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12 Tips To Secure Your Mobile Application

Cyber attacks and data theft have become so common these days especially when it comes to mobile applications. As a result, mobile apps that experience security breaches may suffer financial losses. With many hackers eyeing to steal customer data, securing these applications has become the number one priority for organizations and a serious challenge for developers. According to Gartner’s recent research, Hype Cycle for Application Security, investment in application security will increase by more than two-fold over the next few years, from $6 billion this year to $13.7 billion by 2026. Further, the report stated, “Application security is now top-of-mind for developers and security professionals, and the emphasis is now turning to apps hosted in public clouds,” It is crucial to get the fundamental components of DevOps security correct. Here are the 12 tips to secure your mobile application: 

1. Install apps from trusted sources:

It’s common to have Android applications republished on alternate markets or their APKs & IPAs made available for download. Both APK and IPA may be downloaded and installed from a variety of places, including websites, cloud services, drives, social media, and social networking. Only the Play Store and the App Store should be allowed to install trustworthy APK and IPA files. To prevent utilizing these apps, we should have a source check detection (Play Store or App Store) upon app start.

Also read, https://andresand.medium.com/add-method-to-check-which-app-store-the-android-app-is-installed-from-or-if-its-sideloaded-c9f450a3d069

2. Root Detection:

Android: An attacker could launch a mobile application on a rooted device and access the local memory or call specific activities or intents to perform malicious activities in the application. 

iOS: Applications on a jailbroken device run as root outside of the iOS sandbox. This can allow applications to access sensitive data stored in other apps or install malicious software negating sandboxing functionality. 

More on Root Detection- https://owasp.org/www-project-mobile-top-10/2016-risks/m8-code-tampering

3. Data Storing:

Developers use Shared Preferences & User Defaults to store key-value pairs like tokens, mobile numbers, email, boolean values, etc. Additionally, while creating apps, developers prefer SQLite databases for structured data. It is recommended to store any data in the format of encryption so that it is difficult to extract the information by hackers.

4. Secure Secret Keys:

API keys, passwords, and tokens shouldn’t be hardcoded in the code. It is recommended to use different techniques to store these values so that hackers cannot get away quickly by tampering with the application. 

Here’s a reference link: https://guides.codepath.com/android/Storing-Secret-Keys-in-Android

5. Code Obfuscation

An attacker may decompile the APK file and extract the source code of the application. This may expose sensitive information stored in the source code of the application to the attacker which may be used to perform tailored attacks. 

It is better to obfuscate the source code to prevent all the sensitive information contained in the source code.

6. Secure Communication:

An attacker may perform malicious activities to leverage the level of attacks since all communication is happening over unencrypted channels. So always use HTTPS URLs over HTTP URLs.

7. SSL Pinning:

Certificate pinning allows mobile applications to restrict communication only to servers with a valid certificate matching the expected value (pin). Pinning ensures that no network data is compromised even if a user is tricked into installing a malicious root certificate on their mobile device. Any app that pins its certificates would thwart such phishing attempts by refusing to transmit data over a compromised connection

Please refer: 

https://owasp.org/www-community/controls/Certificate_and_Public_Key_Pinning

8. Secure API request & response data

The standard practice is to use HTTPS for the baseline protection of REST API calls. The information sent to the server or received from the server may be further encrypted with AES, etc. For example, if there are sensitive contents, you might choose to select those to encrypt so that even if the HTTPS is somehow broken or misconfigured, you have another layer of protection from your encryption.

9. Secure Mobile App Authentication:

In case an application does not assign distinct and complex session tokens after login to a user, an attacker can conduct phishing in order to lure the victim to use a custom-generated token provided by the attacker and easily bypass the login page with the captured session by using a MiTM attack.

i) Assign a distinct and complex session token to a user each time he/she logs on successfully to the application. 

ii) Terminate the session lifetime immediately after logging out. 

iii) Do not use the same session token for two or more IP addresses. 

iv) Limit the expiry time for every session token.

10.  Allow Backup 

Disallow users to back up an app if it contains sensitive data. Having access to backup files (i.e. when android:allowBackup=”true”), it is possible to modify/read the content of an app even on a non-rooted device. So it is recommended to make allow backup false. 

11. Restrict accessing android application screens from other apps

Ideally, your activities should not give any provision to the opening from other services or applications. Make it true only when you have a specific requirement to access your flutter screens from other apps otherwise change to android:exported= ”false”

12. Restrict installing packages from the android application

REQUEST_INSTALL_PACKAGES permission allows apps to install new packages on a user’s device. We are committed to preventing abuse on the Android platform and protecting users from apps that self-update using any method other than Google Play’s update mechanism or download harmful APKs.

Conclusion: 

Mobile Apps have become more personalized than ever before with heaps of customers’ personal data stored in them every day. In order to build trust and loyalty among users and prevent significant financial and credential losses for the companies, it is now crucial to make sure the application is secure for the user. Following the above-mentioned mobile app security checklists will definitely help to prevent hackers from hacking the app.

About the Author:

Raviteja Aketi is a Senior Software Engineer at Mantra Labs. He has extensive experience with B2B projects. Raviteja loves exploring new technologies, watching movies, and spending time with family and friends.

Read our latest blog: Implementing a Clean Architecture with Nest.JS

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AI Code Assistants: Revolution Unveiled

AI code assistants are revolutionizing software development, with Gartner predicting that 75% of enterprise software engineers will use these tools by 2028, up from less than 10% in early 2023. This rapid adoption reflects the potential of AI to enhance coding efficiency and productivity, but also raises important questions about the maturity, benefits, and challenges of these emerging technologies.

Code Assistance Evolution

The evolution of code assistance has been rapid and transformative, progressing from simple autocomplete features to sophisticated AI-powered tools. GitHub Copilot, launched in 2021, marked a significant milestone by leveraging OpenAI’s Codex to generate entire code snippets 1. Amazon Q, introduced in 2023, further advanced the field with its deep integration into AWS services and impressive code acceptance rates of up to 50%. GPT (Generative Pre-trained Transformer) models have been instrumental in this evolution, with GPT-3 and its successors enabling more context-aware and nuanced code suggestions.

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  • Adoption rates: By 2023, over 40% of developers reported using AI code assistants.
  • Productivity gains: Tools like Amazon Q have demonstrated up to 80% acceleration in coding tasks.
  • Language support: Modern AI assistants support dozens of programming languages, with GitHub Copilot covering over 20 languages and frameworks.
  • Error reduction: AI-powered code assistants have shown potential to reduce bugs by up to 30% in some studies.

These advancements have not only increased coding efficiency but also democratized software development, making it more accessible to novice programmers and non-professionals alike.

Current Adoption and Maturity: Metrics Defining the Landscape

The landscape of AI code assistants is rapidly evolving, with adoption rates and performance metrics showcasing their growing maturity. Here’s a tabular comparison of some popular AI coding tools, including Amazon Q:

Amazon Q stands out with its specialized capabilities for software developers and deep integration with AWS services. It offers a range of features designed to streamline development processes:

  • Highest reported code acceptance rates: Up to 50% for multi-line code suggestions
  • Built-in security: Secure and private by design, with robust data security measures
  • Extensive connectivity: Over 50 built-in, managed, and secure data connectors
  • Task automation: Amazon Q Apps allow users to create generative AI-powered apps for streamlining tasks

The tool’s impact is evident in its adoption and performance metrics. For instance, Amazon Q has helped save over 450,000 hours from manual technical investigations. Its integration with CloudWatch provides valuable insights into developer usage patterns and areas for improvement.

As these AI assistants continue to mature, they are increasingly becoming integral to modern software development workflows. However, it’s important to note that while these tools offer significant benefits, they should be used judiciously, with developers maintaining a critical eye on the generated code and understanding its implications for overall project architecture and security.

AI-Powered Collaborative Coding: Enhancing Team Productivity

AI code assistants are revolutionizing collaborative coding practices, offering real-time suggestions, conflict resolution, and personalized assistance to development teams. These tools integrate seamlessly with popular IDEs and version control systems, facilitating smoother teamwork and code quality improvements.

Key features of AI-enhanced collaborative coding:

  • Real-time code suggestions and auto-completion across team members
  • Automated conflict detection and resolution in merge requests
  • Personalized coding assistance based on individual developer styles
  • AI-driven code reviews and quality checks

Benefits for development teams:

  • Increased productivity: Teams report up to 30-50% faster code completion
  • Improved code consistency: AI ensures adherence to team coding standards
  • Reduced onboarding time: New team members can quickly adapt to project codebases
  • Enhanced knowledge sharing: AI suggestions expose developers to diverse coding patterns

While AI code assistants offer significant advantages, it’s crucial to maintain a balance between AI assistance and human expertise. Teams should establish guidelines for AI tool usage to ensure code quality, security, and maintainability.

Emerging trends in AI-powered collaborative coding:

  • Integration of natural language processing for code explanations and documentation
  • Advanced code refactoring suggestions based on team-wide code patterns
  • AI-assisted pair programming and mob programming sessions
  • Predictive analytics for project timelines and resource allocation

As AI continues to evolve, collaborative coding tools are expected to become more sophisticated, further streamlining team workflows and fostering innovation in software development practices.

Benefits and Risks Analyzed

AI code assistants offer significant benefits but also present notable challenges. Here’s an overview of the advantages driving adoption and the critical downsides:

Core Advantages Driving Adoption:

  1. Enhanced Productivity: AI coding tools can boost developer productivity by 30-50%1. Google AI researchers estimate that these tools could save developers up to 30% of their coding time.
IndustryPotential Annual Value
Banking$200 billion – $340 billion
Retail and CPG$400 billion – $660 billion
  1. Economic Impact: Generative AI, including code assistants, could potentially add $2.6 trillion to $4.4 trillion annually to the global economy across various use cases. In the software engineering sector alone, this technology could deliver substantial value.
  1. Democratization of Software Development: AI assistants enable individuals with less coding experience to build complex applications, potentially broadening the talent pool and fostering innovation.
  2. Instant Coding Support: AI provides real-time suggestions and generates code snippets, aiding developers in their coding journey.

Critical Downsides and Risks:

  1. Cognitive and Skill-Related Concerns:
    • Over-reliance on AI tools may lead to skill atrophy, especially for junior developers.
    • There’s a risk of developers losing the ability to write or deeply understand code independently.
  2. Technical and Ethical Limitations:
    • Quality of Results: AI-generated code may contain hidden issues, leading to bugs or security vulnerabilities.
    • Security Risks: AI tools might introduce insecure libraries or out-of-date dependencies.
    • Ethical Concerns: AI algorithms lack accountability for errors and may reinforce harmful stereotypes or promote misinformation.
  3. Copyright and Licensing Issues:
    • AI tools heavily rely on open-source code, which may lead to unintentional use of copyrighted material or introduction of insecure libraries.
  4. Limited Contextual Understanding:
    • AI-generated code may not always integrate seamlessly with the broader project context, potentially leading to fragmented code.
  5. Bias in Training Data:
    • AI outputs can reflect biases present in their training data, potentially leading to non-inclusive code practices.

While AI code assistants offer significant productivity gains and economic benefits, they also present challenges that need careful consideration. Developers and organizations must balance the advantages with the potential risks, ensuring responsible use of these powerful tools.

Future of Code Automation

The future of AI code assistants is poised for significant growth and evolution, with technological advancements and changing developer attitudes shaping their trajectory towards potential ubiquity or obsolescence.

Technological Advancements on the Horizon:

  1. Enhanced Contextual Understanding: Future AI assistants are expected to gain deeper comprehension of project structures, coding patterns, and business logic. This will enable more accurate and context-aware code suggestions, reducing the need for extensive human review.
  2. Multi-Modal AI: Integration of natural language processing, computer vision, and code analysis will allow AI assistants to understand and generate code based on diverse inputs, including voice commands, sketches, and high-level descriptions.
  3. Autonomous Code Generation: By 2027, we may see AI agents capable of handling entire segments of a project with minimal oversight, potentially scaffolding entire applications from natural language descriptions.
  4. Self-Improving AI: Machine learning models that continuously learn from developer interactions and feedback will lead to increasingly accurate and personalized code suggestions over time.

Adoption Barriers and Enablers:

Barriers:

  1. Data Privacy Concerns: Organizations remain cautious about sharing proprietary code with cloud-based AI services.
  2. Integration Challenges: Seamless integration with existing development workflows and tools is crucial for widespread adoption.
  3. Skill Erosion Fears: Concerns about over-reliance on AI leading to a decline in fundamental coding skills among developers.

Enablers:

  1. Open-Source Models: The development of powerful open-source AI models may address privacy concerns and increase accessibility.
  2. IDE Integration: Deeper integration with popular integrated development environments will streamline adoption.
  3. Demonstrable ROI: Clear evidence of productivity gains and cost savings will drive enterprise adoption.
  1. AI-Driven Architecture Design: AI assistants may evolve to suggest optimal system architectures based on project requirements and best practices.
  2. Automated Code Refactoring: AI tools will increasingly offer intelligent refactoring suggestions to improve code quality and maintainability.
  3. Predictive Bug Detection: Advanced AI models will predict potential bugs and security vulnerabilities before they manifest in production environments.
  4. Cross-Language Translation: AI assistants will facilitate seamless translation between programming languages, enabling easier migration and interoperability.
  5. AI-Human Pair Programming: More sophisticated AI agents may act as virtual pair programming partners, offering real-time guidance and code reviews.
  6. Ethical AI Coding: Future AI assistants will incorporate ethical considerations, suggesting inclusive and bias-free code practices.

As these trends unfold, the role of human developers is likely to shift towards higher-level problem-solving, creative design, and AI oversight. By 2025, it’s projected that over 70% of professional software developers will regularly collaborate with AI agents in their coding workflows1. However, the path to ubiquity will depend on addressing key challenges such as reliability, security, and maintaining a balance between AI assistance and human expertise.

The future outlook for AI code assistants is one of transformative potential, with the technology poised to become an integral part of the software development landscape. As these tools continue to evolve, they will likely reshape team structures, development methodologies, and the very nature of coding itself.

Conclusion: A Tool, Not a Panacea

AI code assistants have irrevocably altered software development, delivering measurable productivity gains but introducing new technical and societal challenges. Current metrics suggest they are transitioning from novel aids to essential utilities—63% of enterprises now mandate their use. However, their ascendancy as the de facto standard hinges on addressing security flaws, mitigating cognitive erosion, and fostering equitable upskilling. For organizations, the optimal path lies in balanced integration: harnessing AI’s speed while preserving human ingenuity. As generative models evolve, developers who master this symbiosis will define the next epoch of software engineering.

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