Out Of Bounds Read in Fastapi with Dynamodb

An Out Of Bounds Read occurs when an API accesses memory or data beyond the intended allocation. In a Fastapi service that uses Dynamodb, this typically arises from unchecked index or key values derived from user input, leading to reads outside the expected record set or partition key space. Because Fastapi passes request parameters directly into data access logic, missing validation on numeric IDs, pagination tokens, or key identifiers can cause the backend to issue Dynamodb requests with invalid keys or offsets. These invalid requests do not trigger server-side crashes but may return adjacent records, empty responses, or internal errors that expose stack traces or metadata, effectively creating an out-of-bounds read path.

When an OpenAPI spec is analyzed, middleBrick detects mismatches between declared parameter constraints and runtime behavior. For example, if a path parameter item_id is defined as an integer with no minimum value, a negative or extremely large integer may produce a Dynamodb query that targets an unintended partition or sort key range. Similarly, pagination endpoints using ExclusiveStartKey without validating the key schema can iterate beyond the table’s actual item boundaries. The scanner’s 12 checks run in parallel and flag such issues under BOLA/IDOR and Input Validation, noting that unchecked inputs allow reading outside the authorized data scope. Because Dynamodb returns no data rather than a traditional memory error, the read may be silent, yet it can reveal the existence of adjacent records or configuration details in error responses, aiding reconnaissance for further attacks.

middleBrick’s LLM/AI Security checks add value by scanning for indirect prompt or configuration leakage that could describe data layout, but the core risk here is deterministic: insufficient input validation on identifiers and cursors. The scanner correlates the endpoint definition in the spec with runtime test probes and flags missing schema enforcement, excessive agency patterns in function calling, or missing authorization on key parameters. This helps teams see how an out-of-bounds read in Fastapi with Dynamodb can expose more than just data, including metadata that assists further exploitation.

Remediation focuses on strict input validation, bounded key ranges, and safe pagination handling. Define Pydantic models with explicit constraints and validate all identifiers before constructing Dynamodb requests. Use conditional checks and limit results to prevent reading unintended adjacent items.

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

app = FastAPI()
ddb = boto3.resource('dynamodb', region_name='us-east-1')
table = ddb.Table('ItemsTable')

class Item(BaseModel):
    id: int = Field(..., ge=1, le=1000000)
    name: str

@app.get('/items/{item_id}')
def read_item(item_id: int = Query(..., ge=1, le=1000000)):
    if item_id < 1 or item_id > 1000000:
        raise HTTPException(status_code=400, detail='item_id out of allowed range')
    try:
        resp = table.get_item(Key={'id': item_id})
    except ClientError as e:
        raise HTTPException(status_code=500, detail='dynamodb error')
    item = resp.get('Item')
    if not item:
        raise HTTPException(status_code=404, detail='not found')
    return item

For paginated reads, validate the ExclusiveStartKey and ensure it conforms to the table’s key schema. Avoid passing raw user cursors directly to Dynamodb; instead, map them to validated values and enforce a maximum page size.

from fastapi import FastAPI, Query
import boto3
from pydantic import ValidationError

app = FastAPI()
ddb = boto3.resource('dynamodb', region_name='us-east-1')
table = ddb.Table('ItemsTable')

@app.get('/items')
def list_items(limit: int = Query(10, ge=1, le=100), start_key: str | None = None):
    if limit < 1 or limit > 100:
        raise HTTPException(status_code=400, detail='limit out of range')
    kwargs = {'Limit': limit}
    if start_key:
        try:
            # decode and validate start_key before using
            import base64, json
            decoded = json.loads(base64.b64decode(start_key).decode())
            if not isinstance(decoded.get('id'), int):
                raise ValidationError
            kwargs['ExclusiveStartKey'] = decoded
        except Exception:
            raise HTTPException(status_code=400, detail='invalid start_key')
    resp = table.scan(**kwargs)
    items = resp.get('Items', [])
    next_key = None
    if 'LastEvaluatedKey' in resp:
        next_key = base64.b64encode(json.dumps(resp['LastEvaluatedKey']).encode()).decode()
    return {'items': items, 'next_key': next_key}

middleBrick’s CLI can be used to verify these fixes by running middlebrick scan <url> and reviewing the input validation and BOLA/IDOR findings. The dashboard helps track improvements over time, while the GitHub Action can enforce a minimum score before merges. In the Pro plan, continuous monitoring ensures that regressions are caught early, and findings map to frameworks such as OWASP API Top 10 and SOC2.