Use After Free in Buffalo with Jwt Tokens

Use After Free (UAF) in the context of Buffalo applications that handle JWT tokens occurs when memory associated with a token or its parsed representation is released but later accessed during request processing. This typically arises when developers store parsed JWT claims in request-scoped objects, session caches, or context values and then fail to ensure the underlying memory remains valid for the duration of the request. Because Buffalo is a Go web framework, the risk is not heap use-after-free in the runtime itself, but logical use-after-free patterns where references outlive the data they depend on, leading to unpredictable behavior or information exposure.

Consider a Buffalo app that decodes a JWT token to extract user claims and attaches them to the request context for downstream handlers. If the token payload contains sensitive fields and the application retains a pointer or reference to the decoded claims beyond the handler’s completion, concurrent requests or middleware may observe stale or overwritten data. For example, a handler might reuse a buffer or a struct to unmarshal tokens in a loop, and if the context stores a reference to that buffer, later requests retrieving the context value may see data from a previous, logically freed token. This is a logical UAF: the memory may have been reallocated for another purpose, and the stale reference now points to data that no longer represents the original token.

Another scenario involves JWT token validation where the application caches public key material or parsed token objects to avoid repeated computation. If the cache eviction or replacement logic is flawed, a token may be evicted or freed while a handler is still using a borrowed reference. Because Buffalo encourages composing handlers with middleware, a token-validation middleware might store a parsed token in the context for authorization checks in downstream handlers. If the context implementation or middleware chain inadvertently holds a reference after the token’s logical lifetime ends, downstream code might read corrupted claims, including the subject or roles, leading to privilege escalation or unauthorized access patterns analogous to BOLA/IDOR.

JWT tokens themselves do not cause memory safety issues, but the way Buffalo applications manage token lifetimes and associated data can introduce UAF-like conditions. Because JWTs are often used to carry identity and permissions, a UAF on token data can expose sensitive claims or lead to incorrect authorization decisions. For instance, if a handler unmarshals a token into a struct and then passes a pointer to that struct into a closure that executes after the handler returns, the struct may be garbage collected or reused, and the closure might read arbitrary memory. This mirrors the impact of insecure deserialization or improper scoping found in OWASP API Top 10, where trust is placed in data that should not be assumed valid.

In practice, the risk is amplified when applications use global or long-lived caches keyed by token identifiers without ensuring proper synchronization and lifetime management. A token parsed once and stored for reuse across requests may reference buffers that have been released, especially under high concurrency. Because Buffalo does not inherently manage these lifetimes, developers must ensure that any data derived from JWT tokens is either immutable for the required scope or explicitly copied. The scanner in middleBrick can surface these risks by detecting insecure token handling patterns and missing authorization checks, mapping them to relevant findings such as BOLA/IDOR and Property Authorization.

Real-world attack patterns may not directly exploit memory corruption in Go, but they exploit the logical UAF by manipulating token scope and context propagation. For example, an attacker could induce race conditions by sending rapid requests that trigger token reuse, causing handlers to observe claims from other users. This parallels Insecure Consumption and Unsafe Consumption checks, where data is used beyond its safe boundary. The presence of LLM/AI Security probes in middleBrick also highlights how exposed token-handling logic might be leveraged in more complex multi-step attacks if the application’s control flow depends on tainted data.

Remediation focuses on ensuring that data derived from JWT tokens does not outlive its intended scope and that copies, not references, are stored in contexts or caches. Below are concrete code examples for Buffalo that demonstrate safe handling of JWT tokens.

First, decode the token and immediately copy the claims into a new struct, then store that copy in the context. Avoid storing pointers to stack-allocated structs or reusing buffers across requests.

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

func TokenClaimsMiddleware(next buffalo.Handler) buffalo.Handler {
    return func(c buffalo.Context) error {
        auth := c.Request().Header.Get("Authorization")
        if auth == "" {
            return c.Render(401, r.JSON(map[string]string{"error": "missing authorization"}))
        }
        tokenString := auth[len("Bearer "):]
        token, err := jwt.Parse(tokenString, func(token *jwt.Token) (interface{}, error) {
            // TODO: use proper key retrieval
            return []byte("secret"), nil
        })
        if err != nil || !token.Valid {
            return c.Render(401, r.JSON(map[string]string{"error": "invalid token"}))
        }
        // Copy claims into a new map to avoid reference reuse
        claimsCopy := make(map[string]interface{})
        if claims, ok := token.Claims.(jwt.MapClaims); ok {
            for k, v := range claims {
                claimsCopy[k] = v
            }
        }
        // Store the copy in context, not the original token or mutable reference
        c.Set("jwt_claims", claimsCopy)
        return next(c)
    }
}

Second, when using context values downstream, retrieve a copy and do not rely on the stored reference beyond the request scope. This prevents accidental use of data that might be overwritten by subsequent requests in a pooled environment.

func ProfileHandler(c buffalo.Context) error {
    claimsIface := c.Get("jwt_claims")
    claims, ok := claimsIface.(map[string]interface{})
    if !ok {
        return c.Render(400, r.JSON(map[string]string{"error": "invalid claims"}))
    }
    // Work with a copy; do not store or forward the map beyond this handler
    userID, ok := claims["sub"].(string)
    if !ok {
        return c.Render(400, r.JSON(map[string]string{"error": "missing subject"}))
    }
    // Safe usage: userID is a primitive value (string), claims are not reused
    return c.Render(200, r.JSON(map[string]string{"user_id": userID}))
}

Third, avoid global or long-lived caches for raw token objects or parsed mutable maps. If caching is necessary, store immutable snapshots and enforce strict TTLs and eviction policies. For key material, load public keys per-request or use a sync.Pool with size limits to prevent unbounded growth and accidental cross-request exposure.

var keyCache = struct {
    sync.RWMutex
    keys map[string]interface{}
}{
    keys: make(map[string]interface{}),
}

func GetPublicKey(keyID string) (interface{}, error) {
    keyCache.RLock()
    key, exists := keyCache.keys[keyID]
    keyCache.RUnlock()
    if exists {
        return key, nil
    }
    // Fetch and store a copy; in practice, rotate and limit cache size
    pubKey, err := fetchPublicKeyFromJWKS(keyID)
    if err != nil {
        return nil, err
    }
    keyCache.Lock()
    keyCache.keys[keyID] = pubKey
    keyCache.Unlock()
    return pubKey, nil
}

Additionally, configure the JWT parser to reject tokens with unexpected algorithms and to enforce strict validation, reducing the chance of accepting malformed tokens that could exacerbate lifetime issues. Combine these practices with the application of middleBrick scans, which can identify missing Property Authorization and BOLA/IDOR risks in token-based endpoints. The CLI tool (middlebrick scan <url>) and GitHub Action can be integrated into CI/CD to detect insecure token handling before deployment, while the Web Dashboard helps track improvements over time.

Finally, ensure that any middleware or handlers that depend on JWT claims do not inadvertently retain references in global variables or long-lived goroutines. By copying claims, validating scopes on each use, and avoiding unsafe sharing, you mitigate logical UAF scenarios and align with secure patterns recognized by frameworks like OWASP API Top 10 and compliance mappings available in the Pro plan for continuous monitoring.