The Insider's Advantage: Exploiting Automated Privacy Policy Analyzer Tools Through Subtle Text Manipulations

Privacy policies are essential for communicating data practices to users, despite their dense, complex language, which often obstructs comprehension. Automated Policy Analyzer Tools (APATs) and recent advancements in Large Language Models (LLMs) have made strides in simplifying these documents through features such as question-answering and summarization. However, the robustness of these tools against adversarial manipulations remains less explored. This study examines how subtle, organization-side modifications to policy text can mislead APATs. We present APATRA, a multi-level framework for assessing the robustness of APATs against a range of policy-specific attacks designed to manipulate them into generating misleading outputs. We uncover vulnerabilities that can lead to substantial errors in outputs by applying character-, word-, phrase-, and sentence-level perturbations to policy contexts while preserving the original information. Our evaluation spans policy-specialized models (PolicyQA, PrivBERT), state-of-the-art LLMs (LLama-4-Maverick, GPT-3.5, GPT-4, GPT-5-mini, Claude-3.5, Claude-4.6-Sonnet), and third-party policy analyzer tools (AesirX). To validate the subtlety of our attacks, we conducted an IRB-approved user study with 100 general participants and 10 legal experts specializing in privacy law, who assessed adversarial excerpts for grammaticality, contradictions, and logical flow. Their evaluations demonstrate that our modifications maintain readability and raise no concerns. Overall, our results reveal critical security gaps in APATs and underscore the urgent need to enhance their robustness.