The OWASP Top 10 has long served as a reality check for development teams: a concise, community-driven snapshot of the most critical web application security risks organizations face today.
With the release of the latest OWASP Top 10 in late 2025 (the first update since 2021), that snapshot now reflects a rapidly evolving software landscape increasingly shaped by open source dependencies, cloud-native architectures, and now, AI-assisted development.
This OWASP Top 10 focuses on web application security, separate from the OWASP project that covers risks in LLM applications. But many security themes overlap with modern software supply chains and AI risks.
For organizations building software at scale, especially with AI tools, the 2025 update highlights how traditional application security and next-generation risks are now closely connected.
The OWASP Top 10 is not a checklist or compliance standard. It's a project of rankings and remediation guidance for security risks, curated by security practitioners based on real-world data, incident analysis, and expert consensus.
The 2025 edition reinforces several long-standing truths:
Many critical vulnerabilities stem from design and architectural decisions, not just coding mistakes.
The increasing dependence on third-party components dramatically expands the attack surface.
Automation and speed — while essential to modern DevOps — can amplify risk when governance and visibility are lacking.
The list aims to help organizations prioritize security efforts where they matter most.
Rather than focusing on individual list items, it's useful to look at the broader patterns emerging from the 2025 update.
Issues related to insecure design, weak threat modeling, and misconfiguration continue to rank highly. This reflects a reality many teams face: security controls bolted on late in the SDLC are rarely effective.
Modern applications are assembled, not built from scratch, relying on:
Microservices and APIs
Cloud services and infrastructure
Third-party and open source libraries
Each layer introduces configuration complexity that attackers are adept at exploiting.
Although the OWASP Top 10 focuses on web applications, it increasingly acknowledges that vulnerabilities often originate outside first-party code.
Open source components, frameworks, and build-time tools can introduce critical weaknesses long before an application is deployed.
This shift mirrors the broader industry reality: software supply chains are now a primary attack vector.
CI/CD pipelines, infrastructure as code, and automated deployments accelerate innovation. But they also accelerate failure when insecure components or configurations slip through.
Once a flawed artifact enters the pipeline, it can propagate rapidly across environments.
The OWASP Top 10 underscores the need for automated, policy-driven security controls that operate at the same speed as modern development.
Although the OWASP Top 10 is not an AI-specific list, many of its risks become more pronounced in AI-enabled software development.
AI coding assistants can increase productivity, but they can also reproduce insecure patterns, deprecated APIs, or vulnerable dependencies at scale. If insecure design is already a top-tier risk, AI can unintentionally amplify it by accelerating poor decisions.
AI-driven applications introduce new dependencies beyond traditional libraries, including:
Models
Training and inference datasets
Plugins and extensions
Model-serving and orchestration infrastructure
These assets often lack the same maturity in vulnerability disclosure, versioning, and governance that open source ecosystems have developed over decades.
From an OWASP perspective, this expands the definition of what "components" mean and what it takes to secure them.
AI systems frequently interact with sensitive data, external APIs, and dynamic inputs. Misconfiguration, insufficient access controls, or weak validation can lead to data leakage, model abuse, or unintended behavior — all of which map back to familiar OWASP risk categories.
In short, AI doesn't replace traditional application security concerns. It intensifies them.
Sonatype's approach to software supply chain security is built around a simple principle: you cannot secure what you cannot see. That philosophy directly supports the risk awareness goals of the OWASP Top 10.
By providing deep, continuous insight into open source dependencies, Sonatype helps teams identify known vulnerabilities, malicious packages, and risky components early, before they become production incidents.
This directly supports OWASP concerns around vulnerable and outdated components, insecure design decisions, and dependency-driven exposure.
Modern development demands automation, but automation without guardrails increases risk. Sonatype enables organizations to define and enforce security, quality, and license policies directly within CI/CD pipelines.
This ensures that speed does not come at the cost of security — aligning with OWASP's emphasis on systemic risk reduction rather than reactive fixes.
As AI models and datasets become first-class software artifacts, the same principles apply: inventory, provenance, policy, and continuous monitoring.
Sonatype's experience in securing software supply chains positions organizations to extend those practices to AI assets as governance standards continue to evolve.
The foundational controls that mitigate Top 10 risks today are the same ones that will help manage AI-related threats tomorrow.
For organizations building modern software, especially those adopting AI-assisted development and machine learning workflows, the OWASP Top 10 conveys the following implications:
Secure design must be intentional and enforced early.
Third-party and open source risk is inevitable, but it can be governed.
Automation must be paired with automated security controls.
AI innovation must be supported by visibility, policy, and provenance.
With Sonatype Guide, organizations gain continuous insight into open source and AI-related components, enabling teams to detect risk earlier, enforce policy consistently, and make faster, more informed decisions across the SDLC.
Sonatype's open source AI capabilities extend these principles to AI-driven development, helping organizations manage unique risks without slowing innovation.
By combining deep software supply chain intelligence with AI-aware governance, Sonatype helps teams address today's OWASP Top 10 risks while preparing for what comes next.