Cache Poisoning in Restify (Javascript)

Cache poisoning in Restify when using JavaScript occurs when an attacker manipulates cache keys or responses so that malicious or sensitive data is served to subsequent users. This typically arises when route or query parameters are directly used to construct cache identifiers without normalization or validation, allowing one user’s request to overwrite another’s cached response.

Restify does not provide built-in HTTP caching for dynamic routes by default, but developers often layer a caching solution (for example an in-memory store or external cache) in JavaScript to improve performance. If the cache key is derived unsafely from user-controlled inputs such as req.url or unvalidated headers, an attacker can inject crafted query strings or path segments that cause the server to cache a response containing another user’s data or an unintended representation of a resource.

Consider an endpoint that caches user profile details keyed by the full request URL. A request to /profile?user_id=123 might be cached under that exact URL. Without input validation, an attacker could request /profile?user_id=123&evil=true, and the cache may serve that poisoned response to other users whose requests normalize differently. Because the logic is written in JavaScript and executed in the Node.js runtime, any shared state in the caching layer can become a vector if keys are not scoped carefully.

SSRF and external dependency risks can compound cache poisoning in JavaScript services. If your Restify server fetches data from an upstream service and caches the result based on a non-sanitized parameter, an attacker may force the server to retrieve internal or sensitive data and store it in the cache. This exposes data across users and can facilitate horizontal privilege escalation when cached responses are served to accounts that should not see that data.

Because middleBrick scans unauthenticated attack surfaces and tests input validation and data exposure, it can surface cache poisoning risks related to how parameters influence caching behavior in JavaScript implementations. The scanner evaluates whether distinct inputs produce distinct cache behaviors and whether sensitive data could be stored and later returned through the same endpoint.

To mitigate cache poisoning in Restify with JavaScript, normalize and strictly validate all inputs that participate in cache keys. Avoid directly using raw query strings or mutable request properties as the sole cache identifier. Instead, derive a deterministic, scoped key from validated parameters and include tenant or user context where appropriate to prevent cross-user cache pollution.

Example of a vulnerable implementation:

server.get('/profile', (req, res, next) => {
  const userParam = req.query.user_id;
  const cacheKey = `profile:${req.url}`; // unsafe: includes raw query string
  const cached = cache.get(cacheKey);
  if (cached) {
    return res.send(cached);
  }
  db.getUser(userParam, (err, user) => {
    if (err) return next(err);
    cache.set(cacheKey, user); // may cache attacker-controlled key
    res.send(user);
    return next();
  });
});

An attacker can supply additional query parameters that alter the cache key and cause cache collisions or data leakage. The following remediation normalizes inputs and scopes the cache key to the validated user identifier:

const validateUserId = (id) => /^[0-9a-fA-F]{24}$/.test(id); // ObjectId pattern example

server.get('/profile', (req, res, next) => {
  const userId = req.query.user_id;
  if (!validateUserId(userId)) {
    return next(new Error('Invalid user identifier'));
  }
  const cacheKey = `profile:user:${userId}`; // scoped, deterministic key
  const cached = cache.get(cacheKey);
  if (cached) {
    return res.send(cached);
  }
  db.getUser(userId, (err, user) => {
    if (err) return next(err);
    cache.set(cacheKey, user);
    res.send(user);
    return next();
  });
});

When your Restify service calls external JavaScript libraries or microservices and caches their responses, ensure that any parameters forwarded upstream are also validated and that cache keys incorporate the intended scope. This reduces the likelihood that poisoned inputs propagate into cached data that affects other users.

For automated detection of such issues, middleBrick can be used to scan your API endpoints. Its checks for input validation and data exposure help identify whether distinct inputs can affect caching behavior or cause cross-user data leakage. The CLI allows you to integrate scans into scripts, while the GitHub Action can fail builds if new endpoints introduce risky caching patterns.