Expert-in-the-Loop Systems with Cross-Domain and In-Domain Few-Shot Learning for Software Vulnerability Detection

• URL: http://arxiv.org/abs/2506.10104v1
• Date: Wed, 11 Jun 2025 18:43:51 GMT
• Title: Expert-in-the-Loop Systems with Cross-Domain and In-Domain Few-Shot Learning for Software Vulnerability Detection
• Authors: David Farr, Kevin Talty, Alexandra Farr, John Stockdale, Iain Cruickshank, Jevin West,
• Abstract summary: This study explores the use of Large Language Models (LLMs) in software vulnerability assessment by simulating the identification of Python code with known Common Weaknessions (CWEs)<n>Our results indicate that while zero-shot prompting performs poorly, few-shot prompting significantly enhances classification performance.<n> challenges such as model reliability, interpretability, and adversarial robustness remain critical areas for future research.
• Score: 38.083049237330826
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As cyber threats become more sophisticated, rapid and accurate vulnerability detection is essential for maintaining secure systems. This study explores the use of Large Language Models (LLMs) in software vulnerability assessment by simulating the identification of Python code with known Common Weakness Enumerations (CWEs), comparing zero-shot, few-shot cross-domain, and few-shot in-domain prompting strategies. Our results indicate that while zero-shot prompting performs poorly, few-shot prompting significantly enhances classification performance, particularly when integrated with confidence-based routing strategies that improve efficiency by directing human experts to cases where model uncertainty is high, optimizing the balance between automation and expert oversight. We find that LLMs can effectively generalize across vulnerability categories with minimal examples, suggesting their potential as scalable, adaptable cybersecurity tools in simulated environments. However, challenges such as model reliability, interpretability, and adversarial robustness remain critical areas for future research. By integrating AI-driven approaches with expert-in-the-loop (EITL) decision-making, this work highlights a pathway toward more efficient and responsive cybersecurity workflows. Our findings provide a foundation for deploying AI-assisted vulnerability detection systems in both real and simulated environments that enhance operational resilience while reducing the burden on human analysts.

Related papers

• White-Basilisk: A Hybrid Model for Code Vulnerability Detection [50.49233187721795]
  We introduce White-Basilisk, a novel approach to vulnerability detection that demonstrates superior performance.<n>White-Basilisk achieves results in vulnerability detection tasks with a parameter count of only 200M.<n>This research establishes new benchmarks in code security and provides empirical evidence that compact, efficiently designed models can outperform larger counterparts in specialized tasks.
  arXiv  Detail & Related papers  (2025-07-11T12:39:25Z)
• LLMpatronous: Harnessing the Power of LLMs For Vulnerability Detection [0.0]
  Large Language Models (LLMs) for vulnerability detection presents unique challenges.<n>Previous attempts employing machine learning models for vulnerability detection have proven ineffective.<n>We propose a robust AI-driven approach focused on mitigating these limitations.
  arXiv  Detail & Related papers  (2025-04-25T15:30:40Z)
• Beyond the Surface: An NLP-based Methodology to Automatically Estimate CVE Relevance for CAPEC Attack Patterns [42.63501759921809]
  We propose a methodology leveraging Natural Language Processing (NLP) to associate Common Vulnerabilities and Exposure (CAPEC) vulnerabilities with Common Attack Patternion and Classification (CAPEC) attack patterns.<n> Experimental evaluations demonstrate superior performance compared to state-of-the-art models.
  arXiv  Detail & Related papers  (2025-01-13T08:39:52Z)
• Bringing Order Amidst Chaos: On the Role of Artificial Intelligence in Secure Software Engineering [0.0]
  The ever-evolving technological landscape offers both opportunities and threats, creating a dynamic space where chaos and order compete.<n>Secure software engineering (SSE) must continuously address vulnerabilities that endanger software systems.<n>This thesis seeks to bring order to the chaos in SSE by addressing domain-specific differences that impact AI accuracy.
  arXiv  Detail & Related papers  (2025-01-09T11:38:58Z)
• In-Context Experience Replay Facilitates Safety Red-Teaming of Text-to-Image Diffusion Models [104.94706600050557]
  Text-to-image (T2I) models have shown remarkable progress, but their potential to generate harmful content remains a critical concern in the ML community.<n>We propose ICER, a novel red-teaming framework that generates interpretable and semantic meaningful problematic prompts.<n>Our work provides crucial insights for developing more robust safety mechanisms in T2I systems.
  arXiv  Detail & Related papers  (2024-11-25T04:17:24Z)
• BreachSeek: A Multi-Agent Automated Penetration Tester [0.0]
  BreachSeek is an AI-driven multi-agent software platform that identifies and exploits vulnerabilities without human intervention. In preliminary evaluations, BreachSeek successfully exploited vulnerabilities in exploitable machines within local networks. Future developments aim to expand its capabilities, positioning it as an indispensable tool for cybersecurity professionals.
  arXiv  Detail & Related papers  (2024-08-31T19:15:38Z)
• EARBench: Towards Evaluating Physical Risk Awareness for Task Planning of Foundation Model-based Embodied AI Agents [53.717918131568936]
  Embodied artificial intelligence (EAI) integrates advanced AI models into physical entities for real-world interaction.<n>Foundation models as the "brain" of EAI agents for high-level task planning have shown promising results.<n>However, the deployment of these agents in physical environments presents significant safety challenges.<n>This study introduces EARBench, a novel framework for automated physical risk assessment in EAI scenarios.
  arXiv  Detail & Related papers  (2024-08-08T13:19:37Z)
• PenHeal: A Two-Stage LLM Framework for Automated Pentesting and Optimal Remediation [18.432274815853116]
  PenHeal is a two-stage LLM-based framework designed to autonomously identify and security vulnerabilities. This paper introduces PenHeal, a two-stage LLM-based framework designed to autonomously identify and security vulnerabilities.
  arXiv  Detail & Related papers  (2024-07-25T05:42:14Z)
• Improving robustness of jet tagging algorithms with adversarial training [56.79800815519762]
  We investigate the vulnerability of flavor tagging algorithms via application of adversarial attacks. We present an adversarial training strategy that mitigates the impact of such simulated attacks.
  arXiv  Detail & Related papers  (2022-03-25T19:57:19Z)
• Increasing the Confidence of Deep Neural Networks by Coverage Analysis [71.57324258813674]
  This paper presents a lightweight monitoring architecture based on coverage paradigms to enhance the model against different unsafe inputs. Experimental results show that the proposed approach is effective in detecting both powerful adversarial examples and out-of-distribution inputs.
  arXiv  Detail & Related papers  (2021-01-28T16:38:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.