Api Rate Abuse in Axum with Jwt Tokens

API rate abuse in Axum when JWT tokens are used involves an attacker sending many authenticated requests that all consume shared backend resources. Even though each request includes a valid JWT, the server can still be overwhelmed if rate limits are applied only at the token-check layer and not per identity or per scope. Axum routes typically validate a JWT in a request guard, then pass the request to business logic. If the rate limiter is placed before authentication, unauthenticated probes can still consume capacity. If it is placed after authentication but keyed only by IP or global counters, a single compromised or malicious token can generate many high-cost operations that exhaust thread pools, connection pools, or downstream quotas.

Consider an endpoint like POST /api/transfer protected by a JWT guard. An attacker who steals or guesses a token can issue bursts of transfers, each validated as legitimate by the JWT middleware. Because the token encodes identity (e.g., sub), but the server does not enforce per-identity rate limits, one token can trigger account lockouts, transaction fraud, or resource starvation. This is a BOLA/IDOR pattern when the token’s subject can act on other users’ resources, and it becomes rate-limit bypass when there is no per-subject throttling. OWASP API Top 10 2023’s Broken Object Level Authorization (BOLA) intersects with rate abuse because weak authorization checks allow a single token to iterate over identifiers, while missing rate limits enable those iterations at scale.

Another scenario involves token scope escalation. A token minted for read-only access might be reused to hit write-heavy endpoints if coarse-grained scopes are not enforced at the rate limiter. For example, a token with scope read:accounts repeatedly calling POST /api/transfer demonstrates unsafe consumption. Because Axum middleware stacks can execute in any order, if token validation runs before rate checks, the server performs expensive work only to discover later that the client exceeds its quota. This pattern shows how improper middleware ordering and lack of per-scope or per-identity throttling create exploitable windows.

Real-world attack patterns include token sharing among colluding clients, where many users share one stolen token to avoid per-user limits. Data exposure can occur when rate-limited endpoints leak information through timing differences or error messages, aiding an attacker in refining abuse strategies. The server may also face inflated costs if downstream services are metered per request. MiddleBrick’s LLM/AI Security checks highlight such misuse by probing for excessive agency patterns where token-based workflows enable repeated tool-like behavior without adequate governance.

To detect this during a scan, middleBrick runs unauthenticated tests to identify missing rate limits on authenticated endpoints, then correlates findings with JWT usage to highlight BOLA and unsafe consumption risks. The scanner does not fix the implementation but provides prioritized findings with remediation guidance, helping teams align with OWASP API Top 10 and compliance frameworks like SOC2 and PCI-DSS.

Remediation focuses on applying rate limits per identity and per scope, enforcing proper middleware ordering, and validating token claims before allowing costly operations. Below are concrete Axum examples that demonstrate these fixes.

1. Per-identity rate limiting with JWT claims

Extract the subject (sub) from the validated JWT and use it as a key for rate limiting. This prevents a single token from exhausting shared resources.

use axum::{routing::post, Router};
use axum::extract::Extension;
use jsonwebtoken::{decode, Algorithm, DecodingKey, Validation};
use std::collections::HashMap;
use std::sync::{Arc, Mutex};
use std::time::{Duration, Instant};

struct RateLimiter {
    windows: Mutex>>,
    max_requests: usize,
    window: Duration,
}

impl RateLimiter {
    fn new(max_requests: usize, window: Duration) -> Self {
        Self {
            windows: Mutex::new(HashMap::new()),
            max_requests,
            window,
        }
    }

    fn allow(&self, key: &str) -> bool {
        let now = Instant::now();
        let mut windows = self.windows.lock().unwrap();
        let entries = windows.entry(key.to_string()).or_default();
        entries.retain(|&t| now.duration_since(t) < self.window);
        if entries.len() < self.max_requests {
            entries.push(now);
            true
        } else {
            false
        }
    }
}

async fn transfer_handler() -> &'static str {
    "OK"
}

async fn auth_middleware(
    token: String,
    Extension(limiter): Extension<Arc<RateLimiter>>
) -> Result<(String, String), (StatusCode, String)> {
    let token_data = decode::(
        &token,
        &DecodingKey::from_secret("secret".as_ref()),
        &Validation::new(Algorithm::HS256),
    ).map_err(|_| (StatusCode::UNAUTHORIZED, "Invalid token".into()))?;
    let sub = token_data.claims.sub;
    if limiter.allow(&sub) {
        Ok((token, sub))
    } else {
        Err((StatusCode::TOO_MANY_REQUESTS, "Rate limit exceeded".into()))
    }
}

#[tokio::main]
async fn main() {
    let limiter = Arc::new(RateLimiter::new(10, Duration::from_secs(60)));
    let app = Router::new()
        .route("/api/transfer", post(transfer_handler))
        .layer(Extension(limiter))
        .route("/protected", post(|Extension((token, sub)): Extension<(String, String)>| async move {
            // business logic here
            format!("User: {}", sub)
        }));
}

This example decodes the JWT, extracts the subject, and enforces a sliding window rate limit per subject. It ensures that a compromised token cannot saturate the server.

2. Scope-aware rate limiting

Validate scopes in the token and gate high-cost endpoints by scope. If a token lacks the required scope, reject early to avoid unnecessary processing.

use axum::http::StatusCode;
use jsonwebtoken::{Claims as JwtClaims, decode, DecodingKey, Validation, Algorithm};

#[derive(Debug, serde::Deserialize)]
struct Claims {
    sub: String,
    scope: String,
    exp: usize,
}

async fn require_scope(
    token: String,
    required_scope: &str,
) -> Result<(String, String), (StatusCode, String)> {
    let data = decode::(
        &token,
        &DecodingKey::from_secret("secret".as_ref()),
        &Validation::new(Algorithm::HS256),
    ).map_err(|_| (StatusCode::UNAUTHORIZED, "Invalid token".into()))?;
    let scopes: Vec&lt;&str&gt; = data.claims.scope.split(' ').collect();
    if scopes.contains(&required_scope) {
        Ok((token, data.claims.sub))
    } else {
        Err((StatusCode::FORBIDDEN, "Insufficient scope".into()))
    }
}

async fn transfer_handler(
    Extension((token, sub)): Extension<(String, String)>
) -> String {
    format!("Transferred by {}", sub)
}

// In route definition:
// let transfer_route = post(transfer_handler)
//     .layer(Extension(require_scope(token, "write:transfers").await?));

This ensures that tokens with read-only scope cannot invoke write operations, mitigating scope escalation and unsafe consumption.

3. Middleware ordering and early rejection

Place authentication before expensive operations but ensure rate limiting is applied per identity after token validation. This prevents unauthenticated resource consumption while still protecting authenticated paths.

use axum::routing::post;
use axum::Router;
use axum::Extension;

async fn validate_token(token: String) -> Result<String, (StatusCode, String)> {
    // JWT validation logic here
    Ok(token)
}

async fn rate_limited_handler(
    Extension(user): Extension<String>
) -> String {
    user
}

let app = Router::new()
    .route("/api/action", post(|token: String| async move {
        let user = validate_token(token).await?;
        // proceed to rate-limited handler
        format!("Hello, {}", user)
    }));

Combine this with a per-user rate limiter as shown earlier to ensure that authenticated requests are both authorized and rate-limited correctly.

By tying rate limits to JWT identities and scopes, and ordering middleware to reject invalid or excessive requests early, teams can mitigate API rate abuse while preserving legitimate usage. middleBrick’s scans can highlight missing per-identity throttling and unsafe consumption findings, offering remediation guidance aligned with best practices.