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Event Information

  • The AddPermission event in AWS Lambda refers to an action taken to grant permission to another AWS service or entity to invoke a Lambda function.
  • This event is typically used when you want to allow external services or resources to trigger your Lambda function.
  • By using the AddPermission event, you can specify the necessary permissions and conditions for the invoker to successfully invoke your Lambda function.

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Examples

  • Unauthorized access: If the AddPermission action is misconfigured or misused, it can potentially grant unauthorized access to the Lambda function. This can lead to unauthorized execution of the function or unauthorized access to sensitive data or resources.

  • Privilege escalation: If the AddPermission action is used to grant excessive permissions to a Lambda function, it can result in privilege escalation. An attacker may exploit this by gaining access to resources or performing actions that they are not authorized to perform.

  • Denial of Service (DoS) attacks: If the AddPermission action is used to grant overly permissive permissions to a Lambda function, it can be abused to launch DoS attacks. An attacker may flood the function with requests, causing it to consume excessive resources and potentially impacting the availability of other resources in the system.

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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 at rest in Lambda function

    • Access the AWS Lambda console.
    • Select the specific Lambda function that has unencrypted data at rest.
    • Click on the “Configuration” tab.
    • Scroll down to the “Encryption” section.
    • Enable encryption for the Lambda function by selecting an appropriate encryption option (e.g., AWS Key Management Service - KMS).
    • Configure the encryption settings as per your requirements.
    • Click on “Save” to apply the changes.

  3. Validate the remediation by re-evaluating the security findings or event logs to ensure that the identified issues have been successfully addressed.

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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 IAM roles and policies to restrict the permissions of your AWS Lambda function.
  • In your Python script, you can use the boto3 library to interact with AWS services. Here’s an example of how to assume an IAM role with limited permissions:
import boto3

def lambda_handler(event, context):
    # Create a new session with limited permissions
    session = boto3.Session()
    sts_client = session.client('sts')

    # Assume an IAM role with restricted permissions
    role_arn = 'arn:aws:iam::123456789012:role/limited-role'
    assumed_role = sts_client.assume_role(RoleArn=role_arn, RoleSessionName='lambda-session')

    # Create a new session using the assumed role credentials
    assumed_session = boto3.Session(
        aws_access_key_id=assumed_role['Credentials']['AccessKeyId'],
        aws_secret_access_key=assumed_role['Credentials']['SecretAccessKey'],
        aws_session_token=assumed_role['Credentials']['SessionToken']
    )

    # Use the assumed session to interact with AWS services
    s3_client = assumed_session.client('s3')
    response = s3_client.list_buckets()

    # 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 in your AWS Lambda function.
  • In your Python script, you can use the boto3 library to interact with AWS KMS. Here’s an example of how to encrypt and decrypt data using a KMS key:
import boto3

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

    # Encrypt sensitive data
    plaintext_data = 'Sensitive data'
    key_id = 'arn:aws:kms:us-east-1:123456789012:key/abcd1234-5678-90ab-cdef-1234567890ab'
    response = kms_client.encrypt(KeyId=key_id, Plaintext=plaintext_data)

    # Your code here
    # ...

    # Decrypt the encrypted data
    ciphertext_blob = response['CiphertextBlob']
    decrypted_data = kms_client.decrypt(CiphertextBlob=ciphertext_blob)['Plaintext'].decode('utf-8')

    # Your code here
    # ...

    # Return the response
    return {
        'statusCode': 200,
        'body': 'Function executed successfully'
    }
}