Top 10 Lists for Designing and Writing Secure and Safe Software

If you care about writing secure code, should know all about these Top 10 lists:

OWASP Top 10

The OWASP Top 10 is a community-built list of the 10 most common and most dangerous security problems in online (especially web) applications. Injection flaws, broken authentication and session management, XSS and other nasty security bugs.

These are problems that you need to be aware of and look for, and that you need to prevent in your design and coding. The Top 10 explains how to test for each kind of problem to see if your app is vulnerable (including common attack scenarios), and basic steps you can take to prevent each problem.

If you’re working on mobile apps, take time to understand the OWASP Top 10 Mobile list.

IEEE Top Design Flaws

The OWASP Top 10 is written more for security testers and auditors than for developers. It’s commonly used to classify vulnerabilities found in security testing and audits, and is referenced in regulations like PCI-DSS.

The IEEE Center for Secure Design, a group of application security experts from industry and university researchers, has taken a different approach. They have come up with a Top 10 list that focuses on identifying and preventing common security mistakes in architecture and design.

This list includes good design practices such as: earn or give, but never assume trust; identify sensitive data and how they should be handled; understand how integrating external components changes your attack surface. The IEEE’s list should be incorporated into design patterns and used in design reviews to try and deal with security issues early.

OWASP Proactive Controls

IEEE’s approach is principle-based – a list of things that you need to think about in design, in the same way that you think about things like simplicity and encapsulation and modularity.

The OWASP Proactive Controls, originally created by security expert Jim Manico, is written at the developer level. It is a list of practical, concrete things that you can do as a developer to prevent security problems in coding and design. How to parameterize queries, and encode or validate data safely and correctly. How to properly store passwords and to implement a forgot password feature. How to implement access control – and how not to do it.

It points you to Cheat Sheets and other resources for more information, and explains how to leverage the security features of common languages and frameworks, and how and when to use popular, proven security libraries like Apache Shiro and the OWASP Java Encoder.

Katy Anton and Jason Coleman have mapped all of these controls together (the OWASP Top 10, OWASP Proactive Controls and the IEEE Security Flaws), showing how the OWASP Proactive Controls implement safe design practices from the IEEE list and how they prevent or mitigate OWASP Top 10 risks.

You can use these maps to look for gaps in your application security practices, in your testing and coding, and in your knowledge, to identify areas where you can learn and improve.

What does DevOps mean to Developers?

A post that I wrote for JavaWorld DevOps for Developers: A New Agility, describes how DevOps changes the way that developers work, and what they need to know to succeed.

Does DevOps Reduce Technical Debt – or Make it Worse?

DevOps can help reduce technical debt in some fundamental ways.

Continuous Delivery/Deployment

First, building a Continuous Delivery/Deployment pipeline, automating the work of migration and deployment, will force you to clean up inconsistencies and holes in configuration and code deployment, and inconsistencies between development, test and production environments.

And automated Continuous Delivery and Infrastructure as Code gets rid of dangerous one-of-a-kind snowflakes and configuration drift caused by making configuration changes and applying patches manually over time. Which makes systems easier to setup and manage, and reduces the risk of an un-patched system becoming the target of a security attack or the cause of an operational problem.

A CD pipeline also makes it easier, cheaper and faster to pay down other kinds of technical debt. With Continuous Delivery/Deployment, you can test and push out patches and refactoring changes and platform upgrades faster and with more confidence.

Positive Feedback

The Lean feedback cycle and Just-in-Time prioritization in DevOps ensures that you’re working on whatever is most important to the business. This means that bugs and usability issues and security vulnerabilities don’t have to wait until after the next feature release to get fixed. Instead, problems that impact operations or the users will get fixed immediately.

Teams that do Blameless Post-Mortems and Root Cause(s) Analysis when problems come up will go even further, and fix problems at the source and improve in fundamental and important ways.

But there’s a negative side to DevOps that can add to technical debt costs.

Erosive Change

Michael Feathers’ research has shown that constant, iterative change is erosive: the same code gets changed over and over, the same classes and methods become bloated (because it is naturally easier to add code to an existing method or a method to an existing class), structure breaks down and the design is eventually lost.

DevOps can make this even worse.

DevOps and Continuous Delivery/Deployment involves pushing out lots of small changes, running experiments and iteratively tuning features and the user experience based on continuous feedback from production use.

Many DevOps teams work directly on the code mainline, “branching in code” to “dark launch” code changes, while code is still being developed, using conditional logic and flags to skip over sections of code at run-time. This can make the code hard to understand, and potentially dangerous: if a feature toggle is turned on before the code is ready, bad things can happen.

Feature flags are also used to run A/B experiments and control risk on release, by rolling out a change incrementally to a few users to start. But the longer that feature flags are left in the code, the harder it is to understand and change.

There is a lot of housekeeping that needs to be done in DevOps: upgrading the CD pipeline and making sure that all of the tests are working; maintaining Puppet or Chef (or whatever configuration management tool you are using) recipes; disciplined, day-to-day refactoring; keeping track of features and options and cleaning them up when they are no longer needed, getting rid of dead code and trying to keep the code as simple as possible.

Microservices and Technology Choices

Microservices are a popular architectural approach for DevOps teams.

This is because loosely-coupled Microservices are easier for individual teams to independently deploy, change, refactor or even replace.

And a Microservices-based approach provides developers with more freedom when deciding on language or technology stack: teams don’t necessarily have to work the same way, they can choose the right tool for the job, as long as they support an API contract for the rest of the system.

In the short term there are obvious advantages to giving teams more freedom in making technology choices. They can deliver code faster, quickly try out prototypes, and teams get a chance to experiment and learn about different technologies and languages.

But Microservices “are not a free lunch”. As you add more services, system testing costs and complexity increase. Debugging and problem solving gets harder. And as more teams choose different languages and frameworks, it’s harder to track vulnerabilities, harder to operate, and harder for people to switch between teams. Code gets duplicated because teams want to minimize coupling and it is difficult or impossible to share libraries in a polyglot environment. Data is often duplicated between services for the same reason, and data inconsistencies creep in over time.

Negative Feedback

There is a potentially negative side to the Lean delivery feedback cycle too.

Constantly responding to production feedback, always working on what’s most immediately important to the organization, doesn’t leave much space or time to consider bigger, longer-term technical issues, and to work on paying off deeper architectural and technical design debt that result from poor early decisions or incorrect assumptions.

Smaller, more immediate problems get fixed fast in DevOps. Bugs that matter to operations and the users can get fixed right away instead of waiting until all the features are done, and patches and upgrades to the run-time can be pushed out more often. Which means that you can pay off a lot of debt before costs start to compound.

But behind-the-scenes, strategic debt will continue to add up. Nothing’s broke, so you don’t have to fix anything right away. And you can’t refactor your way out of it either, at least not easily. So you end up living with a poor design or an aging technology platform, slowly slowing down your ability to respond to changes, to come up with new solutions. Or forcing you to continue filling in security holes as they come up, or scrambling to scale as load increases.

DevOps can reduce technical debt. But only if you work in a highly disciplined way. And only if you raise your head up from tactical optimization to deal with bigger, more strategic issues before they become real problems.

Software Architecture in DevOps

A new book by Len Bass, Ingo Weber and Liming Zhu “DevOps: A Software Architect’s Perspective”, part of the SEI Series in Software Engineering, looks at how DevOps affects architectural decisions, and a software architect’s role in DevOps.

The authors focus on the goals of DevOps: to get working software into production as quickly as possible while minimizing risk, balancing time-to-market against quality.

“DevOps is a set of practices intended to reduce the time between committing a change to a system and the change being placed into normal production, while insuring high quality”

These fundamental practices are:

1. Engaging operations as a customer and partner, “a first-class stakeholder”, in development. Understanding and satisfying requirements for deployment, logging, monitoring and security in development of an application.
2. Engaging developers in incident handling. Developers taking responsibility for their code, making sure that it is working correctly, helping (often taking the role of first responders) to investigate and resolve production problems.
   This includes the role of a “reliability engineer” on every development team, someone who is responsible for coordinating downstream changes with operations and for ensuring that changes are deployed successfully.
3. Ensuring that all changes to code and configuration are done using automated, traceable and repeatable mechanisms – a deployment pipeline.
4. Continuous Deployment of changes from check-in to production, to maximize the velocity of delivery, using these pipelines.
5. Infrastructure as Code. Operations provisioning and configuration through software, following the same kinds of quality control practices (versioning, reviews, testing) as application software.

Culture and collaboration between developers and operations, shared values and organizational issues, the softer people-side of DevOps, are considered only insofar as they are factors that could affect time-to-market delivery velocity or quality.

Cloud Architecture and Microservices

As a reference for architects, the book focuses on architectural considerations for DevOps. It walks through how Cloud-based systems work, virtualization concepts and especially microservices.

While DevOps does not necessarily require making major architectural changes, the authors argue that most organizations adopting DevOps will find that a microservices-based approach, as pioneered at organizations like Netflix and Amazon, by minimizing dependencies between different parts of the system and between different teams, will also minimize the time required to get changes into production – the first goal of DevOps.

Conway’s Law also comes into play here. DevOps work is usually done by small agile cross-functional teams solving end-to-end problems independently, which means that they will naturally end up building small, independent services:

“Having an architecture composed of small services is a response to having small teams.”

But there are downsides and costs to a microservice-based approach.

As Martin Fowler and James Lewis point out, microservices introduce many more points of failure. Which means that resilience has to be designed and built into each service. Services cannot trust their clients or the other services that they call out to. You need to add defensive checking on data and anticipate failures of other services, implement time-outs and retries, and fall back alternatives or safe default behaviors if another service is unavailable. You also need to design your service to minimize the impact of failure on other services, and to make it easier and faster to recover/restart.

Microservices also increase the cost and complexity of end-to-end system testing. Run-time performance and latency degrade due to the overhead of remote calls. And monitoring and troubleshooting in production can be much more complicated, since a single action often involves many microservices working together (an example at LinkedIn, where a single user request may chain to as many as 70 services).

DevOps in Architecture: Monitoring

In DevOps, monitoring becomes a much more important factor in architecture and design, in order to meet operations requirements.

The chapter on monitoring explains what you need to monitor and why, DevOps metrics, challenges in monitoring systems under continuous change, monitoring microservices and monitoring in the Cloud, and common log management and monitoring tools for online systems.

Monitoring also becomes an important part of live testing in DevOps (Monitoring as Testing), and plays a key role in Continuous Deployment. The authors look at common kinds of live testing, including canaries, A/B testing, and Netflix’s famous Simian Army in terms of passive checking (Security Monkey, Compliance Monkey) and active live testing (Chaos Monkey and Latency Monkey).

DevOps in Architecture: Security

Security is another important cross-cutting concern in software architecture addressed in this book. It looks at security fundamentals including how to identify threats (using Microsoft’s STRIDE model) and the resources that need to be protected, CIA, identity management, access controls. It provides an overview of the security controls in NIST 800-53, and common security issues with VMs and in Cloud architectures (specifically AWS).

In DevOps, security needs to be wired into Continuous Deployment:

1. Enforcing that all changes to code and configuration are done through the Continuous Deployment pipeline
2. Security testing should be included in different stages of the Continuous Deployment pipeline
3. Securing the pipeline itself, including the logs and the artifacts

and security checks need to be part of monitoring (such as Netflix’s Compliance Monkey and Security Monkey).

Continuous Deployment Pipeline and Gatekeepers

Developers – and architects – have to take responsibility for building their automated testing and deployment pipelines. The book explains how Continuous Deployment leverages Continuous Integration, and common approaches to code management and test automation. And it emphasizes the role of gatekeepers along the pipeline – manual decisions or automated checks at different points to determine if it is ok to go forward, from development to testing to staging to live production testing and then to production.

DevOps and Modern Software Architecture

“DevOps: A Software Architect’s Perspective” does a good job of explaining common DevOps practices, especially Continuous Deployment, in a development, instead of operations, context. It also looks at contemporary issues in software architecture, including virtualization and microservices.

It is less academic than Bass’s other book “Software Architecture in Practice”, and emphasizes the importance of real-world operations concerns like reliability, security and transparency (monitoring and live checks and testing) in architecture and deployment.

This is a book written mostly for enterprise software architects and managers who want to understand more about DevOps and Continuous Deployment and Cloud services.

If you’re already deep into DevOps and working with microservices in the Cloud, you probably won’t find much new here.

But if you are looking at how to apply DevOps at scale, or how to migrate legacy enterprise systems to microservices and the Cloud, or if you are a developer who wants to understand operations and what DevOps will mean to you and your job, this is worth reading.