Out Of Bounds Write in Fiber with Jwt Tokens

An Out Of Bounds Write in a Fiber application that uses JWT tokens typically arises when token handling logic writes data into buffers or slices without proper length checks, and the token is treated as a source of controlled input that can be manipulated. This combination exposes a memory safety issue because the application may copy token claims or headers into fixed-size buffers based on attacker-controlled lengths derived from the token. For example, if a developer decodes a JWT and uses a numeric claim such as userId or a header parameter to allocate a buffer or to index into a slice, an attacker can supply an oversized value to trigger an out-of-bounds write. In Go, this often manifests through unsafe use of slices, such as creating a slice with a negative or excessively large capacity derived from token data, which can corrupt adjacent memory.

Because JWT tokens are commonly used for authentication and authorization, the attack surface is significant: an unauthenticated or partially authenticated request carrying a malicious token can exploit this flaw. A typical vulnerable pattern involves using token claims to determine request size limits or buffer sizes on the server, for instance by reading a custom claim like buffer_size and allocating a byte slice of that size without validating bounds. If the token is parsed but its claims are not strictly validated for size, an out-of-bounds write can occur when the application writes more bytes than the allocated buffer can hold. This may lead to memory corruption, which in some environments can be leveraged to escalate privileges or cause denial of service. The vulnerability is not in JWT itself but in how the application uses decoded token data to manage memory.

In the context of middleBrick’s security checks, this scenario would be flagged under Unsafe Consumption and Input Validation, since the token input is accepted without strict bounds checking. The scanner tests whether the application behaves correctly when presented with malformed or oversized tokens, looking for signs that token-derived values are used to control memory operations. Because JWT tokens often pass through multiple layers of parsing and reflection-based decoding, it is easy for developers to inadvertently trust claims that should be treated as untrusted input. A robust approach is to treat all token data as external input and validate numeric fields against strict, server-side limits before using them for memory allocation.

Real-world examples often involve custom middleware where developers extract an integer claim to size a buffer or loop. Consider a handler that reads a chunkSize claim and uses it to preallocate a slice. If the claim is not validated, an attacker can send a token with a huge chunkSize value, causing the application to allocate or write far beyond intended memory boundaries. This pattern is detectable via runtime analysis that correlates token parsing routines with memory operations, a capability included in advanced scans such as those offered by middleBrick’s CLI and Web Dashboard, which can help identify risky usage of JWT-derived data before deployment.

To remediate Out Of Bounds Write risks when handling JWT tokens in Fiber, always validate and sanitize any numeric or length-related claims before using them for memory allocation. Do not trust token claims for sizing buffers; instead, enforce server-side limits. Use strongly typed structures for claims and avoid reflection-based decoding that can be tricked into producing unexpected sizes. Below are concrete code examples showing vulnerable and fixed patterns.

Vulnerable Example

//go
package main

import (
    "github.com/gofiber/fiber/v2"
    "github.com/golang-jwt/jwt/v5"
)

func unsafeHandler(c *fiber.Ctx) error {
    tokenString := c.Get("Authorization")
    token, _, err := new(jwt.Parser).ParseUnverified(tokenString, jwt.MapClaims{})
    if err != nil {
        return c.Status(fiber.StatusBadRequest).JSON(fiber.Map{"error": "invalid token"})
    }
    if claims, ok := token.Claims.(jwt.MapClaims); ok {
        // Vulnerable: using untrusted claim for slice length
        if size, ok := claims["buffer_size"].(float64); ok {
            buf := make([]byte, int(size)) // attacker-controlled length
            // ... writing into buf without bounds check
            c.Send(buf[:10]) // risk of out-of-bounds write
        }
    }
    return c.SendString("ok")
}

Fixed Example

//go
package main

import (
    "errors"
    "github.com/golang-jwt/jwt/v5"
    "github.com/gofiber/fiber/v2"
)

const maxBufferSize = 1024

func safeHandler(c *fiber.Ctx) error {
    tokenString := c.Get("Authorization")
    token, _, err := new(jwt.Parser).ParseUnverified(tokenString, jwt.MapClaims{})
    if err != nil {
        return c.Status(fiber.StatusBadRequest).JSON(fiber.Map{"error": "invalid token"})
    }
    if claims, ok := token.Claims.(jwt.MapClaims); ok {
        // Safe: validate and constrain the size
        sizeFloat, ok := claims["buffer_size"].(float64)
        if !ok {
            return c.Status(fiber.StatusBadRequest).JSON(fiber.Map{"error": "invalid buffer_size claim"})
        }
        size := int(sizeFloat)
        if size <= 0 || size > maxBufferSize {
            return c.Status(fiber.StatusBadRequest).JSON(fiber.Map{"error": "buffer_size out of allowed range"})
        }
        buf := make([]byte, size)
        // Only write within safe bounds
        if len(buf) >= 10 {
            buf = buf[:10]
        }
        c.Send(buf)
    }
    return c.SendString("ok")
}

Best Practices Summary

• Validate numeric claims against predefined min/max limits before using them for allocations.
• Avoid using token data directly to determine memory sizes or loop bounds.
• Use structured claims with explicit types and required constraints rather than raw map access.
• Apply the principle of least privilege: limit the maximum allowed buffer size to what your application logic truly needs.

By combining input validation with careful memory handling, you reduce the risk of Out Of Bounds Write even when JWT tokens are involved. Tools like middleBrick’s CLI (run with middlebrick scan <url>) and Web Dashboard can help detect unsafe patterns by correlating token parsing with runtime behavior, providing findings with severity and remediation guidance to guide secure coding practices.