Best Programming Languages for Cybersecurity

Cybersecurity has been a hot topic for decades, and its importance is only growing with the increasing adoption of AI. Considering that many routine operations will soon be delegated to bots, cybersecurity is set to become even more critical. Therefore, expertise in this field positions you as a highly sought-after candidate in the IT industry.

The term “cybersecurity” has a dual meaning. On the one hand, it refers to a set of practices for developing secure software. On the other hand, cybersecurity can be referred to as a practice of testing existing software for vulnerabilities, modeling attacks and researching ways to prevent security breaches.

Under the latter definition of cybersecurity, the choice of programming language largely depends on the specific role. For instance, a penetration tester might prefer high-level, garbage-collected languages like JavaScript, Python, or Ruby (given the prevalence of Metasploit) for automation purposes, prioritizing ease of use over strict correctness. They might even opt for C to manipulate memory in ways that high-level languages would prohibit. Meanwhile, a malware analyst often deals with raw assembly code. Security and forensic analysts, who require robust data processing capabilities, might find languages like R suitable. In contrast, incident responders might rely solely on shell scripts or not use any programming language at all.

In this article, our focus will be on languages used for developing secure software, with some insights into those used for testing vulnerabilities.

What are the qualities of a good programming language for writing protected software?

Best languages_for_cybersecurity

Programming languages for cybersecurity applications should possess several crucial characteristics:

  • Memory safety: Opt for languages with memory safety features like automatic memory management to prevent common vulnerabilities, such as buffer overflows. Haskell, Rust, Java, TypeScript and Python are all memory-safe languages.
  • Strong typing: Choose a language with strong typing to catch errors early and prevent type confusion vulnerabilities. Again, this is characteristic of Haskell, Rust, Java, TypeScript, and Python.
  • Static typing: Static typing checks for type-related errors at compile time, making type mismatch errors (and related vulnerabilities) impossible at runtime. With a powerful enough type system, statically typed languages can also help detect various logic errors at compile time. Haskell, Rust, TypeScript and Java are examples of such languages.
  • Concurrency support: Ensure the language provides effective concurrency support for building applications. Rust and Haskell excel in concurrent and parallel programming.
  • Immutability: Languages that enforce immutability, where variables cannot be modified after creation, can prevent numerous issues associated with shared mutable state—a common source of concurrency and security problems. Haskell and Rust are examples of this category.
  • Code isolation and sandboxing: A secure language should provide features for limiting the code’s access to system resources and other programs. This isolation can prevent malicious code from affecting other parts of the system. For instance, TypeScript’s Deno runtime has a feature called “permissions.” Python’s PyPy interpreter also has support for sandboxing, although it’s at a “working prototype” stage.

Selecting the right programming language involves a careful consideration of the above aspects, taking into account the specific requirements and objectives of the project.

Which programming languages are best suited for cybersecurity?

While some developers and researchers may argue that certain languages are inherently more secure than others, the reality is that the security of a software project largely depends on how engineers and developers use the language.

Several languages may complement each other, depending on the situation and specific software architecture. For example, to enhance your backend solution, choosing an appropriate statically typed language for the frontend part would be a smart choice. With this principle in mind, we discuss the benefits and weaknesses of some popular languages below.

All the languages we look at in this article are memory-safe and strongly typed. Since we have explained the importance of these qualities above, we will not cover them in the following section. Instead, we will touch upon other characteristics that make them suitable for cybersecurity.

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Haskell

As a functional programming language, Haskell has a steeper learning curve compared to many others. However, the breadth of its capabilities makes the time investment worthwhile. Haskell is useful for developing secure applications, analyzing data, crafting cryptographic algorithms, and automating security checks. Below is a breakdown of why Haskell is a strong choice for cybersecurity.

  • Powerful type system. Out of the languages we look at here, Haskell has the most powerful type system. It allows for advanced type-level programming, enabling developers to encode domain constraints on the type level. This can significantly expand the scope of static analysis, helping catch potential security issues early in the development process.
  • Functional paradigm. There’s no implicit mutable state in the functional paradigm. This helps in preventing a class of bugs and vulnerabilities associated with mutable state, such as race conditions and other concurrency-related issues. Accidental mutation is minimized, along with the likelihood of issues related to temporal coupling.
  • Less boilerplate code. Haskell often requires less code to achieve the same functionality compared to other languages. Concise code means fewer places for bugs to hide, which is a significant advantage in developing secure software.
  • Monadic effects. The use of monads in Haskell to model side effects provides a disciplined approach to secure state and I/O management.
  • Formal verification. Haskell’s functional nature makes it particularly suitable for using formal verification methods. There are various tools to facilitate that, from libraries like Liquid Haskell, to integrations with theorem provers like Coq.

As any other language, Haskell has its limitations:

  • Less mainstream in industry. Haskell is not as widely used in the industry as other languages like Python or C++.
  • Limited low-level control. Haskell is designed to abstract away many low-level details, which is generally a strength. However, in cybersecurity, sometimes direct control over low-level operations, such as memory management, is required. Haskell’s high level of abstraction might be a limitation in such scenarios.

If you wish to learn Haskell or deepen your knowledge of it, check out our Haskell courses.

Rust

As an imperative language, Rust excels in emulating functional and object-oriented patterns, making it a robust choice for writing secure software.

  • Borrow checker. Rust has a unique feature, called a borrow checker. By analyzing the code at compile time, the borrow checker ensures that references to memory are valid, eliminating the need for a garbage collector, usually used by other memory-safe languages. This allows Rust to achieve memory safety without incurring any runtime overhead.
  • Concurrency safety. Rust’s type system and ownership rules greatly reduce the likelihood of concurrency-related bugs. This is especially important in cybersecurity, where concurrent processes must be managed safely to prevent race conditions and similar issues.
  • Minimal runtime. Rust has a minimal runtime, making it a good choice for writing low-level systems applications, such as operating system components or embedded systems, where control over resources is critical for security.
  • Control over low-level details. Rust provides fine-grained control over low-level system details. This allows developers to design secure protocols and algorithms and ensure that the code behaves exactly as intended.
  • Immutability. Variables in Rust are immutable by default, protecting from unintended data modifications.
  • Vibrant ecosystem and modern tooling. The Rust ecosystem is rapidly growing and includes robust tools for code analysis, testing, and package management essentials for developing secure applications.
  • Regular updates. The Rust language and its standard library are regularly updated with a focus on security. This ongoing development helps address potential vulnerabilities and adapt to new security threats.

Some benefits of Rust can also become limitations, such as:

  • Code verbosity. When all other things are being equal, Rust code can be notably more verbose than Python code, for example. This is largely a deliberate choice on the part of Rust’s authors, but it can be a subjective downside.
  • Limited standard library. While Rust’s standard library provides essential functionality, it is deliberately kept small to avoid bloat. But this means that for advanced functionality one needs to turn to external crates (libraries) or write their own.
  • No garbage collection. Rust deliberately lacks garbage collection, making it particularly suitable for systems programming applications where exact control over system resources is necessary. However, that places the responsibility of memory management on the programmer, potentially complicating the code.
  • Error handling. Rust’s error handling is robust but can be verbose. When integrating with SaaS platforms, which often involves handling numerous types of errors from external APIs, the verbosity can increase the complexity of the code.

Explore posts about Rust in our blog.

TypeScript

TypeScript (TS) is a language often used for creating interfaces. It is becoming increasingly popular in the field of cybersecurity, especially for the development of web applications and services. Here are some of the aspects that make this language beneficial for cybersecurity:

  • Code clarity and maintainability.TS enhances code clarity and maintainability by making it easier to understand the structure and intent of the code and speeding up auditing for security vulnerabilities.
  • Compatibility with JavaScript. TypeScript has native support for plain JS, making it possible to mix TS and JS in the same project.
  • Ecosystem. TS has a diverse ecosystem of libraries, tools, and frameworks to support secure coding practices.
  • Enhanced tooling support. TypeScript’s tooling support with integrated development environments (IDEs) and linters can help identify potential security flaws during development. For example, tools can detect potentially unsafe code patterns or dependencies.
  • Integration with modern frameworks. It integrates well with modern web frameworks, such as Angular, React, and Vue.js, which have their own sets of best practices for security. Using TypeScript with these frameworks can enhance overall application security.

TypeScript also has its disadvantages:

  • Dependence on JavaScript. TypeScript is a superset of JavaScript and ultimately compiles down to JavaScript. This implies that certain security vulnerabilities commonly present in pure JavaScript code bases may still arise, even with the added layer of TypeScript. TS allows the use of “any” type and type assertions, which can potentially bypass the compiler’s type checking. This may lead developers to over-rely on compile-time type checking and neglect runtime validation.
  • Third-party type definitions. When using third-party libraries, developers often rely on type definitions from DefinitelyTyped or other sources. These definitions may not always be accurate or up-to-date, potentially leading to security issues.
  • Confusion due to complex types. Complex type declarations in TypeScript can become very intricate and hard to understand. This can lead to errors and oversights in type handling, which can have security implications.
  • No enforced immutability. Unlike some other languages, TypeScript doesn’t enforce immutability. Mutable state can lead to a range of issues, potentially affecting application security.

Read more about TS benefits here.

Java

In cybersecurity, Java is often employed to develop secure enterprise-level applications, encryption technologies, data integrity solutions, and secure communication systems. It is also used to design security tools for detailed log analysis and network traffic monitoring, which are necessary for malware analysis and forensic investigations.

  • Built-in security features. Java’s standard library offers a comprehensive array of built-in security components, including advanced authentication, access control, encryption, and secure communication protocols. Key components essential for creating secure applications include the Java Cryptography Architecture (JCA) and the Java Secure Socket Extension (JSSE).
  • Exception handling. Java’s exception handling framework helps in managing errors and exceptions securely. Proper handling of exceptions is crucial in preventing security loopholes and ensuring that the application behaves predictably under diverse conditions.
  • Static code analysis tools: Java, being one of the more mature languages on this list, boasts an impressive array of static analysis tools, from linters like Checkstyle to bytecode analyzers like SpotBugs, to compiler plugins like Error Prone.

Java’s strengths come with some weaknesses:

  • Deserialization vulnerabilities. Java is particularly susceptible to deserialization vulnerabilities. Malicious actors can exploit deserialization to execute arbitrary code, leading to significant security breaches.
  • Legacy Java code. Older Java applications or those not regularly updated may contain security flaws. As Java has evolved, some older practices and APIs are no longer considered secure, which is actually true for any language.
  • Reflection and dynamic code execution. Java’s support for reflection and dynamic code execution, while powerful, can be misused to alter the behavior of applications at runtime, potentially resulting in security vulnerabilities.

Now that we’ve discussed languages for writing secure software, let’s turn our attention to Python, the most popular programming language in general, and its specific applications in cybersecurity.

Python stands out as the best choice for organizing vulnerability tests, checking for system breaches, and similar tasks.

Python

Python is the leading programming language in many ratings and a popular choice for cybersecurity attack emulation. Being an object-oriented language, it provides multiple benefits:

  • Ease of use: Python’s transparent syntax makes it good for writing quick hacks. This ease of use enables cybersecurity programmers to prototype and implement penetration tests quickly and efficiently.
  • Automation. Python simplifies the process of writing scripts, making it ideal for automating cybersecurity tasks, such as parsing logs and extracting information from various sources.
  • Versatility. Python is a versatile language that can be used for various cybersecurity testing tasks, including penetration testing, network scanning, and vulnerability analysis.
  • Extensive library collection. Python has a vast ecosystem of libraries and frameworks that are specifically suited for cybersecurity tasks. Libraries like Scapy for packet manipulation, Requests for handling HTTP requests, and BeautifulSoup for web scraping are just a few examples.
  • Data analysis and machine learning. Python’s strengths in data analysis are increasingly important in cybersecurity for analyzing large datasets, identifying patterns, and developing AI-driven threat detection systems.
  • Integration capabilities. It can easily integrate solutions with other languages and systems, making it suitable for complex cybersecurity environments where different systems need to interact with each other.
  • Ease of learning. Python’s straightforward syntax makes it accessible for beginners, yet it remains a potent tool for complex tasks, such as pentesting a system to check its level of security against real attacks.

Python’s weaknesses in cybersecurity include:

  • Performance. Python is an interpreted language, which generally makes it slower than compiled languages like C or Rust. For high-performance requirements, such as real-time threat detection or processing large volumes of data quickly, Python’s speed can be a limiting factor.
  • Binary distribution challenges. Distributing Python applications as binaries can be challenging, particularly when you need to include various dependencies. This can be a drawback in scenarios where penetration testing tools need to be portable and easily distributable.
  • Resource intensiveness. Python applications, particularly those that involve complex simulations or large-scale network scanning, can be resource-intensive due to Python’s higher-level abstractions and garbage collection.

Secure coding practices

Regardless of the programming language you choose for your cybersecurity strategy, it’s essential to ensure that your work consistently adheres to the principles of secure coding. Here’s a checklist to follow:

  • Input validation: Always validate data received from users and external sources. Ensure that it is what you expect in terms of format and type. This ensures invariants the code expects to hold are not violated.
  • Parse, don’t validate: A more robust approach to input validation, which postulates it’s better to convert (i.e. parse) input into a correct-by-design data structure rather than validate it and then use it as given. This approach is especially useful with strongly-typed languages, where at least some measure of correctness can be ensured by the type system.
  • Output encoding: Properly encode output when data is transferred between different subsystems to avoid injection attacks. This is particularly important in web development to prevent XSS attacks.
  • Authentication and authorization: Implement strong authentication and ensure that users are authorized to access only the resources they are permitted to. Employ modern hashing algorithms, such as Argon2, scrypt, and bcrypt, which are highly regarded (at the time of writing). Ensure that their complexity parameters are set to sufficiently high levels.
  • Error handling and logging: Handle errors gracefully without providing attackers with detailed information about the system. Logs should be detailed enough to provide insight into potential security events but should not contain sensitive information.
  • Data protection: Use secure protocols like HTTPS and implement proper key management practices.
  • Dependency and third-party code management: Regularly update libraries and frameworks to patch vulnerabilities. Be cautious about third-party code and ensure it’s from a trusted source.
  • Principle of least privilege: Limit access rights for users (and systems) to the bare minimum necessary to perform their functions.
  • Secure configuration: Harden your systems by changing default settings, disabling unnecessary services, and ensuring the secure configuration of all components.
  • Cross-site request forgery (CSRF) protection: Implement anti-CSRF tokens in forms to prevent attackers from performing actions on behalf of authenticated users.
  • Avoid security by obscurity: Security should not rely on secrecy (like hidden URLs or obfuscated code). The system should remain secure even if the attacker knows how it works.
  • Regular security audits and code reviews: Regularly review your code for security vulnerabilities and fix them promptly. Automated tools can help, but manual code review is also essential.
  • Incident response plan: Have a plan in place for responding to security incidents. This plan should include steps for containment, eradication, recovery, and post-incident analysis.

Watch this video to learn about the concept of “security by design:”

Conclusion

To create secure software solutions, collaboration between offensive and defensive teams is necessary. The defensive team focuses on implementing protection at every level, whereas the offensive team aims to uncover vulnerabilities in software and systems that could be exploited. You have the option to choose which team to join. Both roles are crucial, and together, they work towards mitigating potential software security risks.

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