Formula Injection in Buffalo with Jwt Tokens

Formula Injection occurs when untrusted data is interpreted as a formula or expression by a downstream system. In the Buffalo web framework, this pattern commonly arises when developer code embeds user-controlled input into contexts that are later evaluated or coerced into numeric or date/time values. When this happens in combination with JWT handling—where tokens or claims are parsed and used in business logic—the risk is that untrusted data can influence token validation, claim interpretation, or cryptographic material derivation.

Consider a Buffalo endpoint that accepts an id parameter, uses it to look up a resource, and then incorporates that value into a JWT claim such as exp or a custom numeric claim. If the input is not strictly validated and is cast using strconv.Atoi or similar, an attacker can supply a formula-like payload (e.g., 1000 + 100) that some intermediate library or template engine evaluates, leading to an inflated expiration or unexpected claim value. This can cause authorization logic to accept tokens that appear valid but have extended lifetimes or altered scopes.

In Buffalo, JWTs are typically managed via middleware that reads tokens from the Authorization header, validates signatures, and populates the request context with claims. If the application then uses values from those claims in further calculations—such as building session keys, constructing redirect URLs, or querying databases with numeric identifiers derived from claims—formula injection can manifest indirectly. For example, a developer might concatenate a user-influenced string with a secret to derive a signing key, enabling key confusion or brute-force conditions when the injected formula alters the effective key material.

Real-world attack patterns include using arithmetic payloads to manipulate claim-based rate limits or to bypass time-based checks. While Buffalo itself does not evaluate formulas, the surrounding ecosystem—libraries for JWT parsing, template rendering, and ORM mapping—may inadvertently interpret injected expressions. This is especially relevant when integrating with legacy services or when using plugins that perform implicit type conversions. The OWASP API Top 10 category '2023-A1: Improper Input Validation' aligns with this risk, as does the potential for privilege escalation when formula-derived values affect authorization decisions.

An actual example involves a token claim such as user_id being read as a string and then passed to strconv.Atoi. An attacker sends id=1000+100; if the framework or helper libraries evaluate this expression, the resulting integer may be 1100, allowing the attacker to escalate privileges by assuming another user’s identity. Even without direct evaluation, concatenation bugs—such as using user input in JWT jti (JWT ID) values that feed into cache keys or audit logs—can distort traceability and enable log injection or token replay.

Because middleBrick tests unauthenticated attack surfaces and runs checks such as Input Validation and Property Authorization in parallel, it can surface these risks by correlating JWT claim usage with parameter handling. Findings typically highlight places where numeric coercion, template rendering, or claim-based logic intersect with external input, providing prioritized guidance on strict parsing and context-aware escaping.

Remediation centers on strict input validation, avoiding implicit type conversions, and isolating JWT handling from user-controlled data. In Buffalo, ensure that any data used in JWT construction or validation is canonicalized and type-checked before use. Below are concrete code examples demonstrating secure practices.

1. Validating and Parsing Input Before JWT Claim Assignment

Always parse and validate incoming parameters individually. Do not allow expressions or concatenated strings to influence numeric claims.

import "strconv"
import "github.com/gobuffalo/buffalo"

func UsersUpdate(c buffalo.Context) error {
    idStr := c.Param("id")
    // Strict integer parsing without evaluation
    id, err := strconv.Atoi(idStr)
    if err != nil {
        return c.Render(400, r.JSON(map[string]string{"error": "invalid user id"}))
    }

    // Use the validated integer id in JWT claims
    claims := map[string]interface{}{
        "user_id": id,
        "exp":     time.Now().Add(time.Hour * 24).Unix(),
    }
    tokenString, err := jwt.NewWithClaims(jwt.SigningMethodHS256, claims).SignedString([]byte(os.Getenv("JWT_SECRET")))
    if err != nil {
        return c.Render(500, r.JSON(map[string]string{"error": "token generation failed"}))
    }
    return c.Render(200, r.JSON(map[string]string{"token": tokenString}))
}

2. Avoiding Formula Injection in JWT jti and Custom Claims

When generating JWT IDs or custom claims, ensure that values are sanitized and do not contain concatenated or evaluable content.

import "github.com/gobuffalo/buffalo"
import "github.com/dgrijalva/jwt-go"
import "github.com/google/uuid"

func ClaimsHandler(c buffalo.Context) error {
    // Generate a UUID-based jti to prevent injection and collisions
    jti := uuid.New().String()

    claims := jwt.MapClaims{
        "jti": jti,
        "sub": c.Session().Get("user_id"),
        "iat": time.Now().Unix(),
    }

    token := jwt.NewWithClaims(jwt.SigningMethodHS256, claims)
    tokenString, err := token.SignedString([]byte(os.Getenv("JWT_SECRET")))
    if err != nil {
        return c.Render(500, r.JSON(map[string]string{"error": "token signing error"}))
    }
    return c.Render(200, r.JSON(map[string]string{"token": tokenString}))
}

3. Securing JWT Verification Against Injection via Claims

When reading JWTs, validate claims strictly and do not re-incorporate claim values into evaluative contexts without sanitization.

import "github.com/gobuffalo/buffalo"
import "github.com/dgrijalva/jwt-go"

func VerifyToken(next buffalo.Handler) buffalo.Handler {
    return func(c buffalo.Context) error {
        tokenString := c.Request().Header.Get("Authorization")
        claims := jwt.MapClaims{}
        token, err := jwt.ParseWithClaims(tokenString, claims, func(token *jwt.Token) (interface{}, error) {
            return []byte(os.Getenv("JWT_SECRET")), nil
        })
        if err != nil || !token.Valid {
            return c.Render(401, r.JSON(map[string]string{"error": "invalid token"}))
        }

        // Example: safely using a numeric claim
        if userID, ok := claims["user_id"].(float64); ok {
            // Use userID in a safe, cast context
            _ = int(userID)
        } else {
            return c.Render(400, r.JSON(map[string]string{"error": "missing or invalid user_id claim"}))
        }
        return next(c)
    }
}

4. Middleware and Continuous Monitoring

Leverage middleBrick to scan your Buffalo endpoints regularly. The tool’s Input Validation and Property Authorization checks can identify places where formula injection risks intersect with JWT workflows. Using the Pro plan, you can enable continuous monitoring and CI/CD integration to fail builds if insecure patterns are detected in staging environments.

Additionally, the CLI tool (middlebrick scan <url>) and GitHub Action allow you to automate scans against your Buffalo staging URLs, ensuring that new endpoints or claim-based logic remain resistant to injection. The MCP Server can surface these findings directly in IDEs used by developers working on JWT-heavy services.