CWE-1250: Improper Preservation of Consistency Between Independent Representations of Shared State

What is Improper Preservation of Consistency Between Independent Representations of Shared State?

• Overview:

• CWE-1250 involves a failure to maintain consistency between independent copies of shared data within distributed systems. This occurs when each component of a system keeps its local copy of the shared state but lacks synchronization, leading to discrepancies in how the system's state is perceived across components.

• Exploitation Methods:

• Attackers can exploit this vulnerability by manipulating one component's view of the state, causing inconsistencies that can lead to incorrect operations or security breaches.
• Common attack patterns include race conditions, where attackers exploit timing discrepancies, and cache poisoning, where they introduce malformed data into one component's local copy.

• Security Impact:

• Direct consequences include the execution of transactions out of order, incorrect data being served to users, and potential data corruption.
• Potential cascading effects involve broader system failures, as other components may rely on the corrupted data to make critical decisions.
• Business impact includes loss of data integrity, customer trust, and potential financial liabilities due to service disruptions or data breaches.

• Prevention Guidelines:

• Specific code-level fixes include implementing robust synchronization mechanisms, such as locks or atomic transactions, to ensure all components update their state consistently.
• Security best practices involve designing systems with eventual consistency models that can tolerate temporary inconsistencies and provide mechanisms for conflict resolution.
• Recommended tools and frameworks include distributed databases and state management systems designed for consistency, such as Apache Kafka for event streaming or frameworks like Akka for actor-based concurrency.

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Technical Details

Likelihood of Exploit: Not specified

Affected Languages: Not Language-Specific

Affected Technologies: Cloud Computing, Security Hardware

Vulnerable Code Example

Python Example

import threading
import time

# Simulates a shared state accessed by multiple components
class SharedState:
    def __init__(self):
        self.local_state_1 = {"counter": 0}
        self.local_state_2 = {"counter": 0}

    def increment_counter_1(self):
        self.local_state_1["counter"] += 1
        time.sleep(0.1)  # Simulate delay
        print(f"Local State 1: {self.local_state_1['counter']}")

    def increment_counter_2(self):
        self.local_state_2["counter"] += 1
        time.sleep(0.1)  # Simulate delay
        print(f"Local State 2: {self.local_state_2['counter']}")

shared_state = SharedState()
threads = []
for _ in range(5):
    t1 = threading.Thread(target=shared_state.increment_counter_1)
    t2 = threading.Thread(target=shared_state.increment_counter_2)
    threads.append(t1)
    threads.append(t2)

for t in threads:
    t.start()

for t in threads:
    t.join()

# The output will show inconsistent state between local_state_1 and local_state_2

Vulnerability Explanation

• Improper Preservation of Consistency: The two local states (local_state_1 and local_state_2) are incremented independently without any synchronization mechanism. This can lead to inconsistent states, where the counters diverge, which may not be the desired behavior if these states should be synchronized.

How to fix Improper Preservation of Consistency Between Independent Representations of Shared State?

Fixed Code Example

Python Example

import threading

# Centralized shared state with thread-safe operations
class SharedState:
    def __init__(self):
        self.shared_counter = 0
        self.lock = threading.Lock()

    def increment_counter(self):
        with self.lock:  # Ensures that only one thread can increment the counter at a time
            self.shared_counter += 1
            print(f"Shared Counter: {self.shared_counter}")

shared_state = SharedState()
threads = []
for _ in range(10):  # Adjusted to use a single increment function
    t = threading.Thread(target=shared_state.increment_counter)
    threads.append(t)

for t in threads:
    t.start()

for t in threads:
    t.join()

# The output will now show a consistent state across all threads

Fix Explanation

• Centralized State Management: The shared_counter replaces the two separate counters, serving as the single source of truth.
• Thread-Safe Access: A threading.Lock is used to ensure that the increment operation is atomic, preventing race conditions. This guarantees that all increments are accurately recorded and no updates are lost.
• Unified Access: All threads now operate on a single shared counter, ensuring consistency across all accesses. This eliminates the inconsistency between independent representations of the shared state.