CWE-435: Improper Interaction Between Multiple Correctly-Behaving Entities

What is Improper Interaction Between Multiple Correctly-Behaving Entities?

• Overview: An Improper Interaction Between Multiple Correctly-Behaving Entities (CWE-435) occurs when two components function correctly on their own but exhibit incorrect or insecure behavior when combined in a system due to unforeseen interactions.

• Exploitation Methods:

• Attackers may exploit this by triggering unexpected interactions between components that lead to security breaches.
• Common attack patterns include leveraging unexpected data flows or state changes that occur when components interact.

• Security Impact:

• Direct consequences can include data leaks, unauthorized access, or denial of service.
• Potential cascading effects might lead to broader system failures or vulnerabilities.
• Business impact could involve loss of customer trust, legal issues, and financial losses.

• Prevention Guidelines:

• Specific code-level fixes include thorough testing of component interactions and boundary conditions.
• Security best practices involve defining clear interfaces and protocols for component interaction.
• Recommended tools and frameworks include static analysis tools to identify potential interaction issues and integration testing frameworks to simulate real-world interactions.

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Technical Details

Likelihood of Exploit: Not specified

Affected Languages: Not Language-Specific

Affected Technologies: Not Technology-Specific

Vulnerable Code Example

import threading
import time
import random

# Entity A: Data Producer
class DataProducer(threading.Thread):
    def __init__(self, data_queue):
        threading.Thread.__init__(self)
        self.data_queue = data_queue

    def run(self):
        while True:
            data = random.randint(1, 100)
            print(f"Producing data: {data}")
            self.data_queue.append(data)  # Improper interaction: appending data without synchronization
            time.sleep(1)

# Entity B: Data Consumer
class DataConsumer(threading.Thread):
    def __init__(self, data_queue):
        threading.Thread.__init__(self)
        self.data_queue = data_queue

    def run(self):
        while True:
            if self.data_queue:  # Improper interaction: checking without synchronization
                data = self.data_queue.pop(0)  # Improper interaction: popping data without synchronization
                print(f"Consuming data: {data}")
            time.sleep(1)

data_queue = []
producer = DataProducer(data_queue)
consumer = DataConsumer(data_queue)

producer.start()
consumer.start()

Explanation

• Improper Interaction: The DataProducer and DataConsumer classes are interacting with a shared resource (data_queue) without synchronization. This can lead to data corruption or runtime errors due to concurrent access. The producer appends data and the consumer checks and pops data from the queue without any locking mechanism, risking race conditions.

How to fix Improper Interaction Between Multiple Correctly-Behaving Entities?

To address the improper interaction between these entities, we need to ensure that access to shared resources is properly synchronized. This can be achieved using locks or other synchronization primitives provided by the language. In Python, the threading module provides a Lock class that can be used to ensure that only one thread can access the shared resource at a time.

Best practices include:

• Using a lock to control access to shared resources.
• Acquiring the lock before accessing shared resources and releasing it afterward.
• Encapsulating the access logic in a way that minimizes the locked region to avoid performance bottlenecks.

Fixed Code Example

import threading
import time
import random

# Entity A: Data Producer
class DataProducer(threading.Thread):
    def __init__(self, data_queue, queue_lock):
        threading.Thread.__init__(self)
        self.data_queue = data_queue
        self.queue_lock = queue_lock

    def run(self):
        while True:
            data = random.randint(1, 100)
            print(f"Producing data: {data}")
            with self.queue_lock:  # Using lock to synchronize access to the shared resource
                self.data_queue.append(data)
            time.sleep(1)

# Entity B: Data Consumer
class DataConsumer(threading.Thread):
    def __init__(self, data_queue, queue_lock):
        threading.Thread.__init__(self)
        self.data_queue = data_queue
        self.queue_lock = queue_lock

    def run(self):
        while True:
            with self.queue_lock:  # Using lock to synchronize access to the shared resource
                if self.data_queue:
                    data = self.data_queue.pop(0)  # Accessing shared resource within locked context
                    print(f"Consuming data: {data}")
            time.sleep(1)

data_queue = []
queue_lock = threading.Lock()  # Creating a lock for synchronizing access to the queue
producer = DataProducer(data_queue, queue_lock)
consumer = DataConsumer(data_queue, queue_lock)

producer.start()
consumer.start()

Explanation

• Synchronization with Lock: Introduced a Lock object queue_lock to manage access to the shared data_queue.
• Safe Access: Both DataProducer and DataConsumer use with self.queue_lock: to ensure that access to data_queue is thread-safe.
• Encapsulation: The locking mechanism is encapsulated in the data access logic, allowing safe concurrent interactions between the producer and consumer threads. This prevents race conditions and ensures the data integrity of the shared resource.