Vulnhuntr

Local LLMs in AppSec: What Open Tools and Models Can Handle in Application Security

The article examines how local large language models can be integrated into application security workflows without relying on cloud subscriptions. It traces the evolution from llama.cpp in 2023 through agentic frameworks like PentAGI and Strix in 2025-2026. Teams can now run models on local hardware, connect them to code analyzers such as Semgrep, and delegate specific tasks like vulnerability path reconstruction or multi-step application testing. The piece highlights practical scenarios including static code review, dynamic testing with browser and terminal agents, and security evaluation of embedded AI assistants using tools like Garak and PyRIT. It stresses the importance of task decomposition, context management, and verification of model outputs to avoid misleading results. Hardware performance, model selection, and custom agent configurations are presented as key factors for successful adoption.