CWE-409: Improper Handling of Highly Compressed Data (Data Amplification)

What is Improper Handling of Highly Compressed Data (Data Amplification)?

• Overview: This vulnerability occurs when a software application does not properly manage or handle compressed data with a high compression ratio, leading to a significant increase in data size upon decompression, such as with a "decompression bomb."

• Exploitation Methods:

• Attackers can exploit this vulnerability by providing a highly compressed file that expands dramatically when decompressed, potentially consuming excessive system resources.
• Common attack patterns include sending malicious compressed files that trigger resource exhaustion when unpacked, disrupting system operations.

• Security Impact:

• Direct consequences include excessive memory and processing resource consumption, which can lead to system slowdowns or crashes.
• Potential cascading effects include denial of service (DoS), where legitimate users are unable to access system resources.
• Business impact could involve downtime, loss of availability for critical services, and potential reputational damage.

• Prevention Guidelines:

• Specific code-level fixes include implementing limits on resource usage and file size during decompression processes.
• Security best practices involve validating and scanning compressed files before decompression, and employing threshold checks on decompressed data size.
• Recommended tools and frameworks include using libraries that offer built-in protections against decompression bombs, and regularly updating them to incorporate security patches.

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Technical Details

Likelihood of Exploit: Not specified

Affected Languages: Not Language-Specific

Affected Technologies: Not specified

Vulnerable Code Example

const zlib = require('zlib');

function decompressData(compressedData) {
    // Vulnerable code: Directly decompressing data without size checks
    // This can lead to a data amplification attack, where the decompressed
    // output is much larger than the input, exhausting system resources.
    return zlib.inflateSync(compressedData);
}

How to fix Improper Handling of Highly Compressed Data (Data Amplification)?

To address this vulnerability in JavaScript, you should implement a mechanism to monitor and limit the size of the decompressed data. By setting a maximum size limit for the decompressed data, you can prevent excessive memory usage that could lead to Denial of Service (DoS) attacks. Implement chunk-based decompression and check the output size incrementally to ensure it does not exceed the specified limit.

Fixed Code Example

const zlib = require('zlib');

function decompressData(compressedData, maxDecompressedSize = 1000000) {
    let decompressedData = Buffer.alloc(0);
    
    // Create a decompression stream
    const inflater = zlib.createInflate();
    
    inflater.on('data', (chunk) => {
        decompressedData = Buffer.concat([decompressedData, chunk]);
        
        // Check if decompressed data exceeds the allowed size
        if (decompressedData.length > maxDecompressedSize) {
            inflater.emit('error', new Error('Decompressed data exceeds maximum allowed size.'));
        }
    });

    inflater.on('error', (err) => {
        // Handle error appropriately, potentially logging or notifying
        throw err;
    });

    inflater.write(compressedData);
    inflater.end();
    
    return decompressedData;
}

Explanation

In the vulnerable code example, the function decompressData directly decompresses input data without any checks on the resulting size. This can lead to a data amplification attack, where a small amount of compressed data decompresses into a very large output, potentially exhausting system memory and causing a Denial of Service (DoS).

In the fixed code example, the function decompressData uses a streaming approach to handle decompression. It processes the data in chunks and checks the size of the decompressed data incrementally. If the size exceeds a predefined limit (maxDecompressedSize), an error is emitted, preventing further processing. This approach helps mitigate the risk of resource exhaustion by ensuring that decompressed data does not grow beyond safe limits.