Auth Bypass in Fastapi with Dynamodb

An Auth Bypass in a Fastapi application using DynamoDB typically arises when authorization checks are incomplete, overly permissive, or incorrectly applied after authentication. In this stack, authentication may rely on an external identity provider or API keys, while authorization against DynamoDB is implemented at the application layer using IAM policies and condition expressions. If these checks are missing, inconsistent, or bypassed via direct parameter manipulation, an authenticated user can access or modify resources that should be isolated.

DynamoDB itself does not enforce row-level or user-level access; it enforces permissions at the AWS identity level via IAM. Therefore, if the Fastapi service uses a single IAM role or credential with broad DynamoDB permissions and fails to scope requests by user or tenant, any compromised or malicious authenticated context can read or write across the entire table. This becomes an Auth Bypass when the API incorrectly trusts client-supplied identifiers (e.g., userId in the URL or body) without verifying that the authenticated subject owns that identifier.

For example, consider an endpoint /users/{user_id}/profile that retrieves a profile from DynamoDB. If the route uses a simple GetItem with Key={"user_id": user_id} but does not compare the authenticated subject’s ID with the requested user_id, an attacker can change the path parameter to access another user’s profile. Because the underlying IAM policy may allow dynamodb:GetItem on the table, the request succeeds despite the lack of proper ownership validation. This is a BOLA/IDOR pattern enabled by missing authorization logic, not a weakness in DynamoDB itself.

OpenAPI/Swagger spec analysis helps surface these issues by comparing declared parameters and security schemes with actual runtime behavior. If the spec defines a userId path parameter but the security scheme does not enforce scope checks per operation, the scan can flag the operation as an Auth Bypass risk. Instrumentation that cross-references spec definitions with runtime requests reveals whether DynamoDB calls correctly incorporate the authenticated subject’s identity into key expressions or condition expressions.

Another variant involves unauthenticated LLM endpoints or unsafe consumption patterns where an LLM-generated payload is directly used to build DynamoDB requests. If prompts or generated values override intended permissions, an attacker may coerce the service to execute actions under elevated or unintended identities. MiddleBrick’s LLM/AI Security checks detect system prompt leakage and test for prompt injection to reduce this class of risk, ensuring that AI-assisted components do not weaken authorization boundaries.

Finally, misconfigured rate limiting or missing input validation can amplify Auth Bypass risks by enabling brute-force enumeration of user IDs or token abuse. Proper per-user throttling and strict validation of identifiers, combined with scoped IAM policies and conditional DynamoDB expressions, reduce the attack surface. The goal is to ensure that every DynamoDB request is explicitly scoped to the authenticated subject and that no route or parameter alone grants cross-subject access.

Remediation centers on enforcing ownership checks and scoping every DynamoDB operation to the authenticated subject. Below is a concrete Fastapi example using boto3 with DynamoDB and JWT-based authentication. The key pattern is to bind the authenticated subject to every request and never trust client-supplied identifiers for access control.

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

app = FastAPI()

# Assume JWT secret and DynamoDB table name are configured
SECRET_KEY = "your-secret"
TABLE_NAME = "profiles"
ddb = boto3.resource("dynamodb", region_name="us-east-1")
table = ddb.Table(TABLE_NAME)

class Profile(BaseModel):
    user_id: str
    display_name: str

def get_current_user(token: str):
    try:
        payload = jwt.decode(token, SECRET_KEY, algorithms=["HS256"])
        user_id: str = payload.get("sub")
        if user_id is None:
            raise HTTPException(status_code=status.HTTP_401_UNAUTHORIZED, detail="Invalid token")
        return user_id
    except jwt.PyJWTError:
        raise HTTPException(status_code=status.HTTP_401_UNAUTHORIZED, detail="Invalid token")

@app.get("/users/{user_id}/profile")
def get_profile(user_id: str, token: str = Depends(get_current_user)):
    # Critical: ensure the authenticated user matches the requested user_id
    if user_id != token:  # token here is the subject (sub)
        raise HTTPException(status_code=403, detail="Forbidden: cannot access other user’s profile")
    try:
        response = table.get_item(
            Key={"user_id": user_id},
            ConsistentRead=True
        )
    except ClientError as e:
        raise HTTPException(status_code=500, detail="Database error")
    item = response.get("Item")
    if not item:
        raise HTTPException(status_code=404, detail="Profile not found")
    return Profile(**item)

@app.put("/users/{user_id}/profile")
def update_profile(user_id: str, payload: Profile, token: str = Depends(get_current_user)):
    if user_id != token:
        raise HTTPException(status_code=403, detail="Forbidden: cannot update other user’s profile")
    try:
        table.put_item(Item={"user_id": user_id, "display_name": payload.display_name})
    except ClientError as e:
        raise HTTPException(status_code=500, detail="Failed to update")
    return {"status": "ok"}

This pattern ensures that the authenticated subject is used both for identity verification and to scope DynamoDB keys, preventing Auth Bypass via path or body parameter tampering. For more complex multi-tenant scenarios, include a tenant_id in the key and validate that the authenticated subject belongs to that tenant before issuing any DynamoDB request.

When using DynamoDB condition expressions, enforce ownership at the database level as a defense-in-depth measure. For example, use a condition that the user_id in the item matches the authenticated subject. While this does not replace application-layer checks, it reduces the impact of logic errors.

In CI/CD workflows, the GitHub Action can add API security checks to your pipeline and fail builds if risk scores exceed your threshold. This helps catch regressions that might relax ownership checks. For ongoing assurance, the Pro plan provides continuous monitoring and alerts when findings appear, enabling teams to respond quickly to new Auth Bypass risks introduced by changes.

Finally, review IAM policies to ensure least privilege: the service role used by Fastapi should have scoped permissions (e.g., conditional IAM policies referencing the user_id) where possible, and avoid broad administrative policies. Combine this with input validation and per-user rate limiting to further reduce opportunities for abuse.