Out Of Bounds Write in Chi with Cockroachdb

An Out Of Bounds Write occurs when a program writes data outside the intended memory region. In the context of a Chi-based application using CockroachDB as the backend, this typically surfaces through unsafe handling of request parameters that map to database identifiers or slice indices. While CockroachDB itself prevents out-of-bounds writes at the storage layer, the vulnerability arises in the application logic when user input directly controls SQL placeholders or array indices without proper validation.

Chi is a minimalistic router for Go that encourages explicit parameter capture. When route parameters (e.g., /users/{id}) are parsed and forwarded to CockroachDB without type or range checks, an attacker can supply values that lead to incorrect slice indexing in application buffers or unexpected SQL behavior. For example, if an ID is converted to an integer and used to index a Go slice before being used in a query, an oversized integer may target memory beyond the slice bounds. CockroachDB will not reject the query on its own if the application passes a valid integer type, but the surrounding Go code might misuse the value locally, causing memory corruption that can be leveraged for further exploitation.

Consider a scenario where an endpoint retrieves a user profile by ID and stores intermediate results in a fixed-size buffer. If the ID is not bounded to the expected range, the calculated offset may point outside the buffer during read or write operations. Even though CockroachDB returns the correct row, the application’s handling of the result can trigger an out-of-bounds write when copying data into a pre-allocated slice. This is particularly risky when using reflection or unsafe pointers to map database rows to structs, as incorrect assumptions about slice capacity can bypass normal safety guarantees.

The combination of Chi’s flexible parameter extraction and CockroachDB’s strict SQL semantics can obscure these issues during unauthenticated scans. middleBrick tests such endpoints by probing for abnormal data exposure and input validation failures, checking whether unchecked numeric inputs lead to unexpected memory behavior. An API that accepts an ID parameter like ?user_id=999999 and uses it directly to index an in-memory cache without validating its relation to the actual data size may expose an out-of-bounds condition that does not cause an immediate database error but corrupts application state.

To detect this during a scan, middleBrick’s input validation checks analyze how numeric and string parameters are constrained before being used in business logic. It verifies that route parameters are properly bounded, cast, and validated before being used in memory operations or as arguments to SQL queries. The tool also examines whether the API returns consistent error handling for malformed identifiers, ensuring that out-of-bounds attempts do not result in silent memory corruption or information leakage.

Developers should treat every user-supplied identifier as potentially malicious and enforce strict bounds before using it in any in-memory structure. Even when CockroachDB accepts a query, the surrounding Go code must ensure that indices, offsets, and buffer positions remain within valid ranges. This requires explicit validation rules, such as checking that numeric IDs fall within the observed dataset range and that slice accesses use length checks before any write operation.

Remediation focuses on validating and sanitizing all inputs before they interact with database queries or memory structures. In Chi, route parameters should be parsed and explicitly checked against expected constraints. The following patterns demonstrate safe handling of numeric identifiers when working with CockroachDB.

Safe Parameter Parsing and Bounds Checking

Always convert route parameters to the expected type and verify they fall within an acceptable range before using them in queries or as slice indices.

import (
    "net/http"
    "strconv"

    "github.com/go-chi/chi/v5"
)

func getUserProfile(w http.ResponseWriter, r *http.Request) {
    // Extract parameter from Chi route
    idStr := chi.URLParam(r, "id")
    id, err := strconv.Atoi(idStr)
    if err != nil {
        http.Error(w, "invalid user ID", http.StatusBadRequest)
        return
    }

    // Enforce business-level bounds
    if id <= 0 || id > 1000000 {
        http.Error(w, "user ID out of range", http.StatusBadRequest)
        return
    }

    // Safe to use with CockroachDB
    var name string
    err = db.QueryRow(r.Context(), "SELECT name FROM users WHERE id = $1", id).Scan(&name)
    if err != nil {
        http.Error(w, "user not found", http.StatusNotFound)
        return
    }

    w.Write([]byte(name))
}

Using Contextual Bounds with Slice Operations

If the retrieved data is stored in a slice, validate the index before accessing memory directly.

func getCachedUser(cache []User, w http.ResponseWriter, r *http.Request) {
    idStr := chi.URLParam(r, "id")
    id, err := strconv.Atoi(idStr)
    if err != nil {
        http.Error(w, "invalid ID", http.StatusBadRequest)
        return
    }

    // Ensure index is within slice bounds
    if id < 0 || id >= len(cache) {
        http.Error(w, "ID out of cached range", http.StatusBadRequest)
        return
    }

    user := cache[id]
    // Process user safely
    w.Write([]byte(user.Name))
}

Parameterized Queries to Prevent Injection and Misuse

Even when bounds are checked, always use parameterized SQL to avoid accidental injection or type confusion.

err = db.Exec(r.Context(),
    "UPDATE accounts SET balance = $1 WHERE user_id = $2",
    balance, userID)
if err != nil {
    // handle error
}

Defensive Struct Mapping

When mapping rows to structs, avoid unsafe reflection. Use explicit scanning instead of generic decoding that might assume slice sizes.

type User struct {
    ID   int
    Name string
}

var u User
err = db.QueryRow(r.Context(), "SELECT id, name FROM users WHERE id = $1", userID).Scan(&u.ID, &u.Name)

These practices align with the checks performed by middleBrick’s input validation and authentication tests, which verify that parameters are properly constrained before being used in backend operations. The Pro plan’s continuous monitoring can alert developers if any endpoint begins accepting out-of-range identifiers that could lead to memory safety issues.