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kh2vFIma4WntHaS6kBaCCheck which Users have AWS IAM Policies with Admin Access: SOC2 Compliance
Check which Users have AWS IAM Policies with Admin Access: SOC2 Compliance
There was a problem that the LLM was not able to address. Please rephrase your prompt and try again.
This task audits AWS IAM users to identify those with administrative access. It ensures adherence to security standards by limiting broad access rights, crucial for mitigating risks associated with unauthorized permissions in a cloud environment.
inputs
outputs
import boto3
from botocore.exceptions import ClientError, BotoCoreError
creds = _get_creds(cred_label)['creds']
access_key = creds['username']
secret_key = creds['password']
def is_admin_policy(policy_document, exclude_permission_boundary):
"""
Check if the policy document contains admin access statements.
"""
for statement in policy_document.get('Statement', []):
if statement.get('Effect') == 'Allow' and \
statement.get('Action') == '*' and \
statement.get('Resource') == '*':
if not exclude_permission_boundary or \
(exclude_permission_boundary and 'Condition' not in statement):
return True
return False
def evaluate_group_policies(user_name, iam, exclude_permission_boundary):
"""
Evaluate policies attached to groups for a given user.
"""
reasons = []
try:
groups = iam.list_groups_for_user(UserName=user_name)['Groups']
for group in groups:
attached_policies = iam.list_attached_group_policies(GroupName=group['GroupName'])['AttachedPolicies']
#print(attached_policies) # for debugging
for policy in attached_policies:
policy_details = iam.get_policy(PolicyArn=policy['PolicyArn'])
if 'DefaultVersionId' in policy_details['Policy']:
policy_version = iam.get_policy_version(
PolicyArn=policy['PolicyArn'],
VersionId=policy_details['Policy']['DefaultVersionId']
)
if is_admin_policy(policy_version['PolicyVersion']['Document'], exclude_permission_boundary):
reasons.append(f"Group Attached Policy: {policy['PolicyArn']} ({group['GroupName']})")
except ClientError as e:
print(f"Error retrieving group policies for user {user_name}: {e}")
return reasons
def evaluate_attached_policies(user_name, iam, exclude_permission_boundary):
"""
Evaluate attached managed policies for a given user.
"""
reasons = []
try:
attached_policies = iam.list_attached_user_policies(UserName=user_name)['AttachedPolicies']
#print(attached_policies) # for debugging
for policy in attached_policies:
policy_details = iam.get_policy(PolicyArn=policy['PolicyArn'])
if 'DefaultVersionId' in policy_details['Policy']:
policy_version = iam.get_policy_version(
PolicyArn=policy['PolicyArn'],
VersionId=policy_details['Policy']['DefaultVersionId']
)
if is_admin_policy(policy_version['PolicyVersion']['Document'], exclude_permission_boundary):
reasons.append(f"Attached Policy: {policy['PolicyArn']}")
except ClientError as e:
print(f"Error retrieving attached policies for user {user_name}: {e}")
return reasons
def evaluate_inline_policies(user_name, iam, exclude_permission_boundary):
"""
Evaluate inline policies for a given user.
"""
reasons = []
try:
inline_policies = iam.list_user_policies(UserName=user_name)['PolicyNames']
#print(inline_policies) # for debugging
for policy_name in inline_policies:
policy_document = iam.get_user_policy(
UserName=user_name,
PolicyName=policy_name
)['PolicyDocument']
if is_admin_policy(policy_document, exclude_permission_boundary):
reasons.append(f"Inline Policy: {policy_name}")
except ClientError as e:
print(f"Error retrieving inline policies for user {user_name}: {e}")
return reasons
def evaluate_iam_users_and_policies(exclude_permission_boundary=False):
"""
Evaluates IAM users for admin access in attached, inline, and group policies.
"""
iam = boto3.client('iam',aws_access_key_id=access_key,aws_secret_access_key=secret_key)
compliance_report = {
'compliant': [],
'non_compliant': {}
}
try:
users = iam.list_users()['Users']
for user in users:
user_name = user['UserName']
print(f"Evaluating user: {user_name}")
reasons = evaluate_attached_policies(user_name, iam, exclude_permission_boundary) + \
evaluate_inline_policies(user_name, iam, exclude_permission_boundary) + \
evaluate_group_policies(user_name, iam, exclude_permission_boundary)
if reasons:
compliance_report['non_compliant'][user_name] = reasons
else:
compliance_report['compliant'].append(user_name)
except ClientError as e:
print(f"ClientError while listing IAM users: {e}")
except BotoCoreError as e:
print(f"BotoCoreError encountered: {e}")
except Exception as e:
print(f"An unexpected error occurred: {e}")
# Print Compliance Report
if compliance_report['non_compliant']:
print("\nNon-Compliant IAM Users (Admin Access Found):")
for user, reasons in compliance_report['non_compliant'].items():
print(f"{user} - Reasons: {', '.join(reasons)}")
else:
print("\nNo Non-Compliant IAM Users Found.")
if compliance_report['compliant']:
print("\nCompliant IAM Users (No Admin Access):")
for user in compliance_report['compliant']:
print(user)
else:
print("\nAll IAM Users are Non-Compliant.")
exclude_permission_boundary = False
evaluate_iam_users_and_policies(exclude_permission_boundary)
context.skip_sub_tasks=True
copied- 1KyqIZ8LMOnuC9qxXPYEfRemove/Delete an IAM Policy from an AWS IAM User
1
Remove/Delete an IAM Policy from an AWS IAM User
There was a problem that the LLM was not able to address. Please rephrase your prompt and try again.This task is used to detach managed IAM policies or delete inline policies from specific IAM users. This action is crucial for maintaining secure and appropriate access levels within AWS environments, ensuring compliance with best security practices.
inputsoutputsimport boto3 from botocore.exceptions import ClientError, NoCredentialsError, BotoCoreError creds = _get_creds(cred_label)['creds'] access_key = creds['username'] secret_key = creds['password'] def remove_or_modify_policy(iam_client, user_name, policy_arn=None, inline_policy_name=None): """ Detach a managed IAM policy or delete an inline IAM policy from a specified AWS IAM user. Args: iam_client: An initialized Boto3 IAM client. user_name: The name of the IAM user. policy_arn: The ARN of the managed IAM policy to be detached. inline_policy_name: The name of the inline IAM policy to be deleted. The function checks if the user exists and whether the specified policies are attached or exist, then proceeds with the appropriate action. """ try: # Check if the user exists iam_client.get_user(UserName=user_name) if policy_arn: # Detach managed policy if it is attached attached_policies = iam_client.list_attached_user_policies(UserName=user_name)['AttachedPolicies'] if any(policy['PolicyArn'] == policy_arn for policy in attached_policies): iam_client.detach_user_policy(UserName=user_name, PolicyArn=policy_arn) print(f"Detached policy {policy_arn} from {user_name}") else: print(f"Policy {policy_arn} is not attached to {user_name}") elif inline_policy_name: # Delete inline policy if it exists inline_policies = iam_client.list_user_policies(UserName=user_name)['PolicyNames'] if inline_policy_name in inline_policies: iam_client.delete_user_policy(UserName=user_name, PolicyName=inline_policy_name) print(f"Deleted inline policy {inline_policy_name} from {user_name}") else: print(f"Inline policy {inline_policy_name} does not exist for {user_name}") except ClientError as e: print(f"An AWS ClientError occurred: {e}") except NoCredentialsError: print("No AWS credentials available. Please configure them.") except BotoCoreError as e: print(f"A BotoCoreError occurred: {e}") except Exception as e: print(f"An unexpected error occurred: {e}") iam_client = boto3.client('iam',aws_access_key_id=access_key,aws_secret_access_key=secret_key) # user_name = 'test_user' # policy_arn_to_remove = 'arn:aws:iam::aws:policy/AdministratorAccess' # Example ARN # inline_policy_name = 'your-inline-policy-name' remove_or_modify_policy(iam_client, user_name, policy_arn=policy_arn_to_remove)copied1