Memory Leak in Fiber with Jwt Tokens

A memory leak in a Fiber application that uses JWT tokens typically occurs when token handling logic retains references to request-scoped objects, large parsed claims, or cached data beyond the request lifecycle. In Go, memory that is still referenced cannot be garbage collected. When JWT parsing middleware or custom handlers hold onto context objects, claim maps, or byte slices, and those references are inadvertently stored in global caches, long-lived goroutines, or deferred closures, memory usage grows with each request. This pattern is common when developers store entire parsed token claims in request context for later use without cleaning up, or when token validation logic retains buffers or large string values in global variables for performance reasons.

Fiber’s efficient, high-performance design can inadvertently amplify these issues because the framework reuses context objects across requests in its pool. If a developer attaches a large JWT claims map to ctx.Locals or stores tokens in a global LRU cache without proper eviction, the accumulated data can lead to steady memory growth under sustained load. Unlike short-lived CLI tools, serverless functions, or scripts that exit quickly, a long-running Fiber service continuously processes requests, making these leaks observable over time as increased heap size, more frequent garbage collection pauses, or eventual out-of-memory conditions.

Another angle involves middleware ordering and token parsing libraries. Some JWT libraries allocate significant temporary memory when parsing compact token strings, especially when performing cryptographic verification. If the middleware does not release references to these allocations promptly, or if error paths retain partial token data in logs or panic handlers, memory can accumulate. This risk is higher when tokens carry extensive claims or when custom validation logic retains references for debugging purposes. Because the attack surface includes unauthenticated endpoints, an attacker could craft tokens with large payloads or many claims to accelerate memory consumption, leading to denial-of-service conditions even without authentication.

middleBrick’s scans identify such behavioral patterns by correlating authentication and authorization checks with resource consumption indicators. While the scanner does not profile runtime memory, it flags insecure token handling patterns, missing context cleanup, and missing rate limiting that can enable resource exhaustion. Findings include recommendations to scope data tightly, avoid long-lived references to request-specific objects, and apply rate limiting to reduce abuse potential.

Proper architectural practices mitigate these risks: ensure that request-scutable data is not stored in global or long-lived caches, explicitly nil out large objects when they are no longer needed, and design token validation to be stateless where possible. When state is required, use bounded caches with eviction policies and ensure that context cleanup is guaranteed in all code paths, including errors and cancellations.

Remediation focuses on eliminating unintended references and ensuring timely release of memory associated with JWT processing in Fiber. Below are concrete, idiomatic examples that demonstrate secure handling.

1. Avoid storing parsed claims in context

Instead of attaching the entire claims map to ctx.Locals, extract only the minimal required values and store those. This reduces retained graph size.

// Insecure: retains full claims map in context
claims := jwt.Parse(token, func(token *jwt.Token) (interface{}, error) { return key, nil })
ctx.Locals("claims", claims) // risk: large map kept for request lifetime

// Secure: extract only needed values
if sub, ok := claims.Claims.(jwt.MapClaims)["sub"].(string); ok {
    ctx.Locals("userID", sub) // store only the scalar identifier
}

2. Use request-scoped middleware with cleanup

Leverage Fiber’s Next() and explicit cleanup to ensure references are released even on error paths.

app.Use(func(c *fiber.Ctx) error {
    token := c.Get("Authorization")
    if token == "" {
        return c.SendStatus(fiber.StatusUnauthorized)
    }
    parsed, err := jwt.Parse(token, keyFunc)
    if err != nil || !parsed.Valid {
        return c.SendStatus(fiber.StatusUnauthorized)
    }
    // Minimal, scoped usage; no long-lived references
    userID, _ := parsed.Claims.(jwt.MapClaims)["sub"].(string)
    c.Locals("userID", userID)
    err = c.Next()
    // Explicit cleanup is not necessary in most Fiber versions,
    // but avoid adding large objects to c.Locals.
    return err
})

3. Prefer stateless validation and avoid global caches for tokens

If you must cache validation results, use bounded structures and avoid retaining tokens or claims.

var cache = lru.New(1000) // bounded cache

func validateToken(tokenString string) (bool, error) {
    if cached, ok := cache.Get(tokenString); ok {
        return cached.(bool), nil
    }
    parsed, err := jwt.Parse(tokenString, keyFunc)
    if err != nil {
        return false, err
    }
    valid := parsed.Valid
    cache.Add(tokenString, valid) // bounded cache with eviction
    return valid, nil
}

4. Control token size and claims complexity

Impose server-side limits on token payload size and claims count to reduce memory pressure per request. This complements rate limiting and helps prevent abuse via oversized tokens.

// Example: enforce a maximum token length before parsing
const maxTokenLength = 4096
if len(token) > maxTokenLength {
    return c.Status(fiber.StatusRequestEntityTooLarge).SendString("token too large")
}

5. Ensure error paths do not retain references

Avoid capturing token bytes or claims in error variables or logs that remain referenced. Use short-lived variables and avoid global debug accumulators.

func handler(c *fiber.Ctx) error {
    token := c.Get("Authorization")
    parsed, err := jwt.Parse(token, keyFunc)
    if err != nil {
        // Do not include parsed or token bytes in error messages
        return c.Status(fiber.StatusUnauthorized).SendString("invalid token")
    }
    defer func() {
        // Clear large locals if feasible; in practice, keep context minimal.
        _ = parsed.Claims // no-op example; ensure no external references held
    }()
    // Continue handling with minimal context usage
    return c.Next()
}

These patterns reduce the likelihood of retention and make garbage collection more effective. Combine them with monitoring and, where appropriate, integrate middleBrick’s CLI or GitHub Action to detect insecure token handling patterns in CI/CD before deployment.