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Detection of Unassociated GIDs and Security Hardening Procedures in Linux Systems

Tue, 02 Jun 2026 17:02:38 +0900

Guidelines for Identifying and Removing Unnecessary Groups (Orphaned GIDs) in Linux Systems

In Linux system operations, the existence of groups defined in the /etc/group file that are not linked to any active user in /etc/passwd (orphaned GIDs) is considered a security management deficiency. Based on item [U-09] of the Technical Vulnerability Analysis and Evaluation Guidelines for Critical Information Infrastructure (2026 Revision), this article details the technical approach to identifying and properly handling these unnecessary GIDs.

1. Overview of Vulnerability and Risk Analysis

1.1 Objective of the Assessment

By reviewing system configuration files and identifying/removing unnecessary or unassociated groups, the attack surface is minimized. The objective is to prevent unauthorized access to leftover files of deleted users and to ensure transparency in privilege management.

1.2 Potential Security Threats

Abuse of Privileges and Unintended Access: If files owned by a deleted user retain ownership by an orphaned GID, an attacker who compromises a low-privilege account might attempt to join that group to gain access to confidential files.
Social Engineering: If an insider threat discovers high-value files owned by a specific orphaned GID, there is a risk they might request an administrator to add their account to that GID under the guise of operational necessity.
Audit and Management Overhead: Leaving unnecessary GIDs unaddressed complicates security audits and configuration management, making it difficult to clearly track resource ownership.

2. Assessment Criteria and System Architecture

2.1 Target Environment and GID Range Classification

Linux distributions manage GID ranges separately for system services and regular users. During assessment, the focus is on GIDs within the user account range, excluding system GIDs managed by the package manager.

OS Family	System Account GID Range	User Account GID Range
Debian/Ubuntu	0 to 999	1000 or above
RHEL 7 and later	0 to 999	1000 or above
RHEL 6 and earlier	0 to 499	500 or above

2.2 Evaluation Criteria

Pass: All unnecessary groups or GIDs that do not correspond to active user accounts have been identified and removed.
Vulnerable: Unnecessary groups or GIDs without active user accounts exist within the system configuration files.

3. Implementation of the Assessment Script

The following Bash script automatically detects the OS type, applies the appropriate GID threshold, and identifies orphaned GIDs.

#!/bin/bash

# GID threshold determination based on OS distribution
if [ -f /etc/os-release ]; then
 . /etc/os-release
 if [[ "$ID" == "rhel" &amp;&amp; "${VERSION_ID%%.*}" -le 6 ]]; then
 GID_MIN=500
 else
 GID_MIN=1000
 fi
else
 GID_MIN=1000
fi

echo "[Scanning for orphaned GIDs (Threshold: &gt;= $GID_MIN)]"

# Identify GIDs in /etc/group not present as primary GID in /etc/passwd
# and having no members listed in /etc/group
awk -F: -v min=$GID_MIN '$3 &gt;= min {print $3}' /etc/group | while read gid; do
 group_info=$(grep ":$gid:" /etc/group)
 group_name=$(echo "$group_info" | cut -d: -f1)
 group_members=$(echo "$group_info" | cut -d: -f4)
 
 # Check if any user uses this GID as primary
 user_exists=$(awk -F: -v gid=$gid '$4 == gid {print $1}' /etc/passwd)
 
 if [ -z "$user_exists" ] &amp;&amp; [ -z "$group_members" ]; then
 echo "Vulnerable: Orphaned GID detected -&gt; Group: $group_name (GID: $gid)"
 fi
done

4. Remediation Guidelines

If the assessment result is “Vulnerable”, perform the remediation safely using the following steps.

Step 1: Identifying Files Associated with Orphaned GIDs

Before deleting a group, you must verify whether any files owning that GID exist on the filesystem. Failure to do so may result in unintended privilege assignment if the same GID is reused in the future.

# Replace [GID] with the identified orphaned GID value
find / -gid [GID] 2&gt;/dev/null

Step 2: Reassigning File Ownership

If files are found, change their ownership to an appropriate active group (e.g., root or a specific service group).

# Reassign group ownership to a secure administrative group
find / -gid [GID] -exec chgrp root {} + 2&gt;/dev/null

Step 3: Removing Unnecessary Groups

After confirming that no associated files exist, remove the group using the groupdel command.

# Remove the group entry from /etc/group
groupdel [GROUP_NAME]

Operational Notes

While removing orphaned GIDs typically does not affect system services, there are rare cases where legacy applications hardcode and utilize specific GIDs. Therefore, before executing deletions in a production environment, always scan the entire filesystem using the find command to verify the absence of dependencies based on empirical data. Additionally, optimizing the base image configuration in the container image build process (Dockerfile) to prevent the creation of unnecessary groups is effective for maintaining mid-to-long-term security.

Node.js Integration and Verification of WASM-Based PostgreSQL PGlite

Mon, 01 Jun 2026 08:12:26 +0900

Overview and Architectural Features

PGlite is a fully functional, lightweight PostgreSQL engine compiled to WebAssembly (WASM) and packaged as a client-side JavaScript library. Developed by ElectricSQL, PGlite allows developers to run a PostgreSQL database directly within a Node.js runtime, web browser, or mobile environment (via React Native/Capacitor) without requiring a separate PostgreSQL server, Docker container, or external daemon.

This article describes the steps for initializing, operating, benchmarking, and simulating a hybrid synchronization workflow with PGlite in a Node.js environment.

Key Architectural Features

💡 WASM-Driven PostgreSQL Engine: Runs an actual PostgreSQL engine compiled to WebAssembly, maintaining SQL syntax compatibility.
🛠️ Environment-Aware Storage Abstraction: Automatically utilizes the local file system for data persistence in Node.js environments, and falls back to IndexedDB in browser environments.
⚡ Elimination of Network Overhead: Database queries are executed in-process, eliminating the network latency associated with traditional database connections.

Project Setup and Environment Configuration

Set up a Node.js environment configured to use ES Modules and install the required PGlite dependencies.

# 1. Create and navigate to the project directory
mkdir pglite-demo
cd pglite-demo

# 2. Initialize package.json and enable ES Modules
npm init -y
npm pkg set type="module"

# 3. Install the PGlite library
npm install @electric-sql/pglite

Implementation Code (`index.js`)

Create an index.js file in the project root directory and implement the logic to initialize the database, benchmark batch insertions using transactions, perform standard CRUD operations, and simulate an offline-first synchronization cycle.

import { PGlite } from "@electric-sql/pglite";

// Initialize PGlite instance
// Creates or loads a persistent PostgreSQL database in the local "./pgdata" directory.
// Targets the local file system in Node.js environments, and defaults to IndexedDB in browser environments.
const db = new PGlite("./pgdata");

async function main() {
 console.log("🚀 Starting PGlite (Postgres in WASM)...\n");

 // 1. Initialize table (Execute DDL)
 await db.exec(`
 CREATE TABLE IF NOT EXISTS notes (
 id SERIAL PRIMARY KEY,
 title TEXT NOT NULL,
 content TEXT,
 synced BOOLEAN DEFAULT false,
 created_at TIMESTAMP DEFAULT NOW()
 );
 `);
 console.log("✅ 'notes' table ready (Postgres Engine running)\n");

 // ---------------------------------------------------------
 // 2. Performance Benchmark: Bulk Data Insertion
 // ---------------------------------------------------------
 console.log("📊 [Benchmark] Starting insertion test of 100 notes...");
 const start = performance.now();

 // Batch operations using a transaction block.
 // Since there is no network overhead, in-memory WASM execution is fast.
 await db.transaction(async (tx) =&gt; {
 for (let i = 0; i &lt; 100; i++) {
 await tx.query(
 "INSERT INTO notes (title, content) VALUES ($1, $2)",
 [`Note #${i}`, `This is test note number ${i}.`]
 );
 }
 });

 const end = performance.now();
 console.log(`⚡ Completed! Elapsed time: ${(end - start).toFixed(2)}ms`);
 console.log(` (Average processing speed per item: ${((end - start) / 100).toFixed(2)}ms)\n`);

 // ---------------------------------------------------------
 // 3. Execution of CRUD Scenarios
 // ---------------------------------------------------------
 console.log("📝 [CRUD Scenario] Executing");

 // [Create] - Insert a single record and return the created row
 const newNote = await db.query(
 "INSERT INTO notes (title, content) VALUES ($1, $2) RETURNING *",
 ["Important Meeting", "2:00 PM: Q3 Roadmap Discussion"]
 );
 console.log("1. Note Created:", newNote.rows[0]);

 // [Update] - Update the content of the created record
 const updatedNote = await db.query(
 "UPDATE notes SET content = $1 WHERE id = $2 RETURNING *",
 ["Changed to 3:00 PM: Q3 Roadmap Discussion", newNote.rows[0].id]
 );
 console.log("2. Note Updated:", updatedNote.rows[0]);

 // [Read] - Retrieve the 3 most recent records
 const list = await db.query("SELECT * FROM notes ORDER BY created_at DESC LIMIT 3");
 console.log("3. Recent Notes Reference (Top 3):");
 console.table(list.rows.map(n =&gt; ({ id: n.id, title: n.title, content: n.content })));

 // ---------------------------------------------------------
 // 4. Hybrid Synchronization Simulation
 // ---------------------------------------------------------
 console.log("\n🔄 [Sync] Simulating backend synchronization...");

 // Retrieve unsynced local records (synced = false)
 const unsyncedParams = await db.query("SELECT * FROM notes WHERE synced = false");
 const unsyncedCount = unsyncedParams.rows.length;

 if (unsyncedCount &gt; 0) {
 console.log(` -&gt; Sync Target: ${unsyncedCount} items detected`);
 
 // Simulate network latency (500ms) representing data transmission to a remote server
 await new Promise(r =&gt; setTimeout(r, 500));
 console.log(" -&gt; ☁️ Backend transmission completed (Mock Server)");

 // Update local database state, marking as synced
 const ids = unsyncedParams.rows.map(n =&gt; n.id);
 await db.query("UPDATE notes SET synced = true WHERE id = ANY($1)", [ids]);
 console.log(" -&gt; ✅ Local DB status updated to 'Synced'");
 } else {
 console.log(" -&gt; No data to synchronize.");
 }

 // ---------------------------------------------------------
 // Execution End
 // ---------------------------------------------------------
 console.log("\n🎉 All demos completed successfully.");
}

main().catch((err) =&gt; {
 console.error("❌ Error occurred:", err);
});

Execution Steps and Expected Output

To run the script, execute the following command in your terminal.

node index.js

Expected Console Output Example

🚀 Starting PGlite (Postgres in WASM)...

✅ 'notes' table ready (Postgres Engine running)

📊 [Benchmark] Starting insertion test of 100 notes...
⚡ Completed! Elapsed time: 42.50ms
 (Average processing speed per item: 0.43ms)

📝 [CRUD Scenario] Executing
1. Note Created: { id: 101, title: 'Important Meeting', ... }
2. Note Updated: { id: 101, title: 'Important Meeting', content: 'Changed to 3:00 PM...', ... }
3. Recent Notes Reference (Top 3):
┌─────────┬──────────────┬────────────────────────────┐
│ (index) │ id │ title │
├─────────┼──────────────┼────────────────────────────┤
│ 0 │ 101 │ 'Important Meeting' │
│ 1 │ 100 │ 'Note #99' │
│ 2 │ 99 │ 'Note #98' │
└─────────┴──────────────┴────────────────────────────┘

🔄 [Sync] Simulating backend synchronization...
 -&gt; Sync Target: 101 items detected
 -&gt; ☁️ Backend transmission completed (Mock Server)
 -&gt; ✅ Local DB status updated to 'Synced'

🎉 All demos completed successfully.

Architectural and Performance Verification Points

This implementation demonstrates three key functional characteristics of PGlite.

1. Low Latency via In-Process Execution

The process of sequentially inserting 100 records within a transaction block completes in tens of milliseconds (e.g., approx. 42.50ms, or approx. 0.43ms per record). In traditional client-server database configurations, similar operations can take seconds due to network round-trips, TCP handshakes, and connection pooling overhead. PGlite achieves extremely low overhead by running the database engine in-process.

2. Compatibility with PostgreSQL Syntax

Unlike lightweight key-value stores or simple SQL-like engines, PGlite runs an actual PostgreSQL engine. Therefore, it natively supports standard PostgreSQL SQL syntax, including:

Complex DDL (CREATE TABLE IF NOT EXISTS)
Transaction control (db.transaction)
Data manipulation with RETURNING clauses (INSERT ... RETURNING *)
Advanced query operations using array parameters (WHERE id = ANY($1))

3. Data Persistence

Running this script multiple times confirms that data is persisted within the ./pgdata directory. The auto-incrementing primary key (id) does not reset to 1, but continues to increase across executions (e.g., starting from 101 on the second run). This proves that PGlite functions as a persistent, reliable database engine rather than just a temporary in-memory mock.

Operational Notes

💡 Storage Selection: While the file system is used in Node.js environments, IndexedDB is automatically selected when running in browser environments. Keep in mind the behavior of the storage adapter for each environment.
⚠️ Resource Consumption: Because a full-featured PostgreSQL runs on WASM, memory consumption is higher than that of typical key-value stores. When deploying to resource-constrained edge environments or older mobile devices, real-device memory profiling is recommended.
⚠️ Concurrency Control: PGlite is designed with a single-process orientation. Simultaneous write access to the same data directory from multiple Node.js processes can cause lock contention or data corruption, requiring appropriate access control design.

En_translation on K-Life Hack | Systems Architecture & DevOps

Detection of Unassociated GIDs and Security Hardening Procedures in Linux Systems

Guidelines for Identifying and Removing Unnecessary Groups (Orphaned GIDs) in Linux Systems

1. Overview of Vulnerability and Risk Analysis

1.1 Objective of the Assessment

1.2 Potential Security Threats

2. Assessment Criteria and System Architecture

2.1 Target Environment and GID Range Classification

2.2 Evaluation Criteria

3. Implementation of the Assessment Script

4. Remediation Guidelines

Step 1: Identifying Files Associated with Orphaned GIDs

Step 2: Reassigning File Ownership

Step 3: Removing Unnecessary Groups

Operational Notes

Node.js Integration and Verification of WASM-Based PostgreSQL PGlite

Overview and Architectural Features

Key Architectural Features

Project Setup and Environment Configuration

Implementation Code (index.js)

Execution Steps and Expected Output

Expected Console Output Example

Architectural and Performance Verification Points

1. Low Latency via In-Process Execution

2. Compatibility with PostgreSQL Syntax

3. Data Persistence

Operational Notes

Implementation Code (`index.js`)