Memory Leak in Buffalo with Jwt Tokens

A memory leak in a Buffalo application that uses JWT tokens typically occurs when token payloads or session-related objects are retained in memory longer than necessary. Buffalo does not manage token lifecycle by default; developers are responsible for storing and cleaning up token state, especially when attaching user claims or session metadata to request contexts. If application code caches decoded tokens, stores them in global maps, or attaches large claims to request context without cleanup, each request can incrementally consume more memory. Over time, this leads to increased RSS and potential out-of-memory conditions under sustained load.

JWT tokens themselves are often small, but the way they are handled can introduce retention risks. For example, decoding a token and keeping the full claims map in a per-request cache, or storing tokens in session-like structures that are never invalidated, creates references that prevent garbage collection. Buffalo’s use of HTML templates and context objects can exacerbate this if developers place token payloads into the context for template rendering and fail to clear them between requests. In high-throughput scenarios, these retained maps or slices grow unbounded, causing gradual memory pressure. This is especially relevant when token payloads include large custom claims or when many concurrent users are authenticated without token invalidation or rotation.

Another contributing pattern is unbounded token introspection or logging. If your Buffalo app decodes tokens on every request and logs the full claims for debugging, those strings can accumulate in memory buffers or log structures. While JWTs are stateless, application-level state introduced around them can become a leak source. For instance, attaching decoded token data to a request-scoped object that is referenced by a long-lived background worker or global registry prevents the garbage collector from reclaiming memory. Without proper invalidation or size-bounded caches, this can trigger frequent garbage collection cycles and eventually degrade performance or cause crashes, which middleBrick may surface as a Data Exposure or Input Validation finding when unusual memory behavior is detected during scanning.

Additionally, middleware that decodes JWTs on every route without early exit or proper error handling can retain references to request bodies or context when errors occur. If error paths hold references to partially processed tokens or buffers, those references can linger until the next garbage collection cycle, and under continuous attack or malformed token barrage, the accumulation can become significant. Because middleBrick tests unauthenticated attack surfaces, it can detect abnormal memory-related behaviors tied to these patterns through its runtime checks, highlighting inconsistencies that suggest retention issues even though the scanner does not fix the leak.

Real-world attack patterns such as token replay or injection do not directly cause memory leaks, but the application’s response to them can. For example, if your Buffalo app allocates new data structures for each failed validation and does not release them promptly, repeated malformed JWT requests can contribute to memory growth. OWASP API Top 10 highlights security misconfiguration and excessive resource consumption as relevant concerns, and PCI-DSS expectations around stable runtime behavior align with ensuring token handling does not introduce resource exhaustion. middleBrick’s cross-referencing of OpenAPI specs with runtime findings helps correlate configuration choices that may predispose the service to retention issues.

To mitigate memory leaks when using JWT tokens in Buffalo, focus on minimizing retained state and ensuring timely cleanup. Avoid storing decoded tokens in long-lived maps or global caches. Instead, decode tokens per request, extract only required claims, and allow the request context to be garbage collected at the end of the request lifecycle. Use bounded caches if you must cache public key material or revocation status, and prefer primitive types over large structs when passing claims through the request context.

Example of safe JWT decoding in Buffalo without retaining references:

package actions

import (
	"github.com/gobuffalo/buffalo"
	"github.com/golang-jwt/jwt/v5"
)

func AuthenticatedUser(c buffalo.Context) error {
	auth := c.Request().Header.Get("Authorization")
	if auth == "" {
		return c.Error(401, errors.New("missing authorization header"))
	}
	tokenString := auth[len("Bearer "):]
	claims := make(jwt.MapClaims)
	token, err := jwt.ParseWithClaims(tokenString, claims, func(token *jwt.Token) (interface{}, error) {
		// fetch public key or secret appropriately per token
		return publicKey, nil
	})
	if err != nil || !token.Valid {
		return c.Error(401, errors.New("invalid token"))
	}
	// extract only needed fields, avoid attaching the entire claims map to context
	userID, ok := claims["sub"].(string)
	if !ok {
		return c.Error(400, errors.New("invalid subject claim"))
	}
	c.Set("user_id", userID)
	return c.Next()
}

Example with request-scoped cleanup and no global cache:

package actions

import (
	"github.com/gobuffalo/buffalo"
	"sync"
	"time"
)

// boundedCache is a simple size-bounded cache for public key material
type boundedCache struct {
	mu    sync.Mutex
	items map[string]interface{}
	order []string
	size  int
}

var keyCache = &boundedCache{items: make(map[string]interface{}), size: 100}

func (c *boundedCache) Get(key string) (interface{}, bool) {
	c.mu.Lock()
	defer c.mu.Unlock()
	val, ok := c.items[key]
	return val, ok
}

func (c *boundedCache) Add(key string, val interface{}) {
	c.mu.Lock()
	defer c.mu.Unlock()
	if len(c.order) >= c.size {
		oldest := c.order[0]
		c.order = c.order[1:]
		delete(c.items, oldest)
	}
	c.items[key] = val
	c.order = append(c.order, key)
}

func getPublicKey(keyID string) (interface{}, error) {
	if pk, found := keyCache.Get(keyID); found {
		return pk, nil
	}
	// simulate fetching and caching public key
	pk := /* fetch from JWKS or similar */ struct{}{}
	keyCache.Add(keyID, pk)
	return pk, nil
}

func AuthenticatedUserWithCache(c buffalo.Context) error {
	auth := c.Request().Header.Get("Authorization")
	if auth == "" {
		return c.Error(401, errors.New("missing authorization header"))
	}
	tokenString := auth[len("Bearer "):]
	claims := make(jwt.MapClaims)
	token, err := jwt.ParseWithClaims(tokenString, claims, func(token *jwt.Token) (interface{}, error) {
		keyID, ok := token.Header["kid"]
		if !ok {
			return nil, errors.New("missing kid")
		}
		return getPublicKey(keyID.(string))
	})
	if err != nil || !token.Valid {
		return c.Error(401, errors.New("invalid token"))
	}
	// keep claims usage minimal; avoid assigning entire map to context
	if email, ok := claims["email"].(string); ok {
		c.Set("email", email)
	}
	return c.Next()
}

Ensure that any background workers or long-lived goroutines do not capture request-scoped token data. Prefer passing identifiers (user ID, email) rather than the full token or claims map. If you use session-like storage, explicitly invalidate entries on logout or token revocation. Regularly profile memory usage to confirm that token handling does not contribute to unbounded growth, and adjust caching strategies accordingly.