CWE-1246: Improper Write Handling in Limited-write Non-Volatile Memories

What is Improper Write Handling in Limited-write Non-Volatile Memories?

• Overview: This vulnerability occurs when a product fails to correctly implement wear leveling in non-volatile memories like NAND Flash or EEPROM, leading to uneven wear and potential failure of memory cells due to excessive write cycles.

• Exploitation Methods:

• Attackers can exploit this by deliberately writing to the same physical memory location repeatedly.
• Common attack patterns include targeting specific memory blocks to cause premature wear and failure.

• Security Impact:

• Direct consequences include the storage device becoming unreliable or failing completely.
• Potential cascading effects include data loss, system instability, and increased maintenance costs.
• Business impact may involve downtime, loss of data integrity, and financial losses due to hardware replacement and data recovery.

• Prevention Guidelines:

• Specific code-level fixes include implementing effective wear leveling algorithms that distribute write cycles evenly across memory.
• Security best practices involve regularly updating firmware and conducting thorough testing of wear leveling mechanisms.
• Recommended tools and frameworks include using libraries and firmware solutions that provide tested and reliable wear leveling techniques.

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Technical Details

Likelihood of Exploit: Not specified

Affected Languages: Not Language-Specific

Affected Technologies: System on Chip, Memory Hardware, Storage Hardware

Vulnerable Code Example

Python Example

import random

class NonVolatileMemory:
    def __init__(self, size):
        self.memory = [0] * size
        self.size = size

    def write(self, index, value):
        # Vulnerable code: Writing to the same index repeatedly
        # This can wear out the specific memory cell prematurely
        if 0 <= index < self.size:
            self.memory[index] = value
        else:
            raise IndexError("Index out of bounds")
            
nvm = NonVolatileMemory(1024)
for _ in range(1000):
    nvm.write(0, random.randint(0, 255))  # Always writing to index 0

Explanation

In this vulnerable example, the code repeatedly writes to the same memory cell (index 0) without any strategy to distribute the writes. This can lead to premature wear-out of the specific memory cell due to the limited write cycles inherent in non-volatile memories like EEPROM or flash.

How to fix Improper Write Handling in Limited-write Non-Volatile Memories?

To address this issue, we can implement wear leveling strategies that distribute writes more evenly across the memory space. This can be achieved by dynamically selecting the index to write to, ensuring that no single cell is disproportionately worn out.

Fix Approach:

1. Wear Leveling: Implement a wear leveling algorithm to distribute writes across different memory cells.
2. Dynamic Index Selection: Rotate the write location to spread the wear evenly.
3. Logical to Physical Mapping: Use a mapping system to abstract physical writes from logical operations.

Fixed Code Example

import random

class NonVolatileMemory:
    def __init__(self, size):
        self.memory = [0] * size
        self.size = size
        self.write_count = [0] * size  # Track write counts for wear leveling

    def write(self, value):
        # Fixed code: Implement wear leveling by cycling through memory
        index = self._get_least_written_index()  # Select the least written index
        self.memory[index] = value
        self.write_count[index] += 1  # Update the write count

    def _get_least_written_index(self):
        # Return index with the lowest write count
        return self.write_count.index(min(self.write_count))
        
nvm = NonVolatileMemory(1024)
for _ in range(1000):
    nvm.write(random.randint(0, 255))  # Writes are now distributed

Explanation

In the fixed code example, a write_count list is introduced to track the number of writes to each index. The write method selects the index with the least writes using the _get_least_written_index() function. This approach helps distribute the writes evenly across the memory, significantly reducing the risk of wearing out any single memory cell prematurely. This enhancement improves the longevity and reliability of the non-volatile memory.