Race Condition in Fiber with Dynamodb

A race condition in a Fiber application that uses DynamoDB typically arises when multiple concurrent requests read and write the same item without sufficient coordination, leading to lost updates or inconsistent state. For example, consider an endpoint that reads an item’s current version or balance, computes a new value, and then writes it back. If two requests perform the read concurrently, both see the same initial value; each applies its change and writes back, and the second write overwrites the first without incorporating its predecessor’s update. This is a classic time-of-check-to-time-of-use (TOCTOU) pattern in a serverless, high-concurrency environment where Fiber handles many simultaneous requests.

DynamoDB’s default isolation for strongly consistent reads helps, but many applications use eventually consistent reads for performance or rely on conditional expressions only at write time. If the client-side logic between read and write is non-atomic, the window for interference remains. Consider a reservation system or a decrementing inventory counter where stock is read, checked, and decremented. Without a conditional update that encodes the expected prior state, concurrent requests can decrement based on stale data, causing the counter to become incorrect (e.g., going negative or overselling).

In the context of security checks, such a race condition may expose BOLA/IDOR-like behavior when the item identifier is predictable and authorization checks are performed before the update without re-validating state at write time. An attacker could send parallel requests with different subjects but the same target resource, attempting to exploit timing differences to perform actions they should not be able to. Because DynamoDB does not inherently serialize application logic, the onus is on the client to make updates atomic. This is where conditional writes and versioning (e.g., using a version attribute or a condition expression) become essential to ensure integrity under concurrency.

Using the DynamoDB Document Client in a Fiber route without synchronization illustrates the risk:

// Risky: read-modify-write without atomic condition
app.Get('/counter/:id', async (c) => {
  const id = c.Params('id');
  const { data } = await docClient.get({ TableName: 'counters', Key: { id } }).promise();
  if (!data) return c.status(404).send('not found');
  const newVal = data.value + 1;
  await docClient.put({ TableName: 'counters', Item: { id, value: newVal } }).promise();
  return c.json({ value: newVal });
});

If two requests hit this route for the same id at nearly the same time, both may read value = 5, increment to 6, and both write 6, losing one increment. An attacker could drive this concurrency intentionally to manipulate counts or bypass business rules.

Middleware that performs authorization before the read can also be bypassed if the authorization decision does not account for the latest state. Because scans test unauthenticated attack surfaces and check BOLA/IDOR, a race condition can surface as an authorization bypass finding when two subjects compete for the same resource and timing alters which request succeeds.

To eliminate race conditions in Fiber with DynamoDB, make updates atomic by using conditional writes and versioning. Instead of read-modify-write, encode the expected previous state in a condition expression so DynamoDB rejects updates that do not match. This ensures only one writer can succeed when contention occurs.

A safe pattern uses an increment expression for numeric fields when possible. DynamoDB supports ADD for numeric increments, which is server-side and atomic:

// Safe: atomic increment using ADD
app.Get('/counter/:id', async (c) => {
  const id = c.Params('id');
  const { data } = await docClient.update({
    TableName: 'counters',
    Key: { id },
    UpdateExpression: 'ADD #val :inc',
    ExpressionAttributeNames: { '#val': 'value' },
    ExpressionAttributeValues: { ':inc': 1 },
    ReturnValues: 'UPDATED_NEW'
  }).promise();
  return c.json({ value: data?.value });
});

When you need to replace the entire item or enforce more complex constraints, use a version attribute and a condition expression. Read the item, include a version (or timestamp) in the condition, and increment the version on each write. This ensures a concurrent writer will fail if the version changed between read and write:

// Safe: conditional update with versioning
const { data } = await docClient.get({ TableName: 'items', Key: { id } }).promise();
if (!data) { return c.status(404).send('not found'); }
const expectedVersion = data.version;
const newState = { ...data, value: data.value + 10, version: expectedVersion + 1 };
try {
  await docClient.put({
    TableName: 'items',
    Item: newState,
    ConditionExpression: 'version = :expected',
    ExpressionAttributeValues: { ':expected': expectedVersion }
  }).promise();
  return c.json(newState);
} catch (err) {
  // ConditionalCheckFailedException indicates a conflict
  return c.status(409).send('conflict, please retry');
}

For inventory or balance scenarios where you must enforce non-negative constraints, combine ADD with a condition that prevents negative results. DynamoDB evaluates the condition after the arithmetic, so you can block overdrafts atomically:

// Safe: decrement with non-negative condition
await docClient.update({
  TableName: 'inventory',
  Key: { sku: 'ABC-123' },
  UpdateExpression: 'ADD quantity :delta',
  ConditionExpression: 'quantity + :delta >= :zero',
  ExpressionAttributeValues: { ':delta': -1, ':zero': 0 }
}).promise();

If you cannot use increment expressions, implement an ETag-based approach with the Document Client’s concurrencySupport option (when available) or manually manage a version attribute. Always prefer conditional updates over application-level locking, because distributed systems do not share memory. Note that scans will flag missing conditional logic on write operations as a BFLA/Privilege Escalation or BOLA risk when timing enables privilege manipulation.

In the dashboard and via the CLI (middlebrick scan <url>), these fixes will reduce findings related to race conditions by ensuring updates are atomic and authorization is re-validated at write time. For teams needing continuous coverage, the Pro plan provides ongoing monitoring so that regressions in concurrency handling are surfaced promptly.