HIGH stack overflowaxumcockroachdb

Stack Overflow in Axum with Cockroachdb

Stack Overflow in Axum with Cockroachdb — how this specific combination creates or exposes the vulnerability

A Stack Overflow in an Axum service that uses CockroachDB typically occurs when unbounded or recursive data structures are constructed from database rows and then serialized or rendered in a context that causes repeated traversal. In Axum, extractors and handlers build response types that may recursively reference related entities. If a query against CockroachDB returns rows that form a graph (for example, parent-child relationships) and the application constructs nested Rust structs without depth limits, the resulting data structure can grow until the call stack or heap is exhausted.

With CockroachDB, this risk is shaped by how you model and query data. CockroachDB is compatible with PostgreSQL wire protocol and its SQL behavior is largely consistent, but its distributed nature means queries can touch many nodes; a recursive common table expression (CTE) or unbounded join can produce very wide result sets that Axum deserializes into deeply nested structures. If the handler does not bound recursion—by using iterative traversal, DTO projections, or explicit depth limits—a Stack Overflow can manifest either in the application runtime or in serialization logic (e.g., when serializing deeply nested JSON for an API response).

A concrete pattern: an endpoint like GET /org/{id}/tree runs a recursive CTE against CockroachDB to fetch an org hierarchy, then constructs an OrgNode Rust struct that contains a Vec for children. Without a depth guard, a malicious or misconfigured hierarchy can cause unbounded recursion. Axum’s extractor/response pipeline will then allocate extensively, and in worst cases the runtime may exhaust stack space during deserialization or JSON rendering.

Additional contributing factors include missing pagination on list endpoints and overly broad SELECT * queries that pull large rows, which increase memory pressure and can exacerbate stack usage when combined with recursive data shapes. Because Axum is type-driven, the compiler cannot inherently prevent runtime deep recursion; developers must enforce limits in the handler and in the SQL layer.

To mitigate this specific combination, use DTOs that flatten the structure, apply explicit depth limits in SQL (e.g., restrict recursive CTEs with MAX), and avoid returning deeply recursive domain models directly from Axum handlers. Combine this with input validation on path/query parameters that could indicate traversal depth, and monitor payload sizes as an early indicator of abuse.

Cockroachdb-Specific Remediation in Axum — concrete code fixes

Remediation focuses on bounding recursion, flattening responses, and using safe SQL patterns. Below are concrete, working examples for Axum with CockroachDB using sqlx.

  • Use a DTO with a depth limit instead of recursive domain structs:
// Safe DTO: no recursive nesting
#[derive(serde::Serialize)]
struct OrgNodeDto {
    id: i64,
    name: String,
    parent_id: Option,
    depth: i32, // bounded depth
}
  • Query with a recursive CTE that enforces a max depth in CockroachDB:
-- CockroachDB SQL: bounded recursive CTE
WITH RECURSIVE org_path AS (
    SELECT id, name, parent_id, 0 AS depth
    FROM orgs
    WHERE id = $1
    UNION ALL
    SELECT o.id, o.name, o.parent_id, op.depth + 1
    FROM orgs o
    INNER JOIN org_path op ON o.parent_id = op.id
    WHERE op.depth < 10 -- enforce max depth
)
SELECT id, name, parent_id, depth FROM org_path;
  • Axum handler that uses the bounded query and returns the DTO:
// Axum handler with bounded depth and flattened output
async fn get_org_tree(
    Path(id): Path,
    pool: Extension,
) -> Result {
    let rows = sqlx::query_as!(OrgNodeDto,
        r#"
        WITH RECURSIVE org_path AS (
            SELECT id, name, parent_id, 0 AS depth
            FROM orgs
            WHERE id = $1
            UNION ALL
            SELECT o.id, o.name, o.parent_id, op.depth + 1
            FROM orgs o
            INNER JOIN org_path op ON o.parent_id = op.id
            WHERE op.depth < 10
        )
        SELECT id, name, parent_id, depth FROM org_path
        "#,
        id
    )
    .fetch_all(pool.get_ref())
    .await
    .map_err(|e| (StatusCode::INTERNAL_SERVER_ERROR, e.to_string()))?;
    Ok(Json(rows))
}
  • For list endpoints, enforce server-side pagination to limit result size:
// Paginated, non-recursive list endpoint
async fn list_orgs(
    pool: Extension,
    Query(params): Query<ListParams>,
) -> Result {
    let org_list = sqlx::query_as!(OrgSummary,
        "SELECT id, name FROM orgs LIMIT $1 OFFSET $2",
        params.limit,
        params.offset
    )
    .fetch_all(pool.get_ref())
    .await
    .map_err(|e| (StatusCode::INTERNAL_SERVER_ERROR, e.to_string()))?;
    Ok(Json(org_list))
}
  • Validate path/query parameters to prevent deep traversal requests:
// Validate depth-like inputs early
fn validate_depth(depth: Option<i32>) -> Result<i32, (StatusCode, String)> {
    match depth {
        Some(d) if d >= 0 && d <= 100 => Ok(d),
        _ => Err((StatusCode::BAD_REQUEST, "invalid depth".to_string())),
    }
}

These patterns ensure the Axum + CockroachDB stack avoids unbounded recursion and keeps response sizes predictable, reducing the risk of Stack Overflow and related denial-of-service conditions.

Frequently Asked Questions

How does CockroachDB’s distributed SQL behavior affect Stack Overflow risk in Axum?
CockroachDB can return wide result sets from recursive queries that traverse many nodes. If Axum deserializes these into deeply nested Rust structs without depth limits, the application can exhaust stack or heap, leading to a Stack Overflow. Bounding recursion in SQL and using flattened DTOs mitigates this.
Can middleBrick detect Stack Overflow risks in an Axum + Cockroachdb setup?
middleBrick scans unauthenticated attack surfaces and runs checks such as Input Validation and Unsafe Consumption. While it does not identify Stack Overflow by name, it surfaces related findings like missing rate limiting, weak input validation, and data exposure that can precede resource exhaustion issues; remediation guidance is included in the report.