ASG-LLM: An LLM-based pipeline for automated scraping code generation in security-sensitive environments

Automating interactions with security-sensitive web services remains difficult due to dynamic frontends, session-bound tokens, and undocumented parameter-generation logic. Traditionally, developers must manually analyze captured traffic, hypothesize parameter-generation routines, and iteratively test internally defined functions until the interaction succeeds. We propose ASG-LLM, an execution grounded pipeline that generates scraping modules from a single successful browser session trace. ASG-LLM captures full HTTP(S) traffic, removes irrelevant requests through multi-stage filtering, and constructs a dependency-aware representation of the core interaction flow. When security-critical parameters cannot be reconstructed from the trace alone, the FUNC Retriever identifies a small set of reusable internal function descriptions using HyDE-guided selective retrieval, enabling the LLM to select compatible parameter-generation routines based on behavioral similarity rather than keyword matching, without exposing proprietary implementation code. Generated modules are executed in a controlled environment and iteratively repaired using structured failure feedback. ASG-LLM was evaluated on representative banking and public-sector portals. In our case studies, ASG-LLM reduces developer effort by cutting traffic analysis and basic code implementation from hours to minutes and reducing security-handling effort from a full-day task to a couple of hours. At the model level, HyDE-based selective retrieval improves internal function recommendation accuracy from 30% to 70% Top-1 and from 60% to 90% Top-3 compared with a full-context baseline. These results suggest that combining trace grounding, selective retrieval, and execution-time validation is a promising direction for robust web automation in security-constrained environments.