Out Of Bounds Read in Fastapi with Cockroachdb

An Out Of Bounds Read occurs when an API accesses memory or data outside the intended allocation boundaries. In a Fastapi service that uses Cockroachdb, this typically arises from improper bounds checking on user-supplied identifiers or pagination values before constructing SQL queries. Because Cockroachdb is compatible with PostgreSQL wire protocol, developers often use parameterized queries or an ORM; however, if index or cursor values derived from request inputs are used directly in SQL without validation, the query can request rows at negative or excessively large offsets, causing the database driver or underlying storage layer to return unintended rows or memory regions.

The risk is compounded when endpoints expose sequential integer IDs or timestamps without verifying existence or access rights. For example, an endpoint like /users/{user_id} that fetches a profile might decrement user_id to iterate neighbors for related recommendations. If user_id - 1 becomes zero or negative, the SQL offset or key range can point outside the valid row set, leading to an Out Of Bounds Read. The scanner’s BOLA/IDOR and Input Validation checks would flag this as a high-severity finding because the unauthenticated attack surface allows an attacker to probe neighboring records or trigger driver-level exceptions that may leak stack traces or partial data.

Additionally, pagination patterns using LIMIT and OFFSET can become unsafe if the offset is derived from unchecked client input. Cockroachdb’s distributed execution may handle large offsets inconsistently under load, and an extreme offset can cause the query engine to read unintended pages or buffers. The 12 security checks run in parallel will highlight both the BOLA/IDOR risk and the lack of Rate Limiting or Input Validation, producing a finding with severity high and remediation guidance to enforce strict bounds and existence checks before query construction.

middleBrick’s OpenAPI/Swagger analysis resolves $ref definitions and cross-references these definitions with runtime probe results, ensuring that parameter schemas and response examples align with actual behavior. If the spec defines an integer path parameter with a minimum of 1 but the implementation does not enforce this, the discrepancy is surfaced as a finding. The LLM/AI Security module does not apply here, as this scenario involves traditional data access controls rather than prompt injection or system prompt leakage.

When you scan such an endpoint with the CLI tool using middlebrick scan <url>, you receive a per-category breakdown that maps to frameworks like OWASP API Top 10 and highlights the specific checks that triggered. The dashboard and GitHub Action integrations can then track this endpoint over time and fail builds if the risk score drops below your configured threshold, encouraging proactive hardening before deployment.

Remediation focuses on validating and sanitizing all inputs that influence SQL construction, using parameterized queries, and enforcing strict existence checks. Below are concrete examples for Fastapi with Cockroachdb using asyncpg and pydantic models.

from fastapi import FastAPI, HTTPException, Depends
from pydantic import BaseModel, Field
import asyncpg
import os

app = FastAPI()

async def get_pool():
    return asyncpg.create_pool(
        host=os.getenv("COCKROACH_HOST"),
        port=int(os.getenv("COCKROACH_PORT", 26257)),
        user=os.getenv("COCKROACH_USER", "root"),
        database=os.getenv("COCKROACH_DB", "testdb"),
        ssl=True
    )

class UserProfile(BaseModel):
    id: int = Field(..., ge=1, description="User ID must be positive")
    name: str

@app.get("/users/{user_id}")
async def get_user(user_id: int, pool: asyncpg.Pool = Depends(get_pool)):
    if user_id < 1:
        raise HTTPException(status_code=400, detail="Invalid user identifier")
    async with pool.acquire() as conn:
        row = await conn.fetchrow("SELECT id, name FROM users WHERE id = $1", user_id)
        if row is None:
            raise HTTPException(status_code=404, detail="User not found")
        return {"id": row["id"], "name": row["name"]}

@app.get("/users/{user_id}/neighbors")
async def get_neighbors(user_id: int, direction: str = "next", limit: int = 1, pool: asyncpg.Pool = Depends(get_pool)):
    if user_id < 1:
        raise HTTPException(status_code=400, detail="Invalid user identifier")
    if direction not in ("prev", "next"):
        raise HTTPException(status_code=400, detail="Direction must be 'prev' or 'next'")
    if limit < 1 or limit > 100:
        raise HTTPException(status_code=400, detail="Limit must be between 1 and 100")
    offset = limit if direction == "next" else -limit
    # Ensure we do not produce negative ids by checking bounds in app layer
    target_id = user_id + offset
    if target_id < 1:
        return {"neighbors": []}
    async with pool.acquire() as conn:
        rows = await conn.fetch(
            "SELECT id, name FROM users WHERE id = $1",
            target_id
        )
        return {"neighbors": [dict(r) for r in rows]}

Key remediation steps:

• Validate path and query parameters with pydantic or explicit checks (e.g., ge=1) before using them in SQL.
• Use parameterized queries ($1) to avoid injection and ensure type safety; do not concatenate IDs into SQL strings.
• For pagination, validate offset and limit ranges, and avoid large offsets by using keyset pagination where possible.
• Check existence of the row after query execution and return 404 if not found, preventing information leakage via timing differences.
• Leverage the Pro plan’s continuous monitoring and CI/CD integration (GitHub Action) to enforce these patterns across your API fleet and fail builds if risk scores exceed your threshold.

These practices reduce the attack surface for Out Of Bounds Read and align with OWASP API Top 10 classifications for Broken Object Level Authorization and Excessive Data Exposure. middleBrick’s findings will highlight the missing validations and suggest specific remediation steps, while the MCP Server can integrate scanning into your AI coding assistant to catch issues during development.