Privilege Escalation in Chi with Dynamodb

Privilege Escalation (BFLA) in the context of Chi using Amazon DynamoDB often arises when authorization checks are incomplete or when application logic relies on client-supplied identifiers without verifying ownership or scope. In Chi, if API endpoints accept a DynamoDB key (such as a partition key or sort key) directly from the request and use it to fetch or modify items without validating that the authenticated subject has rights to that specific key, an attacker can manipulate the key to access or mutate other users’ data.

DynamoDB’s permission model is attribute-based and policy-driven. If a Chi service uses AWS credentials or IAM policies that are overly permissive (for example, dynamodb:GetItem or dynamodb:UpdateItem without a condition on the key attributes), and the application does not enforce per-user filters, a malicious actor can iterate or guess keys to read or update records they should not touch. This is commonly observed when endpoints expose patterns like /users/{userId} and pass {userId} directly into a DynamoDB query without ensuring the caller owns that userId.

Chi applications that generate signed URLs or temporary tokens for DynamoDB access must ensure those tokens are scoped tightly to the intended operations and resource ARNs. Without scoping, an attacker holding a token can perform actions beyond the intended context, effectively escalating their privileges. Additionally, if the Chi backend uses DynamoDB streams or Lambda triggers to perform further actions, insufficient validation of event payloads can lead to privilege escalation through malicious data injection.

Real-world attack patterns include changing the partition key in requests to access other users’ records (Insecure Direct Object Reference), exploiting missing ownership checks in batch operations, or leveraging overly permissive IAM policies that allow dynamodb:Scan in production-like environments. These patterns map to OWASP API Top 10 A1: Broken Object Level Authorization and can lead to unauthorized data access or modification.

Remediation centers on enforcing ownership checks, scoping IAM policies, and validating input before using DynamoDB keys. Below are concrete code examples for Chi that demonstrate secure patterns.

1. Enforce ownership on queries

Always filter query results by the authenticated subject. Do not rely on the client-provided key alone.

import boto3
from chi import Request

dynamodb = boto3.resource('dynamodb')
table = dynamodb.Table('UserProfiles')

def get_user_profile(request: Request):
    # Assume request.user.id is the authenticated subject
    user_id = request.user.id
    # Do not trust request.path('userId') directly; validate it matches the authenticated user
    profile_id = request.params.get('profile_id')
    response = table.get_item(
        Key={
            'user_id': user_id,
            'profile_id': profile_id
        }
    )
    item = response.get('Item')
    if item is None:
        raise HTTPException(status_code=404, detail='Not found')
    return item

2. Use condition expressions for updates

When updating items, include a condition that ties the update to the owner and expected state, preventing unauthorized modifications.

def update_user_email(request: Request, profile_id: str, new_email: str):
    user_id = request.user.id
    try:
        table.update_item(
            Key={
                'user_id': user_id,
                'profile_id': profile_id
            },
            UpdateExpression='SET email = :val',
            ConditionExpression='attribute_exists(user_id) AND user_id = :uid',
            ExpressionAttributeValues={
                ':val': new_email,
                ':uid': user_id
            }
        )
    except Exception as e:
        # Handle ConditionalCheckFailedException
        raise HTTPException(status_code=403, detail='Update condition failed')

3. Scope IAM policies with least privilege

Define IAM roles and policies that restrict DynamoDB actions to specific table ARNs and key attributes. Avoid wildcard actions in production Chi services.

# Example IAM policy snippet (JSON) for a Chi service role
{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Action": [
                "dynamodb:GetItem",
                "dynamodb:UpdateItem"
            ],
            "Resource": "arn:aws:dynamodb:region:account-id:table/UserProfiles",
            "Condition": {
                "ForAllValues:StringEquals": {
                    "dynamodb:LeadingKeys": ["${aws:username}"]
                }
            }
        }
    ]
}

4. Validate and normalize keys

Normalize keys on the server and reject unexpected formats to reduce injection or enumeration risks.

import re

def normalize_user_id(input_id: str) -> str:
    # Allow only alphanumeric and underscores, prevent path traversal or injection
    if not re.match(r'^[a-zA-Z0-9_]+$', input_id):
        raise ValueError('Invalid user_id')
    return input_id

def safe_get_item(table_name: str, user_input_key: str):
    key = normalize_user_id(user_input_key)
    # Proceed with DynamoDB call using the normalized key

5. Use temporary credentials with tight scoping

If generating temporary credentials or signed URLs for DynamoDB, scope them to specific actions and resources, and set short TTLs.

# Pseudo-code for scoped token generation
# In Chi, use a backend service to issue short-lived tokens that include
# the intended DynamoDB table and key constraints.
def generate_scoped_token(user_id: str, profile_id: str):
    # This should integrate with your auth provider to produce a token
    # that limits operations to the given user_id and profile_id
    pass