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Event Information

  • The CreateEventSourceMapping event in AWS Lambda refers to the process of creating a mapping between an event source and a Lambda function.
  • This event is triggered when you configure an event source, such as an Amazon S3 bucket or an Amazon Kinesis stream, to invoke a specific Lambda function.
  • The CreateEventSourceMapping event allows you to define the source of events that will trigger the Lambda function, and specify any additional configuration settings, such as batch size or starting position in the event stream.

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Examples

  1. Unauthorized access: If the IAM role associated with the Lambda function does not have sufficient permissions to create or modify event source mappings, it can lead to unauthorized access. Ensure that the IAM role has the necessary permissions to create and manage event source mappings.

  2. Exposure of sensitive information: If the event source mapping configuration includes sensitive information such as API keys or credentials, it can potentially expose this information to unauthorized users. Make sure to avoid including sensitive information in the event source mapping configuration.

  3. Insecure event source configuration: If the event source mapping is configured to listen to an insecure event source, such as an unencrypted message queue or an open API endpoint, it can increase the risk of security breaches. Always ensure that event sources are properly secured and encrypted to protect against unauthorized access.

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Remediation

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Using Console

  1. Identify the specific issue or vulnerability in the AWS Lambda function by reviewing the event logs or security findings in the AWS console.

  2. Determine the appropriate remediation steps based on the examples provided in the previous response:

    a. Example 1: Excessive permissions for Lambda function

    • Access the AWS Lambda console.
    • Select the specific Lambda function that has excessive permissions.
    • Click on the “Permissions” tab.
    • Review the existing permissions and identify any unnecessary or excessive permissions.
    • Remove the unnecessary permissions by clicking on the “X” icon next to each permission.
    • Click on “Save” to apply the changes.

    b. Example 2: Insecure environment variables in Lambda function

    • Access the AWS Lambda console.
    • Select the specific Lambda function that has insecure environment variables.
    • Click on the “Configuration” tab.
    • Scroll down to the “Environment variables” section.
    • Review the existing environment variables and identify any sensitive information.
    • Remove or encrypt any sensitive environment variables.
    • Click on “Save” to apply the changes.

    c. Example 3: Unencrypted data storage in Lambda function

    • Access the AWS Lambda console.
    • Select the specific Lambda function that has unencrypted data storage.
    • Click on the “Configuration” tab.
    • Scroll down to the “Environment variables” section.
    • Review the existing environment variables and identify any variables related to data storage.
    • Ensure that the data storage variables are configured to use encrypted storage options such as AWS KMS.
    • Click on “Save” to apply the changes.

  3. Monitor the AWS Lambda function after applying the remediation steps to ensure that the issues have been resolved and the function is functioning as expected. Regularly review the event logs and security findings to identify any new issues or vulnerabilities that may arise.

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Using CLI

  1. Enable VPC configuration for AWS Lambda:

    • Use the update-function-configuration command to update the Lambda function’s configuration.
    • Specify the --vpc-config parameter with the appropriate VPC configuration details, such as SubnetIds and SecurityGroupIds.
    • Example command: aws lambda update-function-configuration --function-name <function-name> --vpc-config SubnetIds=<subnet-ids>,SecurityGroupIds=<security-group-ids>

  2. Enable encryption at rest for AWS Lambda function code:

    • Use the update-function-configuration command to update the Lambda function’s configuration.
    • Specify the --kms-key-arn parameter with the ARN of the KMS key to be used for encryption.
    • Example command: aws lambda update-function-configuration --function-name <function-name> --kms-key-arn <kms-key-arn>

  3. Enable AWS CloudTrail logging for AWS Lambda:

    • Use the update-function-configuration command to update the Lambda function’s configuration.
    • Specify the --tracing-config parameter with the appropriate tracing configuration details, such as Mode set to Active.
    • Example command: aws lambda update-function-configuration --function-name <function-name> --tracing-config Mode=Active

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Using Python

  1. Example 1: Limiting Execution Time
  • Set a timeout value for your AWS Lambda function to ensure it doesn’t exceed the maximum allowed execution time.
  • In your Python script, you can use the signal module to handle timeouts. Here’s an example:
import signal

def lambda_handler(event, context):
    # Set the timeout value in seconds
    timeout = 5

    # Define a function to handle timeouts
    def timeout_handler(signum, frame):
        raise Exception("Function timed out")

    # Set the timeout handler
    signal.signal(signal.SIGALRM, timeout_handler)
    signal.alarm(timeout)

    # Your code here
    # ...

    # Reset the alarm
    signal.alarm(0)

    # Return the response
    return {
        'statusCode': 200,
        'body': 'Function executed successfully'
    }
  1. Example 2: Enforcing Least Privilege
  • Implement AWS Identity and Access Management (IAM) policies to restrict the permissions of your Lambda function.
  • In your Python script, you can use the boto3 library to interact with IAM and assign appropriate roles and policies. Here’s an example:
import boto3

def lambda_handler(event, context):
    # Create an IAM client
    iam = boto3.client('iam')

    # Define the IAM policy
    policy_document = {
        "Version": "2012-10-17",
        "Statement": [
            {
                "Effect": "Allow",
                "Action": [
                    "s3:GetObject"
                ],
                "Resource": "arn:aws:s3:::my-bucket/*"
            }
        ]
    }

    # Create the IAM policy
    response = iam.create_policy(
        PolicyName='LambdaPolicy',
        PolicyDocument=json.dumps(policy_document)
    )

    # Attach the policy to the Lambda function's role
    iam.attach_role_policy(
        RoleName='my-lambda-role',
        PolicyArn=response['Policy']['Arn']
    )

    # Your code here
    # ...

    # Return the response
    return {
        'statusCode': 200,
        'body': 'Function executed successfully'
    }
  1. Example 3: Encrypting Sensitive Data
  • Use AWS Key Management Service (KMS) to encrypt and decrypt sensitive data within your Lambda function.
  • In your Python script, you can utilize the boto3 library to interact with KMS and perform encryption and decryption operations. Here’s an example:
import boto3

def lambda_handler(event, context):
    # Create a KMS client
    kms = boto3.client('kms')

    # Encrypt sensitive data
    response = kms.encrypt(
        KeyId='arn:aws:kms:us-west-2:123456789012:key/12345678-1234-1234-1234-123456789012',
        Plaintext='Sensitive data'
    )

    # Your code here
    # ...

    # Decrypt the encrypted data
    decrypted_data = kms.decrypt(
        CiphertextBlob=response['CiphertextBlob']
    )

    # Return the response
    return {
        'statusCode': 200,
        'body': decrypted_data['Plaintext']
    }

Please note that the above examples are simplified and may require additional configuration and error handling based on your specific use case.