Secure Source of Randomness #12
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| random_sections = secrets.SystemRandom().sample(goodware_list, population_size) | ||
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| for fn in random_sections: | ||
| file_path = os.path.join(goodware_folder, fn) |
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The code is using os.listdir to list files in a directory and then selecting a random sample of these files. This approach does not handle the possibility of encountering directories within goodware_folder, which would cause an error when trying to read them as files later on. To prevent this, the code should be modified to filter out directories before sampling.
| # pick two parents randomly | ||
| p1 = random.choice(old_gen) | ||
| p2 = random.choice(old_gen) | ||
| p1 = secrets.SystemRandom().choice(old_gen) | ||
| p2 = secrets.SystemRandom().choice(old_gen) | ||
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| j = random.randrange(len(p1[1])) | ||
| j = secrets.SystemRandom().randrange(len(p1[1])) | ||
| child = p1[0][:j] + p2[0][j:] | ||
| offsprings.append((child, p1[1])) |
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The code snippet shows a genetic algorithm's crossover operation without any checks to ensure that p1 and p2 are distinct individuals. If p1 and p2 are the same, the crossover operation would be pointless as it would produce an offspring identical to the parent. To improve the genetic diversity of the offspring, the code should be modified to ensure that p1 and p2 are different before performing the crossover.
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| new_section = lief.PE.Section( | ||
| ''.join(random.choice(string.ascii_lowercase) for i in range(5))) | ||
| ''.join(secrets.SystemRandom().choice(string.ascii_lowercase) for i in range(5))) | ||
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| new_section.content = content | ||
| new_section.characteristics = lief.PE.SECTION_CHARACTERISTICS.MEM_DISCARDABLE |
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The new_section is being created with a randomly generated name, but there is no check to ensure that the generated name is unique within the binary. This could lead to a collision if a section with the same name already exists, potentially causing undefined behavior or overwriting existing section data. To mitigate this, implement a check to ensure the generated section name is unique within the binary before adding it.
| @@ -54,7 +54,7 @@ def section_injection(exe_path, amount): | |||
| exe_object: lief.PE.Binary = lief.parse(exe_path) | |||
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| new_section = lief.PE.Section( | |||
| ''.join(random.choice(string.ascii_lowercase) for i in range(5))) | |||
| ''.join(secrets.SystemRandom().choice(string.ascii_lowercase) for i in range(5))) | |||
| new_section.content = [ord(os.urandom(1)) for _ in range(amount)] | |||
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The line is generating content for a new section by calling os.urandom(1) for each byte, which is inefficient. This approach generates a separate system call for each byte, which can be slow if a large amount of data is needed. Instead, call os.urandom once with the total number of bytes required for the section content to improve performance.
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Description
This pull request makes changes to the code in two files:
genetic_optimizer.pyandsection_injection.py. The changes involve replacing the usage of therandommodule with thesecretsmodule for increased security.Here are the specific changes made:
In
genetic_optimizer.py:random.sample()withsecrets.SystemRandom().sample()to generate random sections from a list of goodware samples.random.choice()withsecrets.SystemRandom().choice()to randomly select parents for crossover operation.random.randrange()withsecrets.SystemRandom().randrange()to randomly select a crossover index.random.random()withsecrets.SystemRandom().random()to determine mutation probability.In
section_injection.py:random.choice()withsecrets.SystemRandom().choice()to generate a random lowercase string for the name of a new section.These changes are made to enhance the security and randomness of the genetic optimization and section injection processes.