Heap Overflow in Express with Dynamodb

A heap overflow in an Express service that uses DynamoDB typically arises when untrusted input directly influences how data is accumulated or serialized before being stored or sent to DynamoDB. Unlike languages with manual memory management, JavaScript on Node.js manages heap memory automatically, but logical overflows can still occur when unbounded input is concatenated, spread into arrays or objects, or used to control loops and buffer sizes before being passed to the DynamoDB Data SDK.

Consider an endpoint that builds a request object for DynamoDB based on user-supplied query parameters:

app.get('/items', (req, res) => {
  const filter = req.query.filter || '';
  const expression = `contains(#attr, '${filter}')`;
  const params = {
    TableName: 'Items',
    FilterExpression: expression,
    ExpressionAttributeNames: { '#attr': 'content' }
  };
  docClient.scan(params, (err, data) => {
    if (err) return res.status(500).send(err);
    res.json(data.Items);
  });
});

If filter is not validated, an attacker can supply a very long string that causes the resulting expression string to become huge, leading to excessive memory consumption during string interpolation and request building. This is a logical heap/array growth issue: the runtime must allocate larger contiguous structures to hold the expanded expression and the accumulated response items before the DynamoDB call is issued. In a chain, this can degrade performance or cause the process to spike memory usage, which may be observable via monitoring as an unstable pattern even though Node.js does not expose a traditional exploitable heap overflow.

The DynamoDB-specific exposure occurs when the inflated request leads to a scan or query with an overly permissive filter, returning far more items than intended. Each returned item adds to the response buffer, compounding memory pressure. In an unauthenticated scan, middleBrick tests such paths under the BFLA/Privilege Escalation and Unsafe Consumption checks, looking for missing validation that enables resource exhaustion or data exposure. The combination of Express routing, uncontrolled string building, and DynamoDB’s potentially large result sets creates a scenario where availability degrades and sensitive data can be pulled inadvertently.

Additionally, if the Express app serializes entire DynamoDB responses into client-facing JSON without size or field limits, an attacker who can cause larger scans (via injection or misconfigured filter) can force the server to allocate large output buffers, again stressing the heap. This aligns with middleBrick’s Data Exposure and Input Validation checks, which flag missing size constraints and unchecked user influence over query shape.

Remediation focuses on input validation, bounded query patterns, and safe handling of DynamoDB responses in Express. Always validate and sanitize user input before building expression strings, prefer parameterized expressions, and cap the number of returned items.

Secure implementation example:

const allowedFields = new Set(['name', 'status', 'type']);
app.get('/items', (req, res) => {
  const field = req.query.field;
  const value = req.query.value;
  if (!field || !allowedFields.has(field) || typeof value !== 'string' || value.length > 100) {
    return res.status(400).send('Invalid parameters');
  }
  const params = {
    TableName: 'Items',
    FilterExpression: '#attr = :val',
    ExpressionAttributeNames: { '#attr': field },
    ExpressionAttributeValues: { ':val': value },
    Limit: 50
  };
  docClient.scan(params, (err, data) => {
    if (err) return res.status(500).send(err);
    res.json(data.Items);
  });
});

This approach prevents expression injection and controls memory usage by bounding the filter string length and the result set size. Using parameterized expressions avoids concatenation of raw input into the expression, which is a key cause of logical overflows in query building.

When dealing with potentially large datasets, use pagination with ExclusiveStartKey instead of retrieving everything at once:

app.get('/items/page', async (req, res) => {
  const limit = Math.min(parseInt(req.query.limit) || 10, 100);
  const params = {
    TableName: 'Items',
    Limit: limit
  };
  try {
    const data = await docClient.scan(params).promise();
    res.json({ items: data.Items, lastKey: data.LastEvaluatedKey });
  } catch (err) {
    res.status(500).send(err);
  }
});

This keeps each response bounded and reduces heap pressure by never materializing the full dataset in memory. middleBrick’s Pro plan can be integrated into your workflow to continuously monitor such endpoints and flag missing limits or unsafe patterns via GitHub Action or dashboard alerts.

For LLM-related endpoints, apply the same discipline: do not concatenate user input into prompts that are sent to unauthenticated LLM endpoints, and validate prompt sizes. The LLM/AI Security checks in middleBrick specifically test for prompt injection and output exposure, which can complement secure DynamoDB handling by ensuring prompts themselves do not become vectors.