Dynamic vs Static Code Analysis in Detecting Vulnerabilities

Security remains a paramount issue in software development, particularly as applications become increasingly sophisticated and interconnected. The necessity for early detection of source code vulnerabilities intensifies in response to these complexities. To maintain the code integrity and security, developers and security experts utilise a variety of methodologies, among which static and dynamic code analysis are fundamental.

But first, what is source code? Source code is the foundational component of software, written in a programming language that is understandable to humans. It is the set of instructions that dictates how a program operates and interacts with the systems it’s designed for. Every function, every interface, and every click processed by an application traces back to lines of source code.

However, within these lines of code can lurk vulnerabilities – weaknesses that, if not addressed, provide pathways for attackers. These vulnerabilities are not trivial; they represent serious threats that can lead to consequences such as data breaches, jeopardising user safety, and severely damaging brand reputations. These are not hypothetical scenarios but real risks businesses and users face in an increasingly digital world. In 2023, OpenAI identified a bug in the source code of its AI-enabled chatbot, ChatGPT, which risked a data leak.

The issue was traced back to a vulnerability in the Redis open-source library that ChatGPT uses. OpenAI reported that this flaw allowed “some users” to view “titles from another active user’s chat history.” There was also the potential for users to see the opening message of another user’s new conversation if both were interacting with the chatbot concurrently. In addition to these concerns, OpenAI indicated that the bug might have resulted in the “unintentional visibility of payment-related information” for some premium ChatGPT users.

This underscores the critical importance of static and dynamic code analysis.

Static code analysis, also called static application security testing (SAST), is a type of security testing that reviews the source code of an application line by line to detect potential vulnerabilities that could be exploited once the application is deployed. The static code analysis methodology involves using tools that are designed to analyse the source code in its non-runtime environment. One primary tool in this category is the Style Checking Tool. It ensures the code aligns with an organisation’s prescribed programming conventions and standards.

Advancing in complexity, Semantic Analysis Tools delve deeper into the code’s anatomy. They enhance syntax trees with additional context, making it possible to spot issues like faulty data types, uninitialised variables, and unnecessary methods.

The most advanced tier of static code analysis is represented by Deep Flow Static Analysis tools. By generating a “control flow graph” and conducting an extensive data flow analysis, they can identify complex security threats such as buffer overflows and race conditions, which can slip through less thorough analysis methods.

The implementation of static code analysis brings to the table a range of benefits, among which is the potential for early detection of vulnerabilities. Identifying security issues during the development phase significantly diminishes remediation costs, avoiding the substantial resources required to rectify security shortcomings post-deployment. This proactive approach not only saves time and money but also prevents the reputational damage that can accompany exposed vulnerabilities.

However, static code analysis isn’t without its limitations. For instance, it may generate false positives, indicating a potential security risk when none exists. This requires manual oversight to determine which issues are actionable, which can be resource-intensive. Additionally, static code analysis cannot identify runtime vulnerabilities, meaning it must be part of a broader security strategy that includes dynamic analysis for a comprehensive approach.

Unlike its static counterpart, dynamic code analysis involves examining the system’s behaviour during execution, offering insights unattainable in a non-runtime environment. Dynamic code analysis, also known as dynamic application security testing (DAST), is a security testing methodology that analyses an application in its running state. Essentially, it’s like testing a car’s performance by driving it rather than inspecting its components individually. This form of testing is vital for identifying vulnerabilities that only become apparent during runtime, such as authentication problems, access violations, and insecure data processing.

Conducting a dynamic code analysis involves utilising automated tools and manual techniques to simulate attacks on an application and observe its response. The process often includes inputting unexpected or malicious data and analysing how the application processes it, monitoring for aberrations, crashes, or data leaks that could indicate a security vulnerability.

The real-time aspect of dynamic code analysis helps it identify security issues stemming from user input, network interactions, or the environment conditions – problems often missed by static analysis. However, it may require more resources and time, sometimes slowing down the development process. Also, since it is conducted at runtime, any error found requires the application to be sent back for redevelopment, which might be costlier than early detection methods.

So which one should you choose when it comes to securing your applications? When considering dynamic vs static code analysis, it is vital to view them as complementary rather than opposing strategies. While static analysis offers early detection of potential vulnerabilities, dynamic analysis simulates real-world attack scenarios for a more realistic assessment. This combination allows for comprehensive vulnerability detection and efficient remediation, bolstering the software’s resilience against impending cybersecurity threats.

Overall, prioritising the detection of code vulnerabilities is non-negotiable in today’s digital landscape. Both static and dynamic code analysis play critical roles in safeguarding applications against potential breaches. By understanding and integrating these methods, development and security teams can work hand-in-hand to mitigate risks, protect user data, and uphold the integrity and security of their software solutions.

Whether through the early intervention of static analysis or the real-world testing of dynamic analysis, a multi-layered approach is your strongest bet against cyber vulnerabilities. To get started, don’t hesitate to reach out to softScheck and discover how the services of a cybersecurity consulting company can support your organisation in Singapore.