将一些操作码替换成等价的操作码，增加静态分析工具的分析难度。例如 i = i + 1 变成 i += 1或者 i = i - (-1)。
伪造数据结构： 创建一些看起来像正常数据结构，但实际上是假的结构，让分析工具误判。
函数调用重定向： 将函数调用重定向到其他函数，迷惑分析工具。
Unicode/ASCII 混淆： 在字符串中使用Unicode字符和ASCII字符混淆，让分析工具难以识别字符串内容。

垃圾字符注入的危害

垃圾字符注入的危害可不小：

降低静态分析的准确性： 导致静态分析工具漏报、误报，让程序猿无法及时发现代码中的Bug。
增加静态分析的时间： 让静态分析工具花费更多的时间分析代码，降低开发效率。
增加代码的维护难度： 让代码变得难以阅读、理解，增加代码的维护成本。

静态分析工具为何如此“脆弱”？

静态分析工具虽然强大，但也不是万能的。它们在分析代码时，主要依赖于以下技术：

词法分析： 将代码分解成一个个的Token，例如关键字、变量名、运算符等。
语法分析： 根据编程语言的语法规则，将Token组成语法树。
语义分析： 分析代码的语义，例如变量的类型、函数的调用关系等。
数据流分析： 分析代码中数据的流动情况，例如变量的赋值、使用等。
控制流分析： 分析代码的执行路径，例如条件分支、循环等。

垃圾字符注入正是利用了静态分析工具的这些弱点，通过干扰词法分析、语法分析、语义分析等过程，让静态分析工具无法准确地理解代码的真实意图。

垃圾字符过滤算法的设计思路

要对抗垃圾字符注入，就必须设计一种高效的垃圾字符过滤算法，将这些“牛皮癣”从代码中剔除出去。算法的设计思路主要有以下几点：

识别垃圾字符的特征： 针对不同的垃圾字符注入手法，提取其特征。例如，对于注释干扰，可以识别大量的无意义字符；对于字符串拼接，可以识别被拆分的字符串。
建立垃圾字符的黑名单： 将常见的垃圾字符加入黑名单，例如一些无意义的Unicode字符、控制字符等。
利用机器学习技术： 训练机器学习模型，自动识别垃圾字符。例如，可以使用自然语言处理（NLP）技术，分析代码的语义，识别与代码逻辑无关的字符。
代码规范检查： 强制执行代码规范，例如限制注释的长度、禁止使用过于复杂的条件分支等。

高效的垃圾字符过滤算法：实例讲解

接下来，我们以一个具体的例子来说明如何设计高效的垃圾字符过滤算法。假设我们要过滤掉代码中的注释干扰。

算法描述：

预处理： 将代码中的注释提取出来。
特征提取： 提取注释的长度、字符熵、重复字符比例等特征。
判断： 如果注释的长度超过阈值，或者字符熵低于阈值，或者重复字符比例高于阈值，则认为该注释是垃圾字符。
处理： 将垃圾字符注释从代码中删除。

代码实现（Python）：

import re
import math

def calculate_entropy(text):
  """计算字符串的熵"""
  if not text:
    return 0
  entropy = 0
  frequencies = {}
  for char in text:
    if char in frequencies:
      frequencies[char] += 1
    else:
      frequencies[char] = 1
  for frequency in frequencies.values():
    probability = float(frequency) / len(text)
    entropy -= probability * math.log(probability, 2)
  return entropy

def filter_junk_comments(code, max_length=200, min_entropy=2.0, max_repeat_ratio=0.8):
  """过滤垃圾字符注释"""
  # 提取注释
  comments = re.findall(r'//.*|/*[sS]*?*/', code)
  filtered_code = code

  for comment in comments:
    # 特征提取
    length = len(comment)
    entropy = calculate_entropy(comment)
    repeat_ratio = 0
    if length > 0:
      max_repeat_char = max(set(comment), key=comment.count)
      repeat_ratio = float(comment.count(max_repeat_char)) / length

    # 判断
    if length > max_length or entropy < min_entropy or repeat_ratio > max_repeat_ratio:
      print(f"发现垃圾字符注释: {comment[:50]}...") #打印前50个字符
      # 处理
      filtered_code = filtered_code.replace(comment, "")

  return filtered_code

# 示例代码
code = """
int main() {
  int i = 0;
  // 这是一个正常的注释
  i++;
  /*
   * 这是一个多行注释
   */
  i++;
  // aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa
  i++;
  /*
   * asdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdf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idio.

输出结果：


 发现垃圾字符注释: aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa...
*/
   * asdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdf...

过滤后的代码：

int main() {
  int i = 0;
  // 这是一个正常的注释
  i++;
  /*
   * 这是一个多行注释
   */
  i++;

  i++;

  i++;
  return 0;
}

算法优化：

使用Bloom Filter： 可以使用Bloom Filter来快速判断一个字符串是否是垃圾字符。Bloom Filter是一种空间效率很高的概率型数据结构，可以用来检测一个元素是否在一个集合中。
结合其他特征： 除了长度、字符熵、重复字符比例外，还可以结合其他特征，例如词法特征、语法特征等，来提高判断的准确性。
使用多线程： 可以使用多线程来并行处理代码中的注释，提高过滤效率。

其他垃圾字符过滤策略

上面的例子只是针对注释的过滤。对于其他的垃圾字符注入方式，我们需要采取不同的策略。

对于字符串拼接： 可以使用动态污点分析技术来跟踪字符串的拼接过程，识别被拆分的字符串。
对于条件分支混淆： 可以使用符号执行技术来分析代码的执行路径，识别永远不会执行的条件分支。
对于死代码插入： 可以使用静态程序切片技术来识别与程序输出无关的代码，即死代码。
对于等价代码替换： 可以构建等价代码的模式库，识别并替换这些复杂的等价代码。
对于控制流平坦化： 可以使用控制流解平坦化技术，恢复代码的原始控制流。
操作码替换： 使用模式匹配，识别常见的操作码替换，然后还原。
对于伪造数据结构： 检查数据结构的使用方式和上下文，如果与预期不符，则可能是伪造的。
对于函数调用重定向： 检查函数调用的目标地址，如果目标地址不在预期的范围内，则可能是重定向。
对于Unicode/ASCII 混淆： 可以将字符串统一转换成ASCII或者Unicode，然后进行分析。

表格总结：常见垃圾字符注入方式及应对策略

垃圾字符注入方式	特征	应对策略
注释干扰	长度过长、字符熵低、重复字符比例高	基于长度、字符熵、重复字符比例的过滤算法
字符串拼接	字符串被拆分、拼接符无意义	动态污点分析、字符串重构
条件分支混淆	条件永远为真或假、条件过于复杂	符号执行、控制流分析
死代码插入	代码不影响程序输出	静态程序切片
等价代码替换	代码功能相同但更复杂	等价代码模式库、代码简化
控制流平坦化	控制流被打乱、大量跳转	控制流解平坦化技术
操作码替换	使用等价操作码代替原始操作码	模式匹配替换
伪造数据结构	数据结构使用方式异常	数据结构上下文分析
函数调用重定向	函数调用目标地址异常	函数调用链分析
Unicode/ASCII 混淆	字符串中混用Unicode和ASCII字符	字符串编码统一化

实战案例：某开源项目的垃圾字符过滤

我们曾经对一个开源项目进行过垃圾字符过滤，发现该项目存在大量的注释干扰。我们使用了上述的注释过滤算法，成功地将这些垃圾注释从代码中剔除出去，提高了静态分析的效率和准确性。

总结与展望

垃圾字符注入是一种常见的代码混淆技术，会对静态分析工具造成很大的干扰。设计高效的垃圾字符过滤算法是提高静态分析准确性和效率的关键。未来，随着代码混淆技术的不断发展，垃圾字符过滤算法也需要不断地更新和完善。

几点建议：

重视代码规范： 良好的代码规范是预防垃圾字符注入的有效手段。
使用多种分析工具： 结合使用静态分析、动态分析等多种分析工具，可以提高代码分析的准确性。
持续学习： 关注代码混淆技术的最新发展，及时更新和完善垃圾字符过滤算法。

好了，今天的讲座就到这里。希望大家有所收获，能够更好地应对垃圾字符注入的挑战。谢谢大家！

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