Since I started software development in the early 2000s, the libraries and frameworks we use have constantly changed. While we used to try to write everything ourselves, today we work with hundreds, even thousands, of external dependencies. This situation, while bringing us speed and flexibility, also brings a continuous burden of security vulnerabilities and updates.
In every project, I find myself grappling with this “dependency hell.” On one hand, I’m eager to add new features, while on the other, I’m constantly fighting to patch security vulnerabilities silently accumulating in the background. This post describes the cost of this struggle to me and my teams, and the pragmatic approaches I’ve developed over the years.
Dependency Hell: Every Library a Risk Carrier
Today, with a single npm install or pip install command, I see that the packages we add to our project actually pull in dozens, even hundreds, of other dependencies. These transitive dependencies often go unnoticed but exist in our system as potential risk carriers. Each library, along with its own codebase, creates a new security surface.
In an experiment I conducted, when I added only uvicorn and psycopg2 to a simple FastAPI project, the pip freeze output showed more than 15 packages. This is just the beginning. If I move to Vue or React for a frontend project, the node_modules directory can easily reach gigabytes in size. Ensuring that every line of code in such a vast amount of code is secure is impossible.
In this complexity, I evaluate every library I add as a potential risk. Before adding a new dependency, I look not only at its functionality but also at its maintenance status, community support, and past security records. Sometimes the simplest-looking solution can bring the biggest security vulnerability, so I always have to remain cautious.
The Continuous Update Cycle: Why It’s Inevitable?
I’ve observed for years that there’s no such thing as “stable” in the software world. New features, performance improvements, and, of course, security patches are constantly being released. Security patches, in particular, create an unavoidable pressure for us to update. Malicious actors are constantly searching for vulnerabilities, and when a CVE (Common Vulnerabilities and Exposures) is published, our task is to patch it as quickly as possible.
Last month, I had to act immediately for a vulnerability like CVE-2026-31431, discovered in a Linux kernel and affecting the algif_aead module. For such critical vulnerabilities, which can impact the core of the system, I had to either patch quickly or blacklist the relevant module. This sometimes requires immediate intervention, even in a production environment.
This continuous update cycle, while making our systems more secure, also increases our operational burden. Every update carries some risk. There’s always a possibility of an unexpected regression with a new version. Therefore, instead of blindly updating, it’s necessary to proceed with a strategy. I prioritize critical security patches, while other updates are deployed more controllably, after passing through test environments.
The Technical and Financial Cost of the Update Burden
The burden of dependency updates is not as simple as “run a command and it’s done.” This process has serious costs, both technical and financial. On the technical side, every update creates a potential moment of “fragility.” A new version might introduce breaking changes in the APIs we use, cause performance degradation, or, worst of all, lead to an unexpected bug.
While working on the ERP system of a manufacturing company, a single minor dependency update caused an unexpected error in a serialization library we used in the integration layer. The error was only triggered by specific data types, so it wasn’t noticed in the test environment and appeared 3 days after going into production, during invoicing. Detecting and resolving this error cost our team 2 full days, equating to approximately 1600 USD in labor costs. Such unexpected problems demonstrate how critical dependency management is.
The financial cost covers an even broader spectrum. Developers’ and operations teams’ time is spent updating dependencies, testing, and resolving potential issues instead of developing new features. This directly slows down product development speed and negatively impacts market competitiveness. Furthermore, the costs of data breaches, reputational damage, and legal sanctions that arise if a security vulnerability is exploited can make this update burden seem insignificant by comparison.
This situation is particularly evident in large and complex systems. In a monolithic structure, updating a single dependency can affect the entire system, whereas in microservice architectures, each service needs to manage its own set of dependencies. This means a much larger total number of dependencies and, consequently, a greater update burden. In both cases, we must develop strategic approaches to alleviate this burden.
My Approach: Pragmatic Dependency Management
With twenty years of experience, I’ve seen that extreme approaches to dependency management, such as “pull everything to the latest version” or “never update anything,” are not sustainable. My adopted approach is a pragmatic and risk-oriented management style. First, I don’t treat every dependency equally. I handle critical libraries that form the core of the system, used in the network layer or database interaction, with a much different priority than a style library used in the user interface.
Automated scanning tools are my greatest helpers in this process. In the backend of my side product, automated dependency scanners run weekly. Last month, an automatic Pull Request was opened for a low-priority CVE in a cryptography library. However, when I checked the relevant code path with git blame, I realized that there was no usage scenario where this vulnerability could be exploited, and the code in question was never used. In this case, instead of making an urgent update, I decided to do it at a more critical time along with other updates. This is based on my principle of evaluating real risk rather than blindly reacting to every alert.
Furthermore, I run a separate process for dependency updates in my CI/CD pipelines. While critical security patches have automated tests and rapid deployment mechanisms, for other updates, I use broader regression tests and even canary deployment strategies. This way, I try to minimize the impact of potential update-related issues. As I mentioned in my previous post about integration issues I experienced in a production ERP system, a wrong update can halt all operations. Such experiences push me to be more cautious and planned.
Zero-Trust and the Principle of Minimal Dependencies
The Zero-Trust principle, frequently articulated in modern security architectures, also applies to dependency management. I adopt a “never trust, always verify” approach for every dependency. This is important not only for security vulnerabilities but also for performance and sustainability. When incorporating a library into our project, I look not only at its functionality but also at its footprint and the indirect burden it will bring.
The principle of minimal dependencies is also part of this philosophy. I try not to use any library that is not truly necessary. While I used to quickly add a library to implement a feature, now I first evaluate the basic requirements of that feature, sometimes taking care not to include an entire package in the system for something that can be solved with a simple function. In one client project, I saw the node_modules directory reach 1.2 GB. Even after npm audit fix, we still found hundreds of packages. Removing unnecessary UI libraries accelerated build times by 15% and significantly reduced the potential security surface.
Supply chain security also gains importance in this context. I try to understand the sources of the libraries I use, who their developers are, and how actively they are maintained. Sometimes, thinking about what would happen if a single person behind a package abandoned the project worries me. For such situations, I evaluate alternative solutions or simple implementations that we can develop internally.
Looking Ahead: AI and Dependency Risk Management
The burden of dependency management has long surpassed a level that can be fully handled by human effort. In the future, I believe artificial intelligence will provide significant benefits in this area. Approaches like prompt engineering, RAG (retrieval-augmented generation), and agent patterns can play a key role in automating dependency risk management.
In my own AI application architecture, a new version of a parser library I used for RAG patterns carried the potential for a prompt injection vulnerability. Traditional security scanners didn’t immediately catch this. A simple regex-based static analysis tool I developed detected critical eval() calls and warned me. Perhaps in the future, AI will better understand such ‘semantic’ vulnerabilities—that is, the true intent of the code at runtime and potential misuse scenarios.
Artificial intelligence can revolutionize not only vulnerability detection but also automated patch generation and testing processes. When a CVE is published, AI models can quickly generate potential patches, test them, and even evaluate their compatibility with the existing codebase. This will significantly lighten the burden on developers and enable security patches to be applied much faster. However, this situation could also bring a new paradox: “AI-generated vulnerabilities.” This, too, seems likely to be one of the main topics of our future security discussions.
Conclusion: An Unending Struggle, Continuous Learning
Dependency vulnerabilities and the constant update burden are an unavoidable reality of the software development and operations world. This is an unending struggle, but every new vulnerability and every new patch offers us an opportunity to better understand our systems and make them more secure. Instead of blindly updating, adopting a pragmatic approach that evaluates risks, leverages automation, and embraces the principle of minimal dependencies is the healthiest way to cope with this burden.
For me, this process means continuous learning and adaptation. Every new technology, every new library, brings with it a new responsibility. Being aware of this responsibility and taking proactive steps is critical not only for the software we write but also for ensuring the security of our users and our companies. I will continue to share my experiences and solutions on this topic.