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ALPS: Automated Least-Privilege Enforcement for Securing Serverless Functions

Changhee Shin, Bom Kim, Seungsoo Lee

Abstract

Serverless computing is increasingly adopted for AI-driven workloads due to its automatic scaling and pay-as-you-go model. However, its function-based architecture creates significant security risks, including excessive privilege allocation and poor permission management. In this paper, we present ALPS, an automated framework for enforcing least privilege in serverless environments. Our system employs serverless-tailored static analysis to extract precise permission requirements from function code and a fine-tuned Large Language Model (LLM) to generate language- and vendor-specific security policies. It also performs real-time monitoring to block unauthorized access and adapt to policy or code changes, supporting heterogeneous cloud providers and programming languages. In an evaluation of 8,322 real-world functions across AWS, Google Cloud, and Azure, ALPS achieved 94.8\% coverage for least-privilege extraction, improved security logic generation quality by 220\% (BLEU), 124\% (ChrF++) and 100\% (ROUGE-2), and added minimum performance overhead. These results demonstrate that ALPS provides an effective, practical, and vendor-agnostic solution for securing serverless workloads.

ALPS: Automated Least-Privilege Enforcement for Securing Serverless Functions

Abstract

Serverless computing is increasingly adopted for AI-driven workloads due to its automatic scaling and pay-as-you-go model. However, its function-based architecture creates significant security risks, including excessive privilege allocation and poor permission management. In this paper, we present ALPS, an automated framework for enforcing least privilege in serverless environments. Our system employs serverless-tailored static analysis to extract precise permission requirements from function code and a fine-tuned Large Language Model (LLM) to generate language- and vendor-specific security policies. It also performs real-time monitoring to block unauthorized access and adapt to policy or code changes, supporting heterogeneous cloud providers and programming languages. In an evaluation of 8,322 real-world functions across AWS, Google Cloud, and Azure, ALPS achieved 94.8\% coverage for least-privilege extraction, improved security logic generation quality by 220\% (BLEU), 124\% (ChrF++) and 100\% (ROUGE-2), and added minimum performance overhead. These results demonstrate that ALPS provides an effective, practical, and vendor-agnostic solution for securing serverless workloads.

Paper Structure

This paper contains 18 sections, 8 figures, 3 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Background and Problem Statements
  3. Background
  4. Problem Statements
  5. ALPS Design
  6. Design Considerations
  7. System Architecture and Workflow
  8. ALPS System Details
  9. Automated Least-Privilege Extraction via Serverless-Tailored Static Analysis
  10. Real-Time Monitoring and Verification of Permission Abuse
  11. LLM-Based Adaptive Code Integration
  12. Evaluation
  13. Implementation
  14. Evaluation Environments
  15. Functional Correctness
  16. ...and 3 more sections

Figures (8)

  • Figure 1: The example of serverless computing workflow and general IAM-based access control.
  • Figure 2: The motivating example of unauthorized permission abuse through function invocation bypass (A2 $\rightarrow$ A2') caused by the excessive permission (A2).
  • Figure 3: Overall architecture and its workflow of ALPS with six key components: (i) function analyzer, (ii) permission handler, (iii) policy generator, (iv) adaptive integrator, (v) function handler and (v) runtime verifier. Additionally, our system includes two operational phases: permission extraction and runtime verification.
  • Figure 4: The example of automatic extraction procedure of least-privileged policies and environment variables.
  • Figure 5: Instruction guideline for LLM fine-tuning and the example for automatic function restructuring by the code reconstructor.
  • ...and 3 more figures