Proactively testing software for bugs is not new. The earliest examples date back to the 1950s with the term fuzzing. Fuzzing as we now refer to it is the injection of random inputs and commands into applications. It made its debut quite literally on a dark and stormy night in 1988. Since then, application fuzzing has become a staple of the secure software development lifecycle (SDLC), and according to Gartner*, security testing is growing faster than any other security market, as AST solutions adapt to new development methodologies and increased application complexity.
We believe there is good reason for this. The overall security risk profile of applications has grown in lockstep with accelerated software development and application complexity. Hackers are also aware of the increased vulnerabilities and, as the recent Equifax breach highlights, the application layer is highly targeted. Despite this, the security and development groups within organizations cannot find easy alignment to implement application fuzzing.
While DevOps is transforming the speed at which applications are created, tested, and integrated with IT, that same efficiency hampers the ability to mitigate identified security risks and vulnerabilities, without impacting business priorities. This is exactly the promise that machine learning, artificial intelligence (AI), and the use of deep neural networks (DNN) are expected to deliver on in evolved software vulnerability testing.
Most customers I talk to see AI as a natural next step given that most software testing for bugs and vulnerabilities is either manual or prone to false positives. With practically every security product claiming to be machine learning and AI-enabled, it can be hard to understand which offerings can deliver real value over current approaches.
Adoption of the latest techniques for application security testing doesnt mean CISOs must become experts in machine learning. Companies like Microsoft are using the on-demand storage and computing power of the cloud, combined with experience in software development and data science, to build security vulnerability mitigation tools that embed this expertise in existing systems for developing, testing, and releasing code. It is important, however, to understand your existing environment, application inventory, and testing methodologies to capture tangible savings in cost and time. For many organizations, application testing relies on tools that use business logic and common coding techniques. These are notoriously error-prone and devoid of security expertise. For this latter reason, some firms turn to penetration testing experts and professional services. This can be a costly, manual approach to mitigation that lengthens software shipping cycles.
Modern application security testing that is continuous and integrated with DevOps and SecOps can be transformative for business agility and security risk management. Consider these key use cases and whether your organization has embedded application security testing for each:
- Digital Transformation moving applications to the cloud creates the need to re-establish security controls and monitoring. Fuzzing can uncover errors and missed opportunities to shore up defenses. Automated and integrated fuzzing can further preserve expedited software shipping cycles and business agility.
- Securing the Supply Chain Open Source Software (OSS) and 3rd party applications are a common vector of attack, as we saw with Petya, so a testing regimen is a core part of a plan to manage 3rd party risk.
- Risk Detection whether building, maintaining, or refactoring applications on premises, the process and risk profile have become highly dynamic.Organizations need to be proactive to uncover bugs, holes and configuration errors on a continuous basis to meet both internal and regulatory risk management mandates.
Of course, software development and testing are about more than just tools. The process to communicate risks to all stakeholders, and to act, is where the real benefit materializes. A barrier to effective application security testing is the highly siloed way that testing and remediation are conducted. Development waits for IT and security professionals to implement the changesslowing deployment and time to market. Legacy application security testing is ready for disruption and the built-in approach can deliver long-awaited efficiency in the development and deployment pipeline. Digital transformation, supply chain security, and risk detection all benefit from speed and agility. Lets consider the DevOps and SecOps workflows possible on a Microsoft-based application security testing framework:
- DevOps Continuous fuzzing built into the DevOps pipeline identifies bugs and feeds them to the continuous integration and deployment environment (i.e. Visual Studio Team Services and Team Foundation Server). Developers and stakeholders are uniformly advised of risky code and provided the option of running additional Azure-based fuzzing techniques. For apps in production that are found to be running risky code, IT pros can mitigate risks by using PowerShell and Group Policy (GPO) to enable the features of Windows Defender Exploit Guard. While the apps continue to run, the attack surface can be reduced, and connection scenarios which increase risk are blocked. This gives teams time to develop and implement mitigations without having to take the applications entirely offline.
- SecOps – Azure-hosted containers and VMs, as well as on-premise machines, are scanned for risky applications and code including OSS. The results inform Microsofts various desktop, mobile, and server threat protection regimes, including application whitelisting. Endpoints can be scanned for the presence of the risky code and administrators are informed through Azure Security Center. Mitigations can also be deployed to block those applications implicated and enforce conditional access through Azure Active Directory.
Cloud and AI
Machine learning and artificial intelligence are not new, but the relatively recent availability of graphics processing units (GPUs) have brought their potential to mainstream by enabling faster (parallel) processing of large amounts of data. Our recently announced Microsoft Risk Detection (MSRD) service is a showcase of the power of the cloud and AI to evolve fuzz testing. In fact, Microsofts award winning work in a specialized area of AI called constraint solving has been 10 years in the making and was used to produce the worlds first white-box fuzzer.
A key to effective application security testing is the inputs or seeds used to establish code paths and bring about crashes and bug discovery. These inputs can be static and predetermined, or in the case of MSRD, dynamic and mutated by training algorithms to generate relevant variations based on previous runs. While AI and constraint solving are used to tune the reasoning for finding bugs, Azure Resource Manager dynamically scales the required compute up or down creating a fuzzing lab that is right-sized for the customers requirement. The Azure based approach also gives customers choices in running multiple fuzzers, in addition to Microsofts own, so the customer gets value from several different methods of fuzzing.
For Microsoft, application security testing is fundamental to a secure digital transformation. MSRD for Windows and Linux workloads is yet another example of our commitment to building security into every aspect of our platform. While our AI-based application fuzzing is unique, Microsoft Research is already upping the ante with a new project for neural fuzzing. Deep neural networks are an instantiation of machine learning that model the human brain. Their application can improve how MSRD identifies fuzzing locations and the strategies and parameters used. Integration with our security offerings is in the initial phases, and by folding in more capabilities over time we remove the walls between IT, developers, and security, making near real-time risk mitigation a reality. This is the kind of disruption that, as a platform company, Microsoft uniquely brings to application security testing for our customers and serves as further testament for the power of built-in.
* Gartner: Magic Quadrant for Application Security Testing published: 28 February 2017 ID: G00290926
*** This is a Security Bloggers Network syndicated blog from Microsoft Secure authored by Jenny Erie. Read the original post at: https://cloudblogs.microsoft.com/microsoftsecure/2018/01/03/application-fuzzing-in-the-era-of-machine-learning-and-ai/