Stack Overflow in Buffalo with Jwt Tokens

When a Buffalo application running in the Buffalo environment uses JWT tokens for authentication, a Stack Overflow vulnerability can emerge through unbounded JSON or XML deserialization combined with oversized token handling. Buffalo does not impose a default limit on the size of HTTP request bodies, and if the application decodes JSON payloads or XML data directly into Go structures without size guards, an attacker can send a deeply nested or extremely large JWT-related payload that exhausts stack memory. This often occurs when the JWT is passed in an Authorization header or inside a JSON body that the framework binds to a struct.

Buffalo’s middleware stack allows developers to plug authentication handlers easily; if a JWT verification middleware is invoked on every request and the token payload is processed without limiting nested claims or recursive parsing, the deserialization step can trigger a stack overflow. This is not a flaw in Buffalo itself but a result of unbounded input handling when JWT claims are mapped into complex structs or when third-party libraries recursively traverse claims. In a real exploit scenario, an attacker sends a token with deeply nested {"a": {"a": {"a": ...}}} structures or an XML external entity (XXE) payload if XML parsing is enabled, causing the service to consume increasing stack space and eventually crash or become unresponsive.

Because middleBrick scans the unauthenticated attack surface, it can detect indicators of an exposed deserialization surface tied to JWT handling—such as verbose error messages, absence of request size limits, and endpoints that accept JSON/XML without constraints—and surface related findings under Input Validation and Data Exposure checks. When combined with the LLM/AI Security probes, the scanner also verifies whether JWT validation logic is reachable without authentication and whether token processing paths risk resource exhaustion.

To mitigate Stack Overflow risks when using JWT tokens in Buffalo, constrain input sizes early in the request lifecycle and avoid unbounded deserialization of token claims. Apply request size limits before binding, use shallow claim extraction, and validate token structure without recursive traversal.

1) Limit request body size before parsing

Wrap the Buffalo application with a size-limiting middleware to reject overly large requests before they reach JWT parsing logic.

// app.go or a dedicated middleware file
package app

import (
    "net/http"
)

// maxRequestBody is set to 64 KiB, adjust as needed for your JWT use case.
const maxRequestBody = 64 * 1024

func LimitBodySize(next http.Handler) http.Handler {
    return http.HandlerFunc(func(w http.ResponseWriter, r *http.Request) {
        r.Body = http.MaxBytesReader(w, r.Body, maxRequestBody)
        next.ServeHTTP(w, r)
    })
}

Register the middleware globally or on authentication routes in actions/app.go:

func app() *buffalo.App {
    if app == nil {
        app = buffalo.New(buffalo.Options{})
        app.Use(LimitBodySize)
        // ... other middleware and routes
    }
    return app
}

2) Shallow JWT claim mapping and size checks

When decoding a JWT, avoid mapping the entire payload into deeply nested structs. Instead, extract only required claims and enforce limits on collection sizes.

// handlers/auth.go
package handlers

import (
    "context"
    "net/http"
    "strings"

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

type Claims struct {
    Sub string `json:"sub"`
    // Add only the claims you need; avoid embedding large nested maps.
}

func ValidateJWT(next buffalo.Handler) buffalo.Handler {
    return func(c buffalo.Context) error {
        auth := c.Request().Header.Get("Authorization")
        if auth == "" {
            return c.Error(http.StatusUnauthorized, errors.New("authorization header missing"))
        }
        parts := strings.Split(auth, " ")
        if len(parts) != 2 || parts[0] != "Bearer" {
            return c.Error(http.StatusUnauthorized, errors.New("invalid authorization format"))
        }
        token, err := jwt.ParseWithClaims(parts[1], &Claims{}, func(token *jwt.Token) (interface{}, error) {
            // supply your key or validator
            return []byte("your-secret-key"), nil
        })
        if err != nil {
            return c.Error(http.StatusUnauthorized, errors.New("invalid token"))
        }
        if claims, ok := token.Claims.(*Claims); ok && token.Valid {
            c.Set("user_id", claims.Sub)
            // Proceed; do not recursively process arbitrary claim maps.
            return next(c)
        }
        return c.Error(http.StatusUnauthorized, errors.New("invalid claims"))
    }
}

3) Avoid recursive processing of JWT claims

If your application must handle dynamic claims, iterate over a shallow map and enforce per-key length and count limits instead of allowing libraries to recurse into nested maps.

// handlers/claims.go
package handlers

import (
    "net/http"
    "github.com/gobuffalo/buffalo"
)

func SafeClaimsParse(c buffalo.Context) error {
    raw, ok := c.Get("jwt_claims_raw").(map[string]interface{})
    if !ok {
        return c.Error(http.StatusBadRequest, errors.New("claims format error"))
    }
    maxKeys := 100
    count := 0
    for k, v := range raw {
        if count > maxKeys {
            return c.Error(http.StatusRequestEntityTooLarge, errors.New("too many claims"))
        }
        // enforce shallow usage: reject nested maps/slices deeper than one level
        if _, ok := v.(map[string]interface{}); ok {
            return c.Error(http.StatusBadRequest, errors.New("nested claims not allowed"))
        }
        count++
        _ = v // use or log as needed
    }
    return nil
}

By combining request size limits, shallow claim extraction, and bounded iteration, you reduce the attack surface for Stack Overflow via JWT tokens in Buffalo. middleBrick can validate these mitigations by confirming the presence of size controls and the absence of recursive deserialization patterns in the scan results.