Why Traditional Tools Miss Authorization Bugs (And How Wakefield Finds Them)

Authorization vulnerabilities remain the most frequent and damaging flaw class in modern web applications. Ranked #1 in the OWASP Top 10, Broken Access Control (BAC) encompasses any failure to enforce proper permissions on sensitive actions or data. In practice, this often means a user can tamper with another user's data by modifying a predictable identifier (e.g., changing /comment/123 to /comment/124) without any backend validation.

Recent industry reports validate the scope of this issue. OWASP observed BAC in 94% of real-world assessments, while Verizon's 2025 DBIR and IBM's Cost of a Data Breach highlight that authorization logic errors disproportionately lead to multi-million dollar breaches, often requiring regulatory disclosure.

Conventional DAST tools are not equipped to reliably detect these flaws. Their model (link crawling and parameter fuzzing) was designed for legacy web architectures. They rely on a single authenticated session, preventing them from exercising privilege boundaries across roles or identities. These tools cannot simulate real-world adversaries who probe role misconfigurations, bypass access control lists, or test object-level authorization checks across contexts.

More critically, authorization vulnerabilities are semantic in nature. Determining whether access to a resource is inappropriate often requires knowledge of the intended business logic. For instance, distinguishing between a public and private resource on a social platform requires understanding app state, user relationships, and access policies.

Wakefield is built to operate in this gap. Our system leverages a proprietary knowledge graph representation of the application under test, allowing it to store and reason over authentication paths, privilege structures, and business workflows. Instead of relying on static signatures or rule-based test cases, Wakefield uses LLMs to extract contextual meaning from application behavior and documentation, and reinforcement learning to prioritize likely-vulnerable states and paths.

In a recent evaluation on OWASP Juice Shop, a deliberately insecure application, Wakefield identified an IDOR vulnerability in the comment-editing endpoint. The agent discovered that a user could modify comments belonging to other users without privilege, and critically, that the same action could be performed by unauthenticated users. No payload crafting or manual tuning was required: Wakefield autonomously mapped the surface, executed role-switched probes, and generated a detailed evidence-backed report demonstrating exploitability.

Wakefield is not just an improvement over traditional tools. It's a fundamental shift in how security testing is done. We've built the first system that can autonomously detect access control failures with the precision and depth of a skilled human tester, but at the speed and scale required for modern infrastructure. Traditional DAST tools miss these flaws by design. If you're relying on them to catch logic bugs, you're already behind. Wakefield is setting the standard for how real security gets done.