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Practically implementing an LLM-supported collaborative vulnerability remediation process: a team-based approach

Xiaoqing Wang, Yuanjing Tian, Keman Huang, Bin Liang

TL;DR

A three-step mix-method study to incorporate LLM into the vulnerability remediation process effectively and suggests that when incorporating LLM into practical processes, facilitating the collaborations among all associated stakeholders, reshaping LLM' roles according to task complexity, as well as approaching the short-term side effects of improved user engagement facilitated by LLMs with a rational mindset are suggested.

Abstract

Incorporating LLM into cybersecurity operations, a typical real-world high-stakes task, is critical but non-trivial in practice. Using cybersecurity as the study context, we conduct a three-step mix-method study to incorporate LLM into the vulnerability remediation process effectively. Specifically, we deconstruct the deficiencies in user satisfaction within the existing process (Study 1). This inspires us to design, implement, and empirically validate an LLM-supported collaborative vulnerability remediation process through a field study (Study 2). Given LLM's diverse contributions, we further investigate LLM's double-edge roles through the analysis of remediation reports and follow-up interviews (Study 3). In essence, our contribution lies in promoting an efficient LLM-supported collaborative vulnerability remediation process. These first-hand, real-world pieces of evidence suggest that when incorporating LLMs into practical processes, facilitating the collaborations among all associated stakeholders, reshaping LLMs' roles according to task complexity, as well as approaching the short-term side effects of improved user engagement facilitated by LLMs with a rational mindset.

Practically implementing an LLM-supported collaborative vulnerability remediation process: a team-based approach

TL;DR

A three-step mix-method study to incorporate LLM into the vulnerability remediation process effectively and suggests that when incorporating LLM into practical processes, facilitating the collaborations among all associated stakeholders, reshaping LLM' roles according to task complexity, as well as approaching the short-term side effects of improved user engagement facilitated by LLMs with a rational mindset are suggested.

Abstract

Incorporating LLM into cybersecurity operations, a typical real-world high-stakes task, is critical but non-trivial in practice. Using cybersecurity as the study context, we conduct a three-step mix-method study to incorporate LLM into the vulnerability remediation process effectively. Specifically, we deconstruct the deficiencies in user satisfaction within the existing process (Study 1). This inspires us to design, implement, and empirically validate an LLM-supported collaborative vulnerability remediation process through a field study (Study 2). Given LLM's diverse contributions, we further investigate LLM's double-edge roles through the analysis of remediation reports and follow-up interviews (Study 3). In essence, our contribution lies in promoting an efficient LLM-supported collaborative vulnerability remediation process. These first-hand, real-world pieces of evidence suggest that when incorporating LLMs into practical processes, facilitating the collaborations among all associated stakeholders, reshaping LLMs' roles according to task complexity, as well as approaching the short-term side effects of improved user engagement facilitated by LLMs with a rational mindset.
Paper Structure (41 sections, 3 equations, 7 figures, 3 tables)

This paper contains 41 sections, 3 equations, 7 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Literature review
  3. AI for supporting cybersecurity operations
  4. Large Language Models (LLMs) for real-world high-stakes tasks
  5. Study1: limitation of the existing vulnerability remediation process
  6. Research design
  7. Existing vulnerability remediation process
  8. Theoretical background: D&M model
  9. Measurement of user satisfaction
  10. Participant demographics
  11. Results: insufficient user satisfaction within existing vulnerability remediation process
  12. Information quality
  13. Service quality
  14. Collaboration quality
  15. Study2: LLM-supported collaborative vulnerability remediation process
  16. ...and 26 more sections

Figures (7)

  • Figure 1: Existing vulnerability remediation process with four stages, including detecting the vulnerabilities, generating the remediation plan, disposing of the vulnerabilities, and reporting the remediation results.
  • Figure 2: Users' satisfaction with information quality, service quality, and collaboration quality in Study1. *p<0.1,**p<0.05,***p<0.01.
  • Figure 3: Experimental conditions and process designs for the three different vulnerability remediation processes. The red dashed lines and black dotted lines within User Engagement Enhancement and LLM-supported Technicians Enhancement conditions represent the logical workflow for the two designed components, respectively. The right part is an example of the remediation report that security technicians must submit after patching a vulnerability.
  • Figure 4: User engagement level changes between any two conditions and within different vulnerability groups. $*p<0.1,**p<0.05,***p<0.01.$
  • Figure 5: Remediation duration changes between any two conditions. "Overall" represents the whole 29 vulnerabilities, while others represent the two groups according to the vulnerability remediation complexity. *p<0.1,**p<0.05,***p<0.01.
  • ...and 2 more figures