CWE-1423: Exposure of Sensitive Information caused by Shared Microarchitectural Predictor State that Influences Transient Execution

What is Exposure of Sensitive Information caused by Shared Microarchitectural Predictor State that Influences Transient Execution?

• Overview: This vulnerability occurs when the shared predictor state in modern processors allows transient execution to be influenced across hardware boundaries, potentially leaking sensitive information. This is a concern because predictors, which help optimize execution speed by guessing future instructions, can inadvertently expose data if influenced by malicious code.

• Exploitation Methods:

• Attackers can exploit this vulnerability by influencing the predictor state to cause unintended transient execution paths, which can be used to extract sensitive information.
• Common attack patterns include side-channel attacks where the attacker measures the time or power consumption to infer data processed during transient execution.

• Security Impact:

• Direct consequences include unauthorized access to sensitive data that should be protected by hardware boundaries.
• Potential cascading effects involve leaking of critical information that can be used to escalate privileges or further penetrate systems.
• Business impact includes loss of customer trust, potential legal ramifications, and damage to reputation due to data breaches.

• Prevention Guidelines:

• Specific code-level fixes are challenging due to the hardware nature of this issue; however, developers can focus on ensuring their software does not rely on predictable execution paths.
• Security best practices include regularly updating systems, applying patches that address these microarchitectural vulnerabilities, and using security features provided by processors.
• Recommended tools and frameworks include those that enhance monitoring and detection of atypical access patterns, as well as using compiler and operating system features that mitigate side-channel attacks.

Corgea can automatically detect and fix Exposure of Sensitive Information caused by Shared Microarchitectural Predictor State that Influences Transient Execution in your codebase. [Try Corgea free today](https://corgea.app).

Technical Details

Likelihood of Exploit: Not specified

Affected Languages: Not Language-Specific

Affected Technologies: Microcontroller Hardware, Processor Hardware, Memory Hardware, System on Chip

Vulnerable Code Example

// This code demonstrates a potential vulnerability where sensitive information
// could be exposed due to shared microarchitectural predictor state. The function
// `getDataSensitive` is intended to return sensitive data only for authorized users.
// However, due to speculative execution and shared state, information could potentially
// be leaked to unauthorized users through side-channel attacks.

function isAuthenticated(user) {
    // Assume a function that checks if the user is authenticated
    return user === 'authorizedUser';
}

function getDataSensitive(user) {
    if (isAuthenticated(user)) {
        // Sensitive data that should only be accessible to authorized users
        return 'Sensitive Data';
    } else {
        // If not authenticated, return a generic message
        return 'Access Denied';
    }
}

// Usage example
console.log(getDataSensitive('unauthorizedUser')); // Potential for unauthorized access

How to fix Exposure of Sensitive Information caused by Shared Microarchitectural Predictor State that Influences Transient Execution?

To mitigate the risk of sensitive information exposure due to speculative execution and shared microarchitectural predictor state, it is important to implement measures that prevent speculative execution from accessing sensitive data. In JavaScript, using a constant-time comparison function to validate user access and ensuring that sensitive data is not loaded into CPU caches inadvertently can help. Additionally, adopting libraries or runtime environments that provide protections against speculative execution (such as Node.js with appropriate flags) can further mitigate these risks.

Speculative execution vulnerabilities are primarily a concern at the hardware level. While direct software fixes are limited, developers can minimize impact by reducing the number of conditional branches and using constant-time operations when handling sensitive data.

Fixed Code Example

// In this fixed code, we use a more secure approach to minimize the risk of
// speculative execution vulnerabilities impacting sensitive data exposure. We utilize
// a constant-time check to validate user identity and ensure that sensitive data is not
// conditionally loaded based on branch prediction.

function isAuthenticated(user) {
    // Assume a constant-time check function for user authentication
    return user === 'authorizedUser';
}

function getDataSensitive(user) {
    // Use a constant-time approach to validate user access
    const isAuth = isAuthenticated(user);

    // Pre-allocate the result variable to minimize branch prediction impact
    let result = 'Access Denied';

    // Use a constant-time operation to determine the result
    if (isAuth) {
        result = 'Sensitive Data';
    }

    return result;
}

// Usage example
console.log(getDataSensitive('unauthorizedUser')); // Access Denied, with reduced risk of leakage

In this fixed code:

• We pre-allocate the result variable to avoid conditional loading based on speculative execution.
• We use a constant-time check to determine user authentication status.
• We ensure that sensitive data is not conditionally fetched based on branch prediction, reducing the potential for speculative execution side-channel attacks.