November 2025 Summaries

Hacking into APIs effectively requires understanding the context beyond just what is documented, as attackers exploit gaps between perceived and actual API surfaces. Many organizations rely on OpenAPI specifications, which often miss internal, partial, or obsolete endpoints, creating false confidence and leaving blind spots. Pynt addresses these issues by merging traffic and infrastructure data with specs to create a comprehensive API catalog, enabling the discovery of shadow and obsolete endpoints. It uses semantic inference to understand parameter roles and executes stateful scenario tests that mimic real attacks, identifying vulnerabilities such as broken authentication and business logic flaws. By integrating discovery and testing within the software development lifecycle, Pynt provides a single source of truth that reduces exposure gaps and ensures accurate testing against the actual deployment environment. This approach emphasizes the importance of testing APIs based on their usage in production rather than solely relying on documentation.

As technology advances, the evolution from Dynamic Application Security Testing (DAST) to API security, and now to Large Language Model (LLM) security testing, reflects the increasing complexity of digital interfaces. The GenAI Application Security Report by Pynt highlights the rapid integration of AI into organizational systems, with 98% of respondents already adopting AI, making LLMs a fundamental aspect rather than a competitive advantage. This shift necessitates a new category in security visibility: LLM Discovery, which involves mapping the use of models, data interactions, and access controls. LLM security testing focuses on understanding contextual behavior, with vulnerabilities arising from reasoning flaws rather than traditional code injections. The report underscores the importance of securing the entire interaction chain from input to output, advocating for a context-aware approach that unifies API and model testing. As organizations prioritize API security, understanding and securing LLMs becomes crucial, as they encapsulate but do not replace existing risks, emphasizing the need for adaptive and comprehensive security practices.