CWE-202: Exposure of Sensitive Information Through Data Queries

What is Exposure of Sensitive Information Through Data Queries?

• Overview: Exposure of Sensitive Information Through Data Queries (CWE-202) refers to a vulnerability where attackers derive confidential information by analyzing statistical data queries. Even if data should be anonymized, specific queries can inadvertently reveal user identities or sensitive details.

• Exploitation Methods:

• Attackers can exploit this vulnerability by crafting queries with parameters known to be associated with a specific user or small group.
• Common attack patterns include issuing multiple queries to detect patterns or changes in results that hint at underlying sensitive information.

• Security Impact:

• Direct consequences include unauthorized access to confidential data and potential privacy violations.
• Potential cascading effects involve leakage of additional sensitive information through further data analysis, leading to more significant breaches.
• Business impact includes loss of customer trust, regulatory penalties, and damage to brand reputation.

• Prevention Guidelines:

• Implement specific code-level fixes such as query logging and monitoring to detect unusual patterns that might indicate information harvesting.
• Follow security best practices by ensuring data anonymization techniques are robust and cannot be easily circumvented by combining data points.
• Use recommended tools and frameworks for data privacy, such as differential privacy techniques, to minimize the risk of sensitive information exposure.

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Technical Details

Likelihood of Exploit: Medium

Affected Languages: Not Language-Specific

Affected Technologies: Not specified

Vulnerable Code Example

const mysql = require('mysql');

function getSensitiveStatistics(connection) {
    // Vulnerable query: Directly exposes sensitive statistical data
    // An attacker can infer sensitive information by executing queries
    connection.query("SELECT AVG(salary), MIN(salary), MAX(salary) FROM employees WHERE department='HR'", 
    function (error, results, fields) {
        if (error) throw error;
        console.log('Salary Statistics:', results);
    });
}

Explanation:

• Vulnerability: The code directly queries sensitive data (salary statistics) without any access control, allowing any user with access to this function to retrieve sensitive information. This can lead to exposure of sensitive information through inference attacks.
• Security Risk: Unauthorized users can gain insights into salary data, which could be exploited for malicious purposes.

How to fix Exposure of Sensitive Information Through Data Queries?

To mitigate the exposure of sensitive information, implement role-based access control to ensure that only authorized users can retrieve statistical data. Additionally, consider using data masking or differential privacy techniques to obscure the data further and protect against inference attacks.

Fixed Code Example

const mysql = require('mysql');

function getSensitiveStatistics(userRole, connection) {
    // Implement access control to protect sensitive data
    if (userRole === 'manager') {
        // Authorized users can retrieve the data
        connection.query("SELECT AVG(salary), MIN(salary), MAX(salary) FROM employees WHERE department='HR'", 
        function (error, results, fields) {
            if (error) throw error;
            console.log('Salary Statistics:', results);
        });
    } else {
        // Log unauthorized access attempt and return a message
        console.log('Access Denied: Insufficient permissions to view salary statistics.');
    }
}

Explanation:

• Fix: The function now includes a role check (userRole === 'manager') to ensure only users with the 'manager' role can access the sensitive salary statistics.
• Security Enhancement: By implementing role-based access control, the risk of unauthorized data access is reduced. This ensures that sensitive data is only accessible to users with the appropriate permissions.
• Additional Measures: Consider further enhancing security by implementing data anonymization techniques, such as data masking or adding noise to the data, to protect against inference attacks even for authorized users.