Integrity Failures in Axum with Cockroachdb

Integrity failures occur when an application fails to enforce data correctness and trust boundaries between user input, application logic, and the database. In an Axum application using Cockroachdb, these failures typically arise from missing validation, incorrect transaction usage, or improper handling of database constraints. Because Cockroachdb enforces strong consistency and SQL constraints, the risk surface shifts toward application-layer gaps rather than database weaknesses.

One common pattern is constructing SQL statements by string concatenation instead of using typed queries or parameter binding. For example, building an UPDATE statement with interpolated IDs or monetary values can lead to logical integrity violations, where one user’s data is updated with another’s values. In distributed Cockroachdb deployments, misconfigured transaction isolation or retry handling in Axum can cause lost updates or write skews, especially under concurrent load.

Another scenario involves optimistic concurrency control misuse. If an Axum handler reads a row, computes a new value, and writes it back without verifying the row hasn’t changed, concurrent requests can overwrite each other. With Cockroachdb’s serializable isolation, this may manifest as unexpected transaction aborts rather than silent corruption, but the application must still handle retries correctly. Failure to implement proper retry logic leads to integrity failures where operations appear successful while data becomes inconsistent.

Type and constraint mismatches between Rust models and Cockroachdb schema also contribute. If an Axum DTO allows null for a field that maps to a NOT NULL column without validation, the database will reject the transaction. While this is a safety feature, unhandled rejection paths can expose stack traces or lead to undefined behavior in the application layer. Similarly, using incorrect SQL numeric or timestamp types can cause truncation or timezone-related integrity drift.

These combinations highlight that integrity in Axum with Cockroachdb depends on disciplined use of strongly-typed queries, explicit transactions, constraint-aware modeling, and robust retry strategies. The scanner checks for these classes of issues under BOLA/IDOR, Input Validation, and Property Authorization, mapping findings to OWASP API Top 10 and SOC2 control expectations.

Remediation focuses on using parameterized SQL, explicit transactions with correct retry semantics, and schema-aware validation. Below are concrete, working examples for an Axum handler that updates a user’s account balance safely.

1. Parameterized queries with sqlx and Cockroachdb

Always use query parameters instead of string interpolation. This prevents SQL injection and ensures type-safe communication with Cockroachdb.

use sqlx::postgres::PgPoolOptions;
use sqlx::Row;
use axum::{routing::get, Router};

#[derive(Debug, sqlx::FromRow)]
struct Account {
    id: i64,
    balance: i64, // stored in cents to avoid floating-point issues
}

async fn get_account(pool: &sqlx::PgPool, user_id: i64) -> Result {
    sqlx::query_as!(Account, "SELECT id, balance FROM accounts WHERE id = $1", user_id)
        .fetch_one(pool)
        .await
}

2. Explicit transaction with retry logic for serializable isolation

Cockroachdb recommends explicit transactions for write sets. Axum handlers should run transaction blocks with retry loops to handle serialization failures gracefully.

async fn transfer_balance(
    pool: &sqlx::PgPool,
    from: i64,
    to: i64,
    amount: i64,
) -> Result<(), sqlx::Error> {
    let mut tx = pool.begin().await?;
    // Read within transaction to establish snapshot
    let from_balance: i64 = sqlx::query_scalar("SELECT balance FROM accounts WHERE id = $1")
        .bind(from)
        .fetch_one(&mut *tx)
        .await?;
    if from_balance < amount {
        return Err(sqlx::Error::RowNotFound);
    }
    sqlx::query("UPDATE accounts SET balance = balance - $1 WHERE id = $2")
        .bind(amount)
        .bind(from)
        .execute(&mut *tx)
        .await?;
    sqlx::query("UPDATE accounts SET balance = balance + $1 WHERE id = $2")
        .bind(amount)
        .bind(to)
        .execute(&mut *tx)
        .await?;
    tx.commit().await?;
    Ok(())
}

For production, wrap this in a retry loop (e.g., tenacity or custom) to catch Cockroachdb’s serialization errors (SQLSTATE 40001). This prevents lost updates and preserves integrity under concurrent load.

3. Schema-aware validation and constraints

Enforce NOT NULL and CHECK constraints in Cockroachdb, and validate inputs in Axum before issuing SQL. Use Rust types to mirror schema rules.

use axum::{extract::State, http::StatusCode, response::IntoResponse};
use serde::Deserialize;

#[derive(Deserialize)]
struct CreateAccount {
    user_id: i64,
    initial_balance: i64,
}

async fn create_account_handler(
    State(pool): State<sqlx::PgPool>,
    body: axum::Json<CreateAccount>,
) -> Result<impl IntoResponse, (StatusCode, String)> {
    if body.initial_balance < 0 {
        return Err((StatusCode::BAD_REQUEST, "initial_balance must be non-negative".into()));
    }
    sqlx::query("INSERT INTO accounts (id, balance) VALUES ($1, $2)")
        .bind(body.user_id)
        .bind(body.initial_balance)
        .execute(&pool)
        .await
        .map_err(|e| (StatusCode::INTERNAL_SERVER_ERROR, e.to_string()))?;
    Ok(StatusCode::CREATED)
}

Define Cockroachdb schema with explicit constraints:

CREATE TABLE accounts (
    id BIGINT PRIMARY KEY,
    balance DECIMAL NOT NULL CHECK (balance >= 0),
    updated_at TIMESTAMPTZ DEFAULT now()
);

This ensures the database and application agree on integrity rules, and Axum can reject invalid payloads before touching the cluster.

4. MiddleBrick integrations for ongoing integrity monitoring

Use the CLI to scan your endpoints regularly:

middlebrick scan https://api.example.com/openapi.yaml

With the Pro plan, enable continuous monitoring to detect integrity-related changes in OpenAPI specs or runtime behavior. The GitHub Action can fail a build if a regression is detected, and the MCP Server allows you to scan APIs directly from your AI coding assistant while you develop Axum handlers.