Insecure Deserialization in Chi with Jwt Tokens

Insecure deserialization occurs when an application processes untrusted data without sufficient validation, allowing attackers to manipulate object graphs during reconstruction. In Chi, routes are typically built as functions that deserialize incoming request data (e.g., JSON bodies or query parameters) into structured types before business logic executes. When JWT tokens are used, developers often rely on libraries that decode and parse the token payload into maps or custom structs. If the application directly deserializes values from the decoded JWT payload into complex objects or uses reflection-based deserialization on user-controlled claims, an attacker may supply crafted payloads that trigger unexpected behavior during deserialization.

Consider a Chi application that decodes a JWT token and then deserializes a claim such as user_data into a user-defined struct. If the token’s payload contains serialized objects or references to types that the application reconstructs, and if no strict schema validation is applied, an attacker can embed malicious gadget chains or type information. For example, a token payload like { "user_data": { "@type": "evil.RogueType", "data": "..." } } may lead to arbitrary code execution when the server-side deserializer attempts to reconstruct the object. Even without direct gadget chains, tampering with claims like roles or permissions inside the JWT and then deserializing them into enumerated types can lead to privilege escalation or logic flaws.

Chi itself does not enforce deserialization strategies; the risk emerges from how developers handle the decoded JWT claims. Common patterns include unmarshalling the token payload into a map[string]interface{} and then asserting specific keys into structs. If assertions are too permissive or if interface values are passed to functions that perform further deserialization (e.g., via custom unmarshalers or external libraries), the application may inadvertently process malicious content. This is especially relevant when the JWT is used to carry session-like state or object references, effectively turning the token into a conduit for serialized data that the server reconstructs.

Real-world attack patterns mirror OWASP API Top 10 #1: Broken Object Level Authorization, where insecure deserialization enables attackers to bypass authorization checks embedded in object graphs. For instance, a token claiming role: admin might be deserialized into an object where internal flags are manipulated due to missing validation. Although JWTs are typically signed, signature verification does not prevent unsafe deserialization of the payload’s content. Tools scanning endpoints via middleBrick can detect endpoints that accept JWTs with suspicious claims and flag insecure deserialization risks by correlating OpenAPI definitions with runtime behavior, highlighting mismatches between expected and actual data handling.

Additionally, reflection-based or interface-heavy deserialization in handlers increases the attack surface. If a Chi handler passes decoded JWT values into a generic deserializer that resolves types dynamically, an attacker may exploit known gadget chains in the language runtime (e.g., in Go, certain type switches and interface conversions). This can lead to unauthorized actions or information disclosure, even when the token is cryptographically valid. Because JWTs are often used for stateless session representation, developers may overlook the need to strictly validate and limit the deserialization scope of their contents.

Remediation focuses on strict validation and avoiding unsafe deserialization of JWT payload contents. Instead of deserializing claims into arbitrary types, define explicit structs and validate each field. Treat the JWT payload as an untrusted data source and never pass raw decoded values directly into functions that perform further deserialization or reflection-based processing.

// Unsafe pattern to avoid: deserializing claims into interface{}
var raw map[string]interface{}
if err := newClaim.Claims(&raw); err != nil {
    http.Error(w, "invalid token", http.StatusBadRequest)
    return
}
userData, ok := raw["user_data"]
if !ok {
    http.Error(w, "missing user_data", http.StatusBadRequest)
    return
}
// Risk: userData may contain serialized objects or malicious types

// Safe pattern: use a strongly typed struct with explicit validation
type UserClaims struct {
    UserID   string `json:"user_id" validate:"required,uuid"`
    Role     string `json:"role" validate:"oneof=admin user guest"`
    Email    string `json:"email" validate:"required,email"`
    IssuedAt int64  `json:"iat"`
}

var claims UserClaims
if err := newClaim.Claims(&claims); err != nil {
    http.Error(w, "invalid token", http.StatusBadRequest)
    return
}
if err := validator.Struct(claims); err != nil {
    http.Error(w, "validation failed", http.StatusBadRequest)
    return
}
// Now claims.Role is safe to use in authorization checks

When using JWTs in Chi, always decode into concrete types and apply field-level validation. Avoid libraries that allow dynamic claim parsing into maps or interfaces unless you explicitly sanitize and validate every value. For roles or permissions stored in claims, use enumerated types and strict allow-lists rather than raw strings that could be deserialized into harmful object states.

Additionally, enforce strict type checks before using claims in business logic. For example, if a claim indicates a resource ID, parse it as a UUID or integer and verify it against the current user’s permissions via a separate authorization layer, rather than trusting the deserialized object graph. MiddleBrick scans can help identify endpoints where JWT payloads are deserialized in ways that map to insecure patterns, providing findings aligned with OWASP API Top 10 and compliance frameworks.

For applications that must handle opaque token data, keep JWTs as opaque strings and store server-side mappings (e.g., in a cache) rather than embedding serialized objects in claims. This reduces the need for deserialization of JWT contents altogether. If you use the Pro plan, continuous monitoring and GitHub Action integration can automatically detect when new endpoints introduce risky deserialization patterns, helping you fail builds before insecure code reaches production.