CWE-1038: Insecure Automated Optimizations

What is Insecure Automated Optimizations?

• Overview: Insecure Automated Optimizations occur when a product uses a mechanism to automatically improve code characteristics, like performance, but these optimizations unintentionally compromise security assumptions.

• Exploitation Methods:

• Attackers can exploit this vulnerability by identifying and leveraging the unintended side effects of the optimizations.
• Common attack patterns include side-channel attacks, where optimizations expose information that can be used to deduce sensitive data or application behavior.

• Security Impact:

• Direct consequences include unauthorized access to sensitive information or system resources.
• Potential cascading effects could lead to broader system compromises or data breaches.
• Business impact may involve loss of customer trust, legal ramifications, and financial losses due to data breaches.

• Prevention Guidelines:

• Specific code-level fixes involve careful analysis and testing of optimization changes to ensure they do not introduce security vulnerabilities.
• Security best practices include incorporating security reviews in the optimization process and maintaining a balance between performance and security.
• Recommended tools and frameworks include static and dynamic analysis tools that can help identify unintended security consequences of optimizations.

Corgea can automatically detect and fix Insecure Automated Optimizations in your codebase. [Try Corgea free today](https://corgea.app).

Technical Details

Likelihood of Exploit: Low

Affected Languages: Not Language-Specific

Affected Technologies: Not specified

Vulnerable Code Example

Python Example

import math

def calculate_area(radius):
    # The function uses a naive optimization by converting the radius to an integer.
    # This can lead to losing precision and create unexpected results for small radius values.
    optimized_radius = int(radius)  # This conversion causes precision loss
    return math.pi * optimized_radius * optimized_radius

# Example usage
radius = 2.5
print("Area:", calculate_area(radius))  # Outputs incorrect area due to precision loss

Explanation

In this vulnerable code example, the function calculate_area attempts to optimize by converting the radius from a floating-point number to an integer. This leads to significant precision loss, especially for non-integer radius values, resulting in incorrect area calculations. Such precision loss can be critical in applications where exact measurements are crucial, such as in scientific computations or financial applications.

How to fix Insecure Automated Optimizations?

To address the issue of insecure automated optimizations, follow these guidelines:

• Avoid unnecessary type conversions that could lead to precision loss.
• Ensure any optimization preserves the intended accuracy and behavior of the program.
• Use appropriate data types and functions that maintain precision, especially for calculations involving floating-point numbers.

Fixed Code Example

import math

def calculate_area(radius):
    # Maintain the radius as a float to ensure precision is not lost.
    # This ensures the calculation retains its precision and correctness.
    return math.pi * radius * radius

# Example usage
radius = 2.5
print("Area:", calculate_area(radius))  # Correctly calculates area with full precision

Explanation

In the fixed version, the unnecessary conversion to an integer is removed. The function now computes the area using the full precision of the floating-point radius, preserving the integrity and correctness of the calculation. This example highlights the importance of maintaining precision in calculations, especially when dealing with floating-point numbers, to prevent unintended inaccuracies.