Cache Poisoning in Loopback with Dynamodb

Cache poisoning in a Loopback application that uses DynamoDB can occur when responses from the database are stored in a cache and later served to different users or requests without revalidation. Because Loopback often caches query results to reduce repeated calls to DynamoDB, an attacker who can influence the cache key or the data returned may cause malicious or sensitive data to be persisted and reused.

DynamoDB does not inherently provide cache controls such as cache‑validation headers or automatic cache partitioning. If the application uses user‑controlled input—such as request parameters, headers, or context identifiers—to build cache keys, an attacker may force the storage of attacker‑controlled data under a key that will later be read by another user. For example, a query that includes a user ID in the key but does not fully isolate tenant or session context may allow one user’s data to be cached and subsequently returned to another user, effectively exposing confidential information through the cache.

The risk is compounded when the cached data includes sensitive attributes or when the cache layer is shared across multiple services or instances. Because Loopback middleware may normalize inputs differently across endpoints, the same logical query can result in different cache keys, leading to inconsistent cache behavior. In a black‑box scan, such inconsistencies can be detected as potential data exposure or authorization issues, where cached responses bypass intended access controls.

Additionally, if DynamoDB responses contain sensitive metadata or if the application caches error responses, an attacker may manipulate inputs to cause error messages or crafted data to be stored. Subsequent requests that retrieve the poisoned cache entry may inadvertently disclose information or cause logic errors. This pattern aligns with common OWASP API Top 10 categories related to broken object level authorization and excessive data exposure, and it may intersect with BOLA/IDOR findings when cached data is improperly scoped.

Because middleBrick tests unauthenticated attack surfaces and includes checks for Data Exposure and Authorization, it can flag inconsistencies in caching behavior that suggest cache poisoning risks. The scanner does not fix the issue, but it provides prioritized findings with remediation guidance to help developers address the root cause in the Loopback‑DynamoDB integration.

To reduce cache poisoning risk when using DynamoDB with Loopback, ensure that cache keys are deterministic, tenant‑isolated, and do not rely on mutable or attacker‑controlled values without strict validation. The following practices and code examples illustrate secure integration patterns.

1. Isolate cache keys by tenant and user context

Include a verified tenant identifier and a normalized user identifier in the cache key, rather than raw request parameters. Avoid using IDs supplied directly by the client for cache key construction.

const crypto = require('crypto');
function buildCacheKey(tenantId, userId, querySpec) {
  const normalized = {
    query: querySpec.query || {},
    select: querySpec.select || null,
    filter: querySpec.filter || null
  };
  const canonical = JSON.stringify(normalized);
  const hash = crypto.createHash('sha256').update(canonical).digest('hex');
  return `tenant:${tenantId}:user:${userId}:dynamodb:${hash}`;
}
// Example usage within a Loopback repository method
MyModel.cachedQuery = async function(tenantId, userId, querySpec, cache) {
  const key = buildCacheKey(tenantId, userId, querySpec);
  const cached = await cache.get(key);
  if (cached) return cached;
  const result = await this.find({
    where: querySpec.query,
    fields: querySpec.select,
  });
  await cache.set(key, result, { ttl: 300 });
  return result;
};

2. Validate and sanitize DynamoDB attribute names

Because DynamoDB allows dynamic attribute selection, ensure that projection expressions or key condition expressions are validated against a whitelist. This prevents attackers from injecting attribute names that alter cached response contents.

const ALLOWED_FILTER_ATTRIBUTES = new Set(['id', 'status', 'createdAt', 'ownerId']);
function validateFilterAttributes(filter) {
  if (!filter) return filter;
  return Object.keys(filter).reduce((acc, key) => {
    if (ALLOWED_FILTER_ATTRIBUTES.has(key)) {
      acc[key] = filter[key];
    }
    return acc;
  }, {});
}

// In a repository method
MyModel.findByStatus = async function(tenantId, userId, filter, cache) {
  const safeFilter = validateFilterAttributes(filter);
  const key = buildCacheKey(tenantId, userId, { query: safeFilter });
  return cache.wrap(key, async () => {
    return this.find({ where: safeFilter });
  });
};

3. Use per‑tenant cache namespaces and TTLs aligned with data sensitivity

Configure separate cache namespaces for different data sensitivity levels and enforce TTLs that reflect the volatility and confidentiality of the underlying DynamoDB items. Avoid long TTLs for data that changes frequently or contains PII.

// Example cache configuration with namespaces
const cache = {
  tenantNamespace: (tenantId) => `cache:tenant:${tenantId}`,
  get: async (key) => { /* retrieve from distributed cache */ },
  set: async (key, value, options) => { /* store with TTL */ },
  wrap: async (key, fn, ttl) => {
    const cached = await cache.get(key);
    if (cached !== undefined) return cached;
    const value = await fn();
    await cache.set(key, value, { ttl });
    return value;
  }
};

// Usage with tenant scoping
MyModel.safeQuery = async function(tenantId, userId, querySpec) {
  const key = buildCacheKey(tenantId, userId, querySpec);
  const ttl = querySpec.containsPii ? 60 : 300;
  return cache.wrap(key, () => this.find({ where: querySpec.query }), ttl);
};

4. Disable caching for sensitive or highly dynamic endpoints

For endpoints that return sensitive or rapidly changing data, disable caching entirely at the repository or route level to eliminate poisoning risk.

MyModel.disableCache = true;
MyModel.findWithoutCache = async function(tenantId, userId, whereClause) {
  // Bypass any cache wrapper
  return this.find({ where: whereClause });
};

By combining tenant‑aware cache keys, strict attribute validation, appropriate namespace isolation, and selective disabling of caching, you can significantly reduce the attack surface for cache poisoning in a Loopback application that relies on DynamoDB. These changes align with the remediation guidance provided in middleBrick findings and help address related checks such as Data Exposure and BOLA/IDOR.