Code Smell - Squatting
Don't use guessable names in advance on mission-critical resources
TL;DR: Secure your cloud resources by avoiding predictable naming patterns.
Problems
Predictable names
Unauthorized access
Data exposure risks
Shadow resources
Account takeovers
Idor vulnerability
Premature Optimization
Solutions
Use unique bucket names with dark keys
Verify ownership on creation
Secure resources fully
Have indirections obfuscating real names
Book names to prevent squatting
Randomize names
Context
Resource squatting happens when attackers anticipate the naming patterns of cloud resources, like S3 buckets.
The attacker creates them in regions where the user hasn�t yet deployed resources.
User interaction with these attacker-owned resources can lead to severe security breaches like data exposure, unauthorized access, or account takeovers.
This vulnerability is critical in environments like AWS, where predictable naming conventions are often used.
Many systems avoid this indirection fearing the performance penalty which is a clear case of premature optimization.
Sample Code
Wrong
def create_bucket(account_id, region): bucket_name = f"aws-glue-assets-{account_id}-{region}" create_s3_bucket(bucket_name) # This is deterministic and open
Right
import uuid def create_bucket(account_id, region): unique_id = uuid.uuid4().hex # This number is not deterministic # is a way to generate a random UUID (Universally Unique Identifier) # in Python and then retrieve it as a hexadecimal string. bucket_name = f"aws-glue-assets-{unique_id}-{account_id}-{region}" create_s3_bucket(bucket_name) verify_bucket_ownership(bucket_name, account_id)
Detection
[X] Automatic
A security audit can detect this smell by analyzing your resource names for predictability.
Look for patterns in names that an attacker can easily anticipate or guess.
Many automated tools and manual code reviews can help identify these risks.
Tags
- Security
Level
[X] Intermediate
AI Generation
AI generators may create this smell using standard templates with predictable naming patterns.
Always customize and review generated code for security.
AI Detection
AI can help detect this smell if configured with rules that identify predictable or insecure resource naming conventions.
This is a security risk that requires understanding of cloud infrastructure and potential attack vectors.
Conclusion
Avoiding predictable naming patterns is critical to securing your cloud resources.
Always use unique, obscure, hard-to-guess names, and also verify resource ownership to protect against squatting attacks.
Relations

Code Smell 120 - Sequential IDs
Maxi Contieri ・ Mar 10 '22
More Info
Gb Hackers
Wikipedia
Disclaimer
Code Smells are my opinion.
Credits
Photo by Felix Koutchinski on Unsplash
The only system which is truly secure is one which is switched off and unplugged, locked in a titanium lined safe, buried in a concrete bunker, and is surrounded by nerve gas and very highly paid armed guards. Even then, I wouldn't stake my life on it.
Gene Spafford

Software Engineering Great Quotes
Maxi Contieri ・ Dec 28 '20
This article is part of the CodeSmell Series.

How to Find the Stinky parts of your Code
Maxi Contieri ・ May 21 '21
The above is the detailed content of Code Smell - Squatting. For more information, please follow other related articles on the PHP Chinese website!

Hot AI Tools

Undresser.AI Undress
AI-powered app for creating realistic nude photos

AI Clothes Remover
Online AI tool for removing clothes from photos.

Undress AI Tool
Undress images for free

Clothoff.io
AI clothes remover

Video Face Swap
Swap faces in any video effortlessly with our completely free AI face swap tool!

Hot Article

Hot Tools

Notepad++7.3.1
Easy-to-use and free code editor

SublimeText3 Chinese version
Chinese version, very easy to use

Zend Studio 13.0.1
Powerful PHP integrated development environment

Dreamweaver CS6
Visual web development tools

SublimeText3 Mac version
God-level code editing software (SublimeText3)

Hot Topics











Python is suitable for data science, web development and automation tasks, while C is suitable for system programming, game development and embedded systems. Python is known for its simplicity and powerful ecosystem, while C is known for its high performance and underlying control capabilities.

You can learn basic programming concepts and skills of Python within 2 hours. 1. Learn variables and data types, 2. Master control flow (conditional statements and loops), 3. Understand the definition and use of functions, 4. Quickly get started with Python programming through simple examples and code snippets.

Python excels in gaming and GUI development. 1) Game development uses Pygame, providing drawing, audio and other functions, which are suitable for creating 2D games. 2) GUI development can choose Tkinter or PyQt. Tkinter is simple and easy to use, PyQt has rich functions and is suitable for professional development.

You can learn the basics of Python within two hours. 1. Learn variables and data types, 2. Master control structures such as if statements and loops, 3. Understand the definition and use of functions. These will help you start writing simple Python programs.

Python is easier to learn and use, while C is more powerful but complex. 1. Python syntax is concise and suitable for beginners. Dynamic typing and automatic memory management make it easy to use, but may cause runtime errors. 2.C provides low-level control and advanced features, suitable for high-performance applications, but has a high learning threshold and requires manual memory and type safety management.

To maximize the efficiency of learning Python in a limited time, you can use Python's datetime, time, and schedule modules. 1. The datetime module is used to record and plan learning time. 2. The time module helps to set study and rest time. 3. The schedule module automatically arranges weekly learning tasks.

Python is widely used in the fields of web development, data science, machine learning, automation and scripting. 1) In web development, Django and Flask frameworks simplify the development process. 2) In the fields of data science and machine learning, NumPy, Pandas, Scikit-learn and TensorFlow libraries provide strong support. 3) In terms of automation and scripting, Python is suitable for tasks such as automated testing and system management.

Python excels in automation, scripting, and task management. 1) Automation: File backup is realized through standard libraries such as os and shutil. 2) Script writing: Use the psutil library to monitor system resources. 3) Task management: Use the schedule library to schedule tasks. Python's ease of use and rich library support makes it the preferred tool in these areas.
