Software security is a critical aspect of any software development practice. Implementing resilient software that is free from vulnerabilities requires a thorough understanding of common threats, attack vectors, and effective methods for detecting and mitigifying those threats. One such method involves code analysis, which can be classified into two types, namely, Static Code Analysis and Dynamic Code Analysis. In this article, we delve into each of these practices in great detail, providing examples, use cases, and best practices.
Static Code Analysis, often termed as Static Application Security Testing (SAST), is a method of debugging by examining source code before a program is run. It's done by using tools and processes that analyze the code for potential vulnerabilities and errors.
Consider this simple JavaScript example:
var userEntry = document.getElementById('userInput').value;
eval(userEntry);
In the above code, we are directly using the user's input in the eval function, which is a severe security threat as it can execute any arbitrary JavaScript code. A static code analyzer would flag this potential security vulnerability.
Static code analysis involves inspecting the code without executing it. This method can discover many types of issues, such as syntax errors, coding standard violations, type mismatches, etc. early in the development process. More importantly, it can point out vulnerabilities such as potential SQL injections, buffer overflows, and other security threats.
During static code analysis, the source code is analyzed, checking for patterns that indicate potential issues. This method does not need the code to be running, meaning it can be performed at any stage of the software development process. It can scan a hundred percent of the codebase for errors, which is why it's an extremely effective method to support secure coding standards.
While using tools can automate the process of static code analysis, it should be complemented by other good practices. For instances, using a coding standard and adhering to it can drastically reduce the number of issues that static code analysis would otherwise have to flag.
Conditions for errors should also be handled properly. The software should be able to work under all possible configurations, which can be verified by ensuring that all branches (like if, else) have test cases for their execution.
False Positives - A common setback of SCA is the high number of false positives it can generate. It might identify a section of code as vulnerable when it isn't which can lead to wasted time and resources in trying to mend a non-existent issue.
Incomplete Coverage - SCA cannot analyze parts that are not reachable through code, such as libraries or frameworks that are only run at the execution time. Thus, it potentially leaves software vulnerable.
Limited Scope - SCA typically analyzes one file or one project at a time but doesn't consider how multiple applications might interact with each other. This may lead to missed interaction-based vulnerabilities.
Dynamic Code Analysis is a method of analyzing an application during its execution. It's often referred to as Dynamic Application Security Testing (DAST) and primarily aims at identifying security vulnerabilities, and behavioral or runtime errors.
Dynamic code analysis is primarily done by executing the programmed code and analyzing the output for expected or unexpected results. It identifies issues that static code analysis can't, like concurrency issues, performance problems, etc. It's often used to find vulnerabilities that can be exploited by attackers, like runtime injection attacks and authentication issues.
Dynamic code analysis is crucial for detecting flaws that static analysis might miss, such as runtime errors, authentication issues, and access control problems. However, it is not a one-stop solution for security. It needs to be part of a larger security ecosystem that includes static analysis and manual code review to ensure comprehensive application security coverage.
During dynamic code analysis, the code is run, and different inputs are provided to it. This type of analysis can reveal how the program behaves during execution and can find issues like memory leaks and buffer overruns, which might not be noticeable during static code analysis.
For instance, consider an application that takes user input for their credentials. A dynamic code analysis solution would essentially act as an attacker and attempt to input unexpected or malicious data instead of normal user credentials to test how the software responds.
Dynamic code analysis tools (like BurpSuite, Zap, etc.) intercept and record HTTP/S traffic, which can reveal runtime issues such as unhandled exceptions, reference errors, etc.
Just like static code analysis, dynamic code analysis should be done regularly and complemented by good coding practices. Dynamic analysis often requires that all software dependencies be properly configured. Also, given that dynamic analysis only checks the sections of code that get executed, complete code coverage is necessary to ensure maximum efficiency.
Perform dynamic analysis in a controlled environment to prevent accidental damages or security breaches. Be aware of the performance impact of dynamic code analysis and consider running these tests during non-peak times to avoid disrupting services.
Execution Environment - DCA requires complete staging of execution environments which can be time consuming and resource intensive. Also, reproducing actual runtime environments is often difficult, and this may lead to certain problems not being detected.
Time Consuming - DCA is typically slower than SCA as it requires the completion of the coding phase and configuration of test environments. Thus, this delay could represent a significant disadvantage especially in agile development environments.
Escaped Defects - DCA can only analyze the vulnerabilities it has test cases for and the paths that are covered during the execution. Any defect that is not triggered during testing will escape being detected.
Both static and dynamic code analysis are critical in the software development process. When used effectively, these analysis techniques can yield valuable insights, catch errors early, and ensure that the final product is as secure and reliable as possible. By adhering to good coding practices and implementing thorough static and dynamic code analysis, software developers can greatly enhance the security of their software.
