Core Concepts
A hybrid LLM workflow assists in identifying user privilege variables efficiently.
Abstract
The content discusses the importance of identifying user privilege-related variables in programs for security purposes. It introduces a novel Large Language Model (LLM) workflow to aid analysts in this identification process. The workflow involves generating Program Dependence Graphs (PDGs), extracting Variable Subgraphs, rating Code Statements using an LLM, and computing UPR Scores for variables. The approach aims to reduce manual efforts and false positives while improving the identification of UPR variables.
Structure:
- Introduction to User Privilege Variables in Programs
- Challenges in Identifying UPR Variables
- Proposed Hybrid LLM Workflow Overview
- Data Extraction and Analysis Process
- Evaluation of Practicality and Reliability through Experiments
Key Highlights:
- Importance of protecting organizations against privilege leakage attacks.
- Logic vulnerabilities are more challenging to detect than memory vulnerabilities.
- Existing methods rely on heuristic rules, limiting scalability.
- Introduction of a novel LLM workflow to identify UPR variables efficiently.
- Use of PDGs, Variable Subgraphs, and LLM ratings to compute UPR scores.
Stats
Many analysts choose to find user privilege related (UPR) variables first as start points.
Our experiments show that using a typical UPR score threshold (i.e., UPR score >0.8), the false positive rate (FPR) is only 13.49%.
Our method detected 645 variables as positive, with a false positive rate of 13.49%.
Out of 413 positive variables reported by both methods, more than half were false positives.
Quotes
"Since user privileges are related to the logical level of program understanding, it is necessary to analyze the application logic before accurately discovering UPR variables." - Content
"Our contributions include proposing a novel LLM workflow that can help human analysts identify UPR variables in programs of any size." - Content