Information Disclosure in Fastapi with Dynamodb

Information disclosure occurs when an API unintentionally exposes data that should remain restricted. The combination of FastAPI and DynamoDB can create exposure paths when application logic, data access patterns, or error handling reveal sensitive information. This typically happens when responses include unintended fields, when error messages surface internal details, or when access patterns allow one user to infer or retrieve another user’s data.

DynamoDB itself does not leak information by default, but the way an application interacts with it can. In FastAPI endpoints, developers may construct responses by reading items from DynamoDB and returning them directly. If the item contains fields such as internal identifiers, administrative flags, or debugging metadata, and those fields are not explicitly filtered, the client receives them. This is an information disclosure vector: the backend stores more data than the API contract implies, and the contract is not enforced strictly.

Consider an endpoint that retrieves a user profile by ID. If the implementation performs a DynamoDB get_item and returns the full item as JSON, fields like internal_role, password_reset_token, or debug_trace_id may be included unintentionally. In a black-box scan, an attacker can request the same endpoint with different IDs and observe whether different data classes appear in responses, leading to inference or enumeration. Even without authenticated access, misconfigured CORS or inconsistent error handling can amplify this: verbose error messages might include stack traces that reference DynamoDB attribute names or internal service details.

Another vector arises from incomplete filtering and projection. Suppose a FastAPI endpoint accepts query parameters to narrow results but does not enforce field-level authorization before passing the request to DynamoDB. An attacker could supply parameter combinations that cause the backend to retrieve items belonging to other users, and if the response includes sensitive attributes, information disclosure occurs. This intersects with BOLA/IDOR checks in middleBrick’s scan, where unauthenticated probing can reveal whether responses differ across IDs in ways that expose data scope or structure.

Patterns to avoid include returning entire DynamoDB items (which may contain system attributes like aws:rep:updateregion or sequenceNumber from streams), echoing user input in error messages, and failing to validate that the requesting user is authorized for the specific item. MiddleBrick’s LLM/AI Security checks also look for scenarios where endpoints might leak prompts or configuration via error messages, which can compound information disclosure when AI components are involved. Proper remediation centers on strict output filtering, schema validation, and ensuring error responses are generic and do not reference internal storage details.

Remediation focuses on controlling what leaves DynamoDB and what reaches the HTTP response. Use projection expressions in Query and Scan to retrieve only the fields required by the API contract. Validate and transform data in FastAPI models (Pydantic) to strip internal attributes before serialization. Enforce ownership checks at the data-access layer so that one user cannot request another user’s items, even if IDs are guessed.

Below is a concrete example of a FastAPI route that safely retrieves a user profile from DynamoDB using a projection expression and a Pydantic model for output. The code ensures only intended fields are read and returned, and it avoids exposing internal metadata.

from fastapi import FastAPI, HTTPException, Depends
from pydantic import BaseModel
import boto3
from botocore.exceptions import ClientError

app = FastAPI()
dynamodb = boto3.resource('dynamodb', region_name='us-east-1')
table_name = 'users'

class UserProfile(BaseModel):
    user_id: str
    username: str
    email: str
    display_name: str

def get_user_profile_from_db(user_id: str, requester_id: str):
    table = dynamodb.Table(table_name)
    try:
        response = table.get_item(
            Key={'user_id': user_id},
            ProjectionExpression='user_id, username, email, display_name'
        )
    except ClientError as e:
        raise HTTPException(status_code=502, detail='Data service error')
    item = response.get('Item')
    if not item:
        raise HTTPException(status_code=404, detail='Not found')
    # Ownership check: in a real app, derive requester from auth context
    if item.get('user_id') != requester_id:
        raise HTTPException(status_code=403, detail='Access denied')
    return item

@app.get('/profiles/{user_id}')
def read_profile(user_id: str, auth: dict = Depends(lambda: {'user_id': 'u-123'})):
    data = get_user_profile_from_db(user_id, auth['user_id'])
    return UserProfile(**data)

Key points in this example:

• ProjectionExpression limits DynamoDB to return only necessary attributes, preventing exposure of internal or sensitive fields.
• The UserProfile Pydantic model ensures only declared fields are serialized into the HTTP response; extra attributes in the DynamoDB item are ignored.
• An explicit ownership check is performed before returning data, mitigating IDOR risks by confirming the requesting user is allowed to view the target item.
• Errors are generic (e.g., Data service error, Not found) to avoid leaking stack traces or DynamoDB-specific details.

For broader listing endpoints, apply similar discipline. Use FilterExpression after retrieving only projected attributes, and enforce row-level ownership in the filter condition where possible. Avoid returning raw DynamoDB items from scan operations; instead, map to domain models and validate each item. These practices reduce the attack surface for information disclosure and align with secure-by-design patterns for API data access.