Automated Software Development Tools for Improving IoT Device Security
Posted by Bill Graham
For IoT and M2M device security assurance, it's critical to introduce automated software development tools into the development lifecycle. Although software tools' roles in quality assurance is important, it becomes even more so when security becomes part of a new or existing product's requirements.
Automated Software Development Tools
There are three broad categories of automated software development tools that are important for improving quality and security in embedded IoT products:
Application lifecycle management (ALM): Although not specific to security, these tools cover requirements analysis, design, coding, testing and integration, configuration management, and many other aspects of software development. However, with a security-first embedded development approach, these tools can help automate security engineering as well. For example, requirements analysis tools (in conjunction with vulnerability management tools) can ensure that security requirements and known vulnerabilities are tracked throughout the lifecycle. Design automation tools can incorporate secure design patterns and then generate code that avoids known security flaws (e.g. avoiding buffer overflows or checking input data for errors). Configuration management tools can insist on code inspection or static analysis reports before checking in code. Test automation tools can be used to test for "abuse" cases against the system. In general, there is a role for ALM tools in the secure development just as there is for the entire project.
Dynamic Application Security Testing (DAST): Dynamic testing tools all require program execution in order to generate useful results. Examples include unit testing tools, test coverage, memory analyzers, and penetration test tools. Test automation tools are important for reducing the testing load on the development team and, more importantly, detecting vulnerabilities that manual testing may miss.
Static Application Security Testing (SAST): Static analysis tools work by analyzing source code, bytecode (e,g, compiled Java), and binary executable code. No code is executed in static analysis, but rather the analysis is done by reasoning about the potential behavior of the code. Static analysis is relatively efficient at analyzing a codebase compared to dynamic tools. Static analysis tools also analyze code paths that are untested by other methods and can trace execution and data paths through the code. Static analysis can be incorporated early during the development phase for analyzing existing, legacy, and third-party source and binaries before incorporating them into your product. As new source is added, incremental analysis can be used in conjunction with configuration management to ensure quality and security throughout.
Figure 1: The application of various tool classes in the context of the software development lifecycle.
Although adopting any class of tools helps productivity, security, and quality, using a combination of these is recommended. No single class of tools is the silver bullet[1]. The best approach is one that automates the use of a combination of tools from all categories, and that is based on a risk-based rationale for achieving high security within budget.
The role of static analysis tools in a security-first approach
Static analysis tools provide critical support in the coding and integration phases of development. Ensuring continuous code quality, both in the development and maintenance phases, greatly reduces the costs and risks of security and quality issues in software. In particular, it provides some of the following benefits:
Continuous source code quality and security assurance: Static analysis is often applied initially to a large codebase as part of its initial integration as discussed below. However, where it really shines is after an initial code quality and security baseline is established. As each new code block is written (file or function), it can be scanned by the static analysis tools, and developers can deal with the errors and warnings quickly and efficiently before checking code into the build system. Detecting errors and vulnerabilities (and maintaining secure coding standards, discussed below) in the source at the source (developers themselves) yields the biggest impact from the tools.
Tainted data detection and analysis: Analysis of the data flows from sources (i.e. interfaces) to sinks (where data gets used in a program) is critical in detecting potential vulnerabilities from tainted data. Any input, whether from a user interface or network connection, if used unchecked, is a potential security vulnerability. Many attacks are mounted by feeding specially-crafted data into inputs, designed to subvert the behavior of the target system. Unless data is verified to be acceptable both in length and content, it can be used to trigger error conditions or worse. Code injection and data leakage are possible outcomes of these attacks, which can have serious consequences.
Third-party code assessment: Most projects are not greenfield development and require the use of existing code within a company or from a third party. Performing testing and dynamic analysis on a large existing codebase is hugely time consuming and may exceed the limits on the budget and schedule. Static analysis is particularly suited to analyzing large code bases and providing meaningful errors and warnings that indicate both security and quality issues. GrammaTech CodeSonar binary analysis can analyze binary-only libraries and provide similar reports as source analysis when source is not available. In addition, CodeSonar binary analysis can work in a mixed source and binary mode to detect errors in the usage of external binary libraries from the source code.
Secure coding standard enforcement: Static analysis tools analyze source syntax and can be used to enforce coding standards. Various code security guidelines are available such as SEI CERT C [2] and Microsoft's Secure Coding Guidelines [3]. Coding standards are good practice because they prevent risky code from becoming future vulnerabilities. As mentioned above, integrating these checks into the build and configuration management system improves the quality and security of code in the product.
As part of a complete tools suite, static analysis provides key capabilities that other tools cannot. The payback for adopting static analysis is the early detection of errors and vulnerabilities that traditional testing tools may miss. This helps ensure a high level of quality and security on an on-going basis.
Conclusion
Machine to machine and IoT device manufacturers incorporating a security-first design philosophy with formal threat assessments, leveraging automated tools, produce devices better secured against the accelerating threats on the Internet. Modifying an existing successful software development process that includes security at the early stages of product development is key. Smart use of automated tools to develop new code and analyze existing and third party code allows development teams to meet strict budget and schedule constraints. Static analysis of both source and binaries plays a key role in a security-first development toolset.
References
No Silver Bullet – Essence and Accident in Software Engineering, Fred Brooks, 1986
SEI CERT C Coding Standard,
Outsource Code Development Driving Automated Test Tool Market, VDC Research, IoT & Embedded Blog, October 22, 2013
