Dynamic Code analysis tests the application while it is executing. This methodology enables developers to discover errors and security threats that may not be spotted during static examination.
It simulates a malicious attacker or an end-user to look for runtime vulnerabilities, including web server misconfigurations. New tools are streamlining and automating this process to help CISOs sleep better at night.
What is Dynamic Analysis?
An outage caused by application issues or malware has an immediate and damaging effect on employees and customers, raising questions regarding user data protection and integrity. Such events can have lasting repercussions for a business’s reputation, which is why teams should implement dynamic analysis systems to detect bugs before reaching production.
Dynamic analysis refers to the practice of inspecting an application or software while it’s running, using specialized tools for program monitoring and behavior reporting. Fuzzers simulate unexpected inputs that stress test its resilience against them; memory analysis tools detect memory management defects like buffer overflows; while frameworks automate running different inputs through programs to observe its behavior and produce reports on them.
Dynamic analysis offers many advantages over static code analysis, chief among them the ability to quickly detect defects and vulnerabilities that would otherwise be hard to spot. Dynamic code analysis makes this possible by showing you their effects on actual application behavior rather than being limited by rules alone.
An issue in how an app handles exceptions could cause it to crash under certain conditions, making it hard for static code analysis tools to detect this error because it only manifests when running under specific circumstances.
Dynamic analysis requires running an application under evaluation in order to observe its behavior, which is known as black-box testing, unlike static code analysis which takes place within an open environment where testers have access to architecture, source code and libraries used by an application.
Static analysis can be invaluable in pinpointing numerous defects and issues on a site, yet can miss others. For instance, an attacker could potentially use clickjacking (repurposing clickable content from your website to deliver malware to visitors) which is known as clickjacking; dynamic analysis checks that your assets are properly displayed without redirecting visitors to malicious websites.
What is the Difference Between Static and Dynamic Analysis?
Dynamic analysis seeks out issues that might occur while an application is running in its production environment, by simulating attacks against its code and seeing how it responds. It serves as an excellent complement to static analysis by uncovering issues it might miss.
Dynamic analysis is used in the Quality Assurance phase of SDLC to detect issues that could be exploited during exploitation (deferring null pointers to dereference null pointers, accessing array elements beyond their end, or reusing dynamically allocated blocks without first freeing them). Static analysis, on the other hand, is conducted earlier during development to detect errors in source code that would otherwise remain undetected once an application has been compiled and run.
Static analysis plays an essential part of software lifecycle, yet is limited in its ability to address problems in complex applications. It cannot provide an in-depth view of an app and its interactions with other applications, databases and external services.
Therefore, both static and dynamic analysis should be employed concurrently in order to effectively detect all vulnerabilities. Many teams already employ dynamic analysis as part of their routine software testing and debugging process – running memory profilers or performing load/stress tests are examples of automated dynamic analysis used as part of routine software debugging activities.
Dynamic analysis combined with SAST can provide additional checks that address various forms of vulnerabilities. It can help validate SAST results as well as detect new bugs in running applications.
Dynamic Analysis tools come in all shapes and sizes; from free and open-source Valgrind to more expensive proprietary ones that offer greater speed and accuracy. However, it should be remembered that dynamic analysis can often be bypassed – for instance with packed files preventing access to their contents – making dynamic analysis less effective overall.
What are the Benefits of Dynamic Analysis?
Many development teams employ dynamic analysis in some form. From running applications to verify fixes or testing software against different inputs to simply starting up their app and watching its behavior – dynamic analysis provides invaluable information that static analysis alone cannot find such as memory leaks and null pointer dereferencing vulnerabilities.
When bugs or security threats emerge in a codebase, it must be detected quickly to avoid impacting production environments and having devastating repercussions for businesses. Dynamic analysis allows developers and testers to quickly detect issues as they execute an application so they can be debugged before hitting production environments and having disastrous consequences on businesses.
Dynamic analysis allows you to see exactly how your application runs and examine the data it generates in real-time, providing real-time visibility of performance bottlenecks, memory leaks and security flaws such as XSS attacks or SQL injection attacks that cannot be detected with static analysis alone.
Dynamic analysis differs from static analysis in that it only checks active lines as they are executed, making it much faster and more efficient. Therefore, dynamic analysis should be combined with static analysis for an in-depth examination of application codebase.
Dynamic code analysis can detect misconfigurations in your web servers and other infrastructure components that would go undetected by static scans, such as clickjacking attacks that redirect users from harmless site elements to malicious websites they hadn’t intended to visit.
Dynamic code analysis tools are simple to integrate into any development environment and continuous integration (CI) pipeline, and can even be deployed without additional overhead or workarounds. Although designed primarily for simple apps, more robust tools exist that can analyze complex multithreaded programs with multiple CPUs or GPUs utilizing them all simultaneously. Most tools offer an easy to use graphical user interface so developers can better understand what processes and threads are active while helping determine resource usage more accurately.
How to Perform Dynamic Analysis
Static code analysis is a powerful white-box testing methodology, but it isn’t effective when it comes to testing running applications and their dependencies. Dynamic analysis, on the other hand, looks at the tangible actions of software and provides a real-time view of potential security vulnerabilities that would be impossible to detect with static tools. This method can reduce mean time to identification for production incidents, improve visibility into application functionality and provide better risk management throughout the lifecycle of an enterprise application.
Dynamic analysis requires the program to be executed, which can be a lengthy process in the case of complex applications. To speed up the solution process, dynamic analysis uses a technique called program slicing. Essentially, the goal is to find a reduced form of the program that produces the selected behavior subset. For example, if you want to know the mode shapes for a building under vibration, the dynamic analysis will calculate the natural periods and mode shape of each frame member, solve the RSA and then compare predicted forces against allowable stresses based on the design codes.
As a result, reducing the time it takes to debug and identify bugs can significantly decrease the overall development and maintenance of an application. Performing dynamic analysis on a live application can also help developers quickly identify and isolate memory or performance issues, as well as security risks resulting from dependencies tied to the application such as database servers, web application services or third-party integrations.
While many tools claim to be capable of dynamic analysis, they are often limited in scope and may not be able to effectively handle complex applications. In addition, they are not as flexible or scalable as other solutions.
A dynamic analysis tool should be robust enough to handle complex applications and provide an easy-to-use graphical interface so that developers can control and examine the information gathered during the process. TotalView offers a complete suite of dynamic analysis tools for identifying complex problems that can affect application performance, security and reliability. Learn more about the benefits of using dynamic analysis or start a free trial to see how it can benefit your organization.
