Memory Leak in Fastapi with Dynamodb
Memory Leak in Fastapi with Dynamodb — how this specific combination creates or exposes the vulnerability
A memory leak in a FastAPI application that uses DynamoDB typically arises from resource management issues on the application side, since DynamoDB itself is a managed service and does not leak memory in the process sense. When FastAPI keeps long-lived references to DynamoDB resources—such as low-level clients, paginated result sets, unclosed asynchronous context, or cached data—the runtime process retains memory that is never released back to the operating system. This pattern is common when developers instantiate a boto3 client or resource at the module level and reuse it across requests without considering lifecycle, or when iterating through large scan/query results without pagination control or streaming cleanup.
In a black-box scan, middleBrick tests the unauthenticated attack surface and does not inspect runtime memory, but it can identify indicators that may correlate with misconfiguration, such as missing pagination controls on endpoints that return large datasets, missing request-level resource handling, or overly permissive input that leads to expensive operations. For example, an endpoint that performs a full table scan without limiting result size or using pagination can cause the application layer to accumulate many items in memory, leading to increased memory usage over time. middleBrick also runs checks such as Input Validation and Rate Limiting, which can highlight endpoints that are susceptible to abusive queries that exacerbate memory pressure.
With DynamoDB, specific triggers include: using Scan without FilterExpression or Limit and loading entire segments into a list; repeatedly creating and discarding high-level resources in short-lived processes without proper cleanup in async contexts; and caching large items or query sets in global structures. These patterns do not directly expose credentials via the API, but they degrade performance and availability, which can indirectly affect security by making the service more susceptible to denial-of-service conditions. The LLM/AI Security checks in middleBrick do not analyze runtime memory but do verify whether endpoints expose behaviors that could be leveraged for resource exhaustion through prompt-driven interactions.
Dynamodb-Specific Remediation in Fastapi — concrete code fixes
Apply lifecycle-aware resource handling and efficient query patterns in FastAPI to avoid retaining memory across requests. Use dependency injection to share a single boto3 resource safely, paginate or stream large result sets, and avoid caching unbounded data in global structures.
Example: Safe DynamoDB resource reuse with FastAPI dependency injection
from fastapi import Depends, FastAPI, HTTPException
import boto3
from botocore.exceptions import ClientError
app = FastAPI()
def get_dynamodb_resource():
# Create once and reuse across requests; boto3 manages its own connection pooling.
return boto3.resource("dynamodb", region_name="us-east-1")
@app.get("/items/{item_id}")
def read_item(item_id: str, db=boto3.resource("dynamodb", region_name="us-east-1")):
try:
item = db.Table("Items").get_item(Key={"id": item_id}).get("Item")
if item is None:
raise HTTPException(status_code=404, detail="Item not found")
return item
except ClientError as e:
raise HTTPException(status_code=500, detail=str(e))
Example: Paginated query to avoid loading entire result sets into memory
from fastapi import FastAPI
import boto3
app = FastAPI()
db = boto3.resource("dynamodb", region_name="us-east-1")
@app.get("/search")
def search_items(query: str):
table = db.Table("Items")
response = table.query(
KeyConditionExpression="pk = :v",
ExpressionAttributeValues={":v": query},
Limit=100 # enforce a reasonable page size
)
items = response.get("Items", [])
# If you need to handle more pages, use pagination with a loop and process one page at a time
return {"results": items}
Example: Streaming scan with pagination and explicit cleanup
from fastapi import FastAPI
import boto3
app = FastAPI()
db = boto3.resource("dynamodb", region_name="us-east-1")
@app.get("/export")
def export_table():
table = db.Table("LargeTable")
done = False
last_evaluated_key = None
total_count = 0
while not done:
if last_evaluated_key:
resp = table.scan(ExclusiveStartKey=last_evaluated_key, Limit=1000)
else:
resp = table.scan(Limit=1000)
items = resp.get("Items", [])
total_count += len(items)
# Process items in this page and release references when done
for item in items:
# e.g., write to file or forward to another system
pass
last_evaluated_key = resp.get("LastEvaluatedKey")
done = last_evaluated_key is None
return {"total_scanned": total_count}
Best practices summary
- Instantiate boto3 resources at an appropriate scope (application-level) and reuse them; avoid creating new clients/resources per request.
- Prefer Query with KeyConditionExpression over Scan; when Scan is necessary, use pagination and process items page-by-page without accumulating all pages in memory.
- Do not store large result sets or raw items in global variables or long-lived caches without bounds and eviction policies.
- Use explicit Limit and FilterExpression to reduce payload size; validate input parameters to prevent excessively large requests.
- In async contexts, ensure proper lifecycle management; if using async wrappers, follow library guidance to avoid reference retention.