CWE-407: Inefficient Algorithmic Complexity

What is Inefficient Algorithmic Complexity?

• Overview: An inefficient algorithmic complexity vulnerability occurs when an algorithm's worst-case performance is poor, leading to significant slowdowns or resource exhaustion. This can be exploited by attackers who craft inputs that trigger these worst-case scenarios, potentially degrading system performance.

• Exploitation Methods:

• Attackers can exploit this vulnerability by sending specially crafted inputs that force the algorithm to operate at its worst-case complexity.
• Common attack patterns include using large or specially structured data sets that are known to cause the algorithm to slow down significantly.

• Security Impact:

• Direct consequences of successful exploitation include degraded system performance, increased response times, and potential denial of service (DoS).
• Potential cascading effects can impact other systems or components due to resource exhaustion or bottlenecks.
• Business impact includes loss of service availability, reduced user satisfaction, and potential financial losses due to downtime or decreased efficiency.

• Prevention Guidelines:

• Specific code-level fixes include optimizing algorithms to have better worst-case performance and using data structures that provide efficient operation across a range of inputs.
• Security best practices involve conducting thorough performance testing with a wide variety of input scenarios to identify and address inefficiencies.
• Recommended tools and frameworks include using profiling tools to identify performance bottlenecks and employing libraries with well-tested, efficient algorithms.

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Technical Details

Likelihood of Exploit: Low

Affected Languages: Not Language-Specific

Affected Technologies: Not specified

Vulnerable Code Example

def inefficient_search(data, target):
    # This is a naive search algorithm with O(n^3) complexity
    # It checks each substring of 'data' to see if it matches 'target'
    for i in range(len(data)):  # Iterate over each starting point
        for j in range(i + 1, len(data) + 1):  # Iterate over each ending point
            if data[i:j] == target:  # Compare substring with target
                return True
    return False

data = "a" * 10000  # A large input string
target = "a" * 9999 + "b"  # A crafted target to reach worst-case complexity
inefficient_search(data, target)

Explanation of Vulnerability

The vulnerability in the code arises from the inefficient O(n^3) complexity of the naive substring search algorithm. This inefficiency can be exploited by an attacker by providing large inputs that trigger the worst-case scenario, leading to performance degradation or denial of service. The algorithm checks every possible substring, resulting in unnecessary and excessive computations.

How to fix Inefficient Algorithmic Complexity?

Fixed Code Example

def kmp_search(data, target):
    # This function implements the KMP algorithm for efficient substring searching
    def build_lps(target):
        # Build the longest prefix suffix (LPS) array
        lps = [0] * len(target)
        length = 0
        i = 1
        while i < len(target):
            if target[i] == target[length]:
                length += 1
                lps[i] = length
                i += 1
            else:
                if length != 0:
                    length = lps[length - 1]
                else:
                    lps[i] = 0
                    i += 1
        return lps

    lps = build_lps(target)
    i = 0  # index for data
    j = 0  # index for target
    while i < len(data):
        if target[j] == data[i]:
            i += 1
            j += 1
        if j == len(target):
            return True
        elif i < len(data) and target[j] != data[i]:
            if j != 0:
                j = lps[j - 1]
            else:
                i += 1
    return False

data = "a" * 10000
target = "a" * 9999 + "b"
kmp_search(data, target)  # Use the efficient KMP search algorithm

Explanation of Fix

In the fixed code, the kmp_search function uses the Knuth-Morris-Pratt algorithm, which significantly improves the efficiency of substring searching. The KMP algorithm preprocesses the target string to create an LPS (longest prefix suffix) array, which allows it to skip unnecessary comparisons, ensuring the search runs in O(n + m) time relative to the size of the input and target. This ensures that even in the worst-case scenario, the algorithm remains efficient and prevents performance degradation due to large inputs.