AI-assisted development tools are changing how software is built. From code generation and automated testing to rapid prototyping and full-stack application scaffolding, Large Language Models (LLMs) are increasingly being used to accelerate software delivery across startups, SaaS providers, and engineering teams.

In many cases, these tools are delivering genuine operational value. Development cycles are shortening, smaller teams are able to deliver broader functionality, and technical barriers to entry are lower than they were only a few years ago.

However, this shift is also creating a distinct set of security challenges. As more organisations rely heavily on AI-generated code, security testing is increasingly identifying recurring weaknesses in how applications are designed and implemented. These issues are rarely caused by the AI tooling alone. More often, they emerge when generated code is deployed into production environments without sufficient architectural oversight, security review, or contextual understanding of how the wider application behaves.

From a penetration testing perspective, several patterns are now appearing consistently across vulnerabilities in AI-developed applications. In this article, we discuss our observations based on recent testing projects of web applications underpinned by AI-driven development.

Access control weaknesses

One of the most common issues identified in AI-developed applications relates to access control. In many cases, we have observed authentication mechanisms are implemented correctly at a surface level, while deeper authorisation logic is inconsistent or incomplete.

This often manifests as users being able to access data or functionality beyond their intended permissions. Administrative features may be exposed to lower-privileged users, APIs may trust client-side controls too heavily, or multi-tenant boundaries may not be enforced consistently across the application.

The underlying issue is frequently architectural rather than syntactical. AI coding tools can generate functional components quickly, but they do not inherently maintain a deep understanding of how permissions should operate consistently across a large and evolving application. As additional features are introduced over time, access control logic can become fragmented, particularly where multiple generated components interact with shared data or workflows. Architectural decisions often fail to appropriately consider authorisation middleware as a fundamental principle of application design.

For SaaS platforms and customer-facing applications, these weaknesses can create significant exposure. Broken access control remains one of the highest impact vulnerability categories because it often enables direct access to sensitive customer data or operational functionality.

Business logic and workflow flaws

Another recurring issue is the presence of logic flaws within application workflows. These vulnerabilities are particularly difficult to detect through automated tooling because the application may behave exactly as coded, while still allowing unintended outcomes. Identifying these types of vulnerabilities involves lateral thinking and contextual awareness, often overlooked in AI-led design and automated testing.

In AI-developed applications, multi-step processes are a common area of weakness. Testing frequently identifies scenarios where steps within authentication or approval workflows can be bypassed entirely. In some cases, applications expose later-stage functionality without adequately validating whether earlier security checks have been completed.

Examples may include bypassing email verification stages, circumventing multi-factor authentication workflows, or accessing privileged functionality through direct requests to backend endpoints.

These types of vulnerabilities often arise because AI-generated code tends to focus on implementing individual features successfully rather than maintaining coherent security assumptions across complex user journeys. Without sufficient human oversight, inconsistencies between frontend behaviour and backend validation can emerge over time.

Legacy vulnerabilities in modern applications

Despite advances in AI-assisted development, traditional application vulnerabilities remain prevalent. Penetration testing continues to identify issues such as SQL injection, cross-site scripting, and excessive information disclosure within applications dependent upon AI-assisted design and coding workflows.

In many cases, these vulnerabilities do not appear because the generated code is inherently insecure. Instead, they emerge through incomplete validation, insecure integration patterns, or assumptions made during rapid development cycles.

These weaknesses highlight an important point. AI-generated code is not automatically secure simply because it is modern or generated quickly. Established application security risks still apply, particularly where development speed outpaces security review.

Why human context still matters

One of the recurring themes in assessing AI-developed applications is the importance of contextual understanding and decision making. Effective application security relies not only on whether individual components function correctly, but on whether the overall system behaves securely as a whole with awareness of each component and how it interacts.

Human developers and security reviewers provide context that current AI tooling does not fully appear to replicate in our tests. This includes understanding how permissions should propagate across services, how steps interact in complex workflows, and how security assumptions should still be challenged and testing performed rigorously.

AI-assisted development can accelerate implementation significantly, but security still depends heavily on architectural consistency, review processes, and a clear understanding of risk across the broader application environment. This is particularly important for organisations developing customer-facing applications handling sensitive data, where security weaknesses can lead to material exposure.

The role of security testing in AI-developed applications

As AI-assisted application development becomes more common, structured web penetration testing is becoming increasingly important for validating how these applications behave in practice.

Automated scanning alone is currently insufficient for identifying the observed types of common vulnerabilities in AI-developed applications. Access control weaknesses and business logic flaws typically require manual assessment and contextual analysis to identify effectively, and effective coverage of typical application vulnerabilities is inconsistent.

Security testing therefore plays an important role not only in identifying technical vulnerabilities, but also in assessing whether the application’s underlying trust model operates consistently across the wider environment.

For organisations adopting AI-assisted development practices, the objective should not be to avoid AI tooling altogether. Rather, it should be to ensure that accelerated development is balanced with sufficient architectural oversight, security review, and structured testing.

How can Sentrium help?

AI-assisted development is reshaping how modern applications are built, particularly within fast-moving SaaS and technology environments. While these tools can significantly improve development speed and productivity, they can also introduce recurring security weaknesses when used without sufficient oversight.

Access control inconsistencies, business logic flaws, excessive information disclosure, and traditional web vulnerabilities are all appearing regularly within AI-developed applications. In many cases, these issues stem less from the generated code itself and more from the absence of consistent architectural and security review across the wider platform.

As organisations continue adopting AI-assisted development workflows, maintaining strong application security practices will remain essential. Penetration testing provides an important mechanism for validating not just whether individual components function correctly, but whether the application behaves securely as a complete system.