CWE-1293: Missing Source Correlation of Multiple Independent Data

What is Missing Source Correlation of Multiple Independent Data?

• Overview: Missing Source Correlation of Multiple Independent Data (CWE-1293) refers to a vulnerability where a product relies on a single data source, lacking the ability to verify its integrity against other independent sources. This makes it difficult to detect if the data source has been tampered with or compromised.

• Exploitation Methods:

• Attackers can exploit this vulnerability by compromising the single trusted data source, altering the data it provides without detection.
• Common attack patterns include data injection, data manipulation, and man-in-the-middle attacks where the attacker controls or alters the data source.

• Security Impact:

• Direct consequences include the system accepting false or malicious data as legitimate, leading to incorrect operations or decisions based on compromised data.
• Potential cascading effects involve systemic failures where dependent systems or processes also rely on the compromised data, amplifying the impact.
• Business impact could encompass financial losses, reputational damage, and legal consequences from relying on inaccurate or manipulated data.

• Prevention Guidelines:

• Specific code-level fixes include implementing checks that compare data from multiple independent sources before trusting the information.
• Security best practices involve designing systems to query and verify data integrity across multiple sources, ensuring no single point of failure.
• Recommended tools and frameworks include those that facilitate data integrity verification, such as checksum utilities, cryptographic signatures, and redundancy checks in distributed systems.

Corgea can automatically detect and fix Missing Source Correlation of Multiple Independent Data in your codebase. [Try Corgea free today](https://corgea.app).

Technical Details

Likelihood of Exploit: Not specified

Affected Languages: Not Language-Specific

Affected Technologies: Not Technology-Specific

Vulnerable Code Example

import requests

def fetch_data_from_source():
    # Fetch data from a single source, which is vulnerable to compromise.
    response = requests.get('https://trusted-source.com/data')
    if response.status_code == 200:
        return response.json()
    else:
        raise Exception("Failed to fetch data")

def process_data():
    data = fetch_data_from_source()
    # Directly process the data without any verification
    print("Data processed:", data)

Explanation:

• Line 8: The code fetches data from a single, potentially compromised source. If this source is compromised, the data can be manipulated.
• Line 12: The data is directly processed without any verification, making the application susceptible to attacks if the source is compromised.

This approach is vulnerable because it relies solely on a single source for critical data, which can be easily manipulated if the source is compromised.

How to fix Missing Source Correlation of Multiple Independent Data?

To mitigate this issue, data should be fetched from multiple independent sources and cross-verified to ensure integrity and reliability. This reduces the risk of manipulation, as an attacker would need to compromise multiple sources simultaneously.

Steps to Implement the Fix:

1. Fetch Data from Multiple Sources: Use at least two independent data sources.
2. Cross-Verify Data: Implement logic to compare and validate the consistency of data across these sources.
3. Handle Discrepancies: Decide on a resolution strategy if data discrepancies are found.

Fixed Code Example

import requests

def fetch_data_from_sources():
    # Fetch data from multiple sources
    source1 = requests.get('https://trusted-source.com/data')
    source2 = requests.get('https://backup-source.com/data')

    if source1.status_code == 200 and source2.status_code == 200:
        data1 = source1.json()
        data2 = source2.json()
        
        # Verify data consistency between sources
        if data1 == data2:
            return data1
        else:
            raise Exception("Data discrepancy detected between sources")
    else:
        raise Exception("Failed to fetch data from one or more sources")

def process_data():
    data = fetch_data_from_sources()
    # Process the data only after verification
    print("Data processed:", data)

Explanation:

• Lines 8-9: Fetch data from two independent sources to ensure redundancy.
• Lines 10-12: Compare the fetched data from both sources to ensure consistency and reliability.
• Line 13: Raise an exception if discrepancies are detected, preventing the use of potentially compromised data.
• Line 19: Process the data only after successful verification, ensuring that only consistent and verified data is used.

This approach enhances security by ensuring that data processing is based on consistent and verified information, mitigating the risk associated with relying on a single data source.