Software Composition Analysis (SCA) is a fundamental component of application security, aiming to identify vulnerabilities within third-party libraries and dependencies. However, while SCA tools have come a long way, they are not always accurate enough on their own. In this blog post, we'll explore the limitations of SCA and discuss how combining it with other technical capabilities can significantly improve accuracy and coverage in identifying vulnerabilities within your applications.
SCA tools work by scanning the dependencies of an application to identify known vulnerabilities. However, they often face several challenges:
In our quest for more accurate and reliable application security, it's imperative to recognize the shortcomings of Software Composition Analysis (SCA) and the valuable role that static analysis plays in filling these gaps. While SCA tools are essential for identifying vulnerabilities within third-party libraries and dependencies, they are not without their limitations.
One significant challenge with SCA tools is the potential for false positives. These tools may flag vulnerabilities related to installed libraries that are not actively used by the application. This can lead to unnecessary alerts that divert the attention of development teams from addressing critical issues. Additionally, even when a library is indeed used, SCA tools might overlook the crucial context of whether the vulnerable function within that library is called by the application's code.
This is where static analysis comes to the rescue. Static analysis can be used to examine the source code of the application itself and provide crucial insights that enhance the accuracy of SCA results.
Firstly, static analysis can help determine if the library identified by SCA is actually used within the application's codebase. This verification process ensures that SCA findings are contextually relevant, eliminating the noise of unused libraries and preventing false positives.
Secondly, static analysis can delve deeper into the code to ascertain whether the vulnerable functions within these libraries are genuinely being invoked by the application. This added layer of scrutiny helps filter out irrelevant vulnerabilities and prioritize critical issues that pose actual risks.
However, it's essential to acknowledge the limitations of this approach. While static analysis can significantly improve the accuracy of SCA results by confirming library usage and identifying calls to vulnerable functions, it does have constraints.
One notable limitation is its inability to handle dynamically loaded libraries effectively. In scenarios where libraries are loaded dynamically at runtime, traditional static analysis struggles to identify them, potentially missing vulnerabilities in these dynamically loaded components.
Moreover, static analysis relies on dataflow analysis, which is inherently limited. This technique traces the flow of data and control within the code to identify vulnerabilities, but it cannot account for all runtime behaviors and complex interactions. As a result, static dataflow analysis may still yield false positives and false negatives, albeit to a lesser extent compared to standalone SCA.
Combining SCA with static analysis is a powerful strategy for enhancing the accuracy of vulnerability detection within your applications. It helps eliminate false positives related to unused libraries and ensures that identified vulnerabilities are contextually relevant. However, it's crucial to recognize the inherent limitations of static analysis, particularly in scenarios involving dynamically loaded libraries and complex dataflow, where a more holistic approach may be necessary to achieve comprehensive application security.
In addition to enhancing SCA with static analysis, another approach to further improve the accuracy of vulnerability detection is by incorporating runtime context. By analyzing the application's behavior during runtime, we can gain deeper insights into how vulnerabilities may be exploited in real-world scenarios.
Runtime context can provide valuable information such as the interactions between different components, the data flows within the application, and the actual usage of libraries and functions. This dynamic analysis can help identify vulnerabilities that may not be apparent through static analysis alone.
By combining static analysis, SCA, and runtime context, we can create a more comprehensive and effective approach to application security. This holistic approach enables us to detect vulnerabilities with higher accuracy and prioritize critical issues for remediation.
Runtime instrumentation is a widely recognized technique used in the development of Interactive Application Security Testing (IAST) products. By incorporating runtime instrumentation into the analysis process, we can obtain a highly accurate and comprehensive understanding of an application's behavior during runtime. This level of insight is particularly valuable when it comes to enhancing the accuracy of Software Composition Analysis (SCA) results.
In the context of SCA, runtime instrumentation allows us to monitor the libraries that are actually loaded by the application. This enables us to go beyond the limitations of static analysis and detect the usage of libraries in a much more precise and reliable way. By leveraging the power of runtime instrumentation, we can identify dynamically-loaded libraries that would have otherwise been missed by traditional static analysis techniques.
One of the key advantages of using runtime instrumentation for SCA is its ability to provide real-time information about the application's behavior. By closely monitoring the libraries that are being loaded and utilized by the application, we can obtain accurate and up-to-date insights into the actual usage of these libraries. This helps us eliminate false positives and ensures that the vulnerabilities identified by SCA are contextually relevant and pose actual risks to the application.
Moreover, runtime instrumentation allows us to capture complex interactions and data flows within the application. This dynamic analysis provides valuable information that may not be apparent through static analysis alone. By analyzing the interactions between different components, the flow of data within the application, and the usage of libraries and functions, we can gain deeper insights into how vulnerabilities may be exploited in real-world scenarios.
In summary, incorporating runtime instrumentation into the SCA process is a powerful strategy for improving the accuracy and effectiveness of vulnerability detection. By monitoring the libraries that are actually loaded by the application, we can overcome the limitations of static analysis and detect the usage of libraries in a more accurate and reliable way. This holistic approach, combining static analysis, SCA, and runtime context, enables us to identify vulnerabilities with higher accuracy and prioritize critical issues for remediation.
While runtime instrumentation has been proven to be more accurate in detecting loaded libraries and vulnerable calls, there is still an important topic that needs to be addressed in order to fully leverage this powerful approach: expanding the coverage.
IAST is highly effective and precise in identifying vulnerabilities at runtime, particularly in staging or testing environments. Its ability to operate at runtime makes it more accurate than SAST and reduces the likelihood of false positive results. However, if the testing coverage is inadequate, certain data flows may remain unexamined and vulnerabilities could potentially be overlooked.
To tackle this issue and maximize the effectiveness of IAST, it is crucial to combine this approach with the fuzzing techniques commonly used in DAST. By generating traffic within the tested application, we can significantly increase the coverage and ensure that all possible scenarios are triggered. This not only enhances the detection of injection vulnerabilities by IAST, but also broadens the coverage of loaded libraries and vulnerable calls detections.
DAST also has its own shortcomings and cannot generate high-quality traffic on its own. And again, we can see the benefits of having a holistic approach composed of various top-notch technologies working together towards a common goal.
API Discovery, which is at the core of API Security, focuses on identifying all endpoints within the application. It not only identifies the endpoints themselves but also how they can be accessed, the expected parameters, the HTTP method, and the authentication process, among other things. This is precisely the missing component that DAST needed to generate high-quality traffic.
API Discovery is a broad topic on its own with various underlying technologies and use cases. Here I would like to focus on its integration in the holistic approach. This is the key point for turning a standalone tool into an effective team member contributing to the quality of the SCA results and their prioritization.
The combination of these approaches, namely IAST, DAST and API Discovery, is an incredibly powerful tool for prioritizing potential vulnerabilities identified by SCA. It provides invaluable insights that no other security platform can provide. That’s not a correlation engine that would work on final results of various tools, but a holistic approach where each tool contributes to the high-quality results shown to the user. By focusing specifically on those vulnerabilities that are truly exploitable in the running application, organizations can ensure a more comprehensive and robust security testing process.
In an ideal world, all the detected vulnerabilities would be fixed right away. But even if the AppSec platform shows you only accurate and exploitable vulnerabilities, that’s not always enough for having those vulnerabilities fixed all at once.
Developers are already busy and fixing vulnerabilities is not always an easy task. It can take time to fix all the vulnerabilities in your applications, and in the meantime, those vulnerabilities are still there, exploitable by malicious actors.
So let’s imagine the situation where the AppSec platform already knows that your application is using a vulnerable version of some library, that this library is actually loaded by the application at runtime, and also that the vulnerable function of this library is called by your application in some situations. This vulnerability has been reported to the developers and they are already working on a fix but they haven't yet managed to deploy it. In that situation, runtime protection can be incredibly useful by precisely blocking the malicious calls to that vulnerable function in order to prevent potential damages caused by this attack. Nice!
In this article, we have thoroughly examined the numerous advantages and benefits that arise from adopting a holistic approach when developing an Application Security (AppSec) platform. By integrating and leveraging the most advanced and cutting-edge technologies to augment each other, we are able to deliver exceptional results in terms of Software Composition Analysis (SCA) to ensure utmost satisfaction for our esteemed customers.
Of course, adopting a holistic approach has numerous advantages that extend beyond just SCA. In addition to the benefits mentioned, we can explore how these technologies can collaborate and integrate with other aspects of application security. By considering the bigger picture, we can gain a deeper understanding of how these technologies can complement each other and enhance overall security measures. In upcoming articles, we will delve into the ways in which these technologies can synergize and harmonize with different facets of application security, providing a comprehensive and robust security framework.
The Oxeye Application Security Platform is an application security prioritization engine that surfaces and prioritizes critical application risks, instead of just listing potential vulnerabilities. Our runtime-fueled prioritization engine takes your business priorities into account when analyzing your environmental risks, vulnerabilities, and current AppSec tool results to provide a consolidated view of your application threats. This means that your SCA findings are provided with additional context that helps to filter out package vulnerabilities that aren’t exploitable. To learn more about how Oxeye can help you streamline your AppSec program, lighten your developers’ workload and reduce technical debt, contact us to schedule a conversation.