Leveraging LLM to Strengthen ML-Based Cross-Site Scripting Detection

• URL: http://arxiv.org/abs/2504.21045v1
• Date: Mon, 28 Apr 2025 15:22:31 GMT
• Title: Leveraging LLM to Strengthen ML-Based Cross-Site Scripting Detection
• Authors: Dennis Miczek, Divyesh Gabbireddy, Suman Saha,
• Abstract summary: Cross-Site Scripting (XSS) remains among the top 10 security vulnerabilities.<n>We fine-tune a Large Language Model (LLM) to generate complex obfuscated XSS payloads automatically.<n>Our approach achieved a 99.5% accuracy rate with the obfuscated dataset.
• Score: 1.6334609937053302
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: According to the Open Web Application Security Project (OWASP), Cross-Site Scripting (XSS) is a critical security vulnerability. Despite decades of research, XSS remains among the top 10 security vulnerabilities. Researchers have proposed various techniques to protect systems from XSS attacks, with machine learning (ML) being one of the most widely used methods. An ML model is trained on a dataset to identify potential XSS threats, making its effectiveness highly dependent on the size and diversity of the training data. A variation of XSS is obfuscated XSS, where attackers apply obfuscation techniques to alter the code's structure, making it challenging for security systems to detect its malicious intent. Our study's random forest model was trained on traditional (non-obfuscated) XSS data achieved 99.8% accuracy. However, when tested against obfuscated XSS samples, accuracy dropped to 81.9%, underscoring the importance of training ML models with obfuscated data to improve their effectiveness in detecting XSS attacks. A significant challenge is to generate highly complex obfuscated code despite the availability of several public tools. These tools can only produce obfuscation up to certain levels of complexity. In our proposed system, we fine-tune a Large Language Model (LLM) to generate complex obfuscated XSS payloads automatically. By transforming original XSS samples into diverse obfuscated variants, we create challenging training data for ML model evaluation. Our approach achieved a 99.5% accuracy rate with the obfuscated dataset. We also found that the obfuscated samples generated by the LLMs were 28.1% more complex than those created by other tools, significantly improving the model's ability to handle advanced XSS attacks and making it more effective for real-world application security.

Related papers

• Enhancing Leakage Attacks on Searchable Symmetric Encryption Using LLM-Based Synthetic Data Generation [0.0]
  Searchable Symmetric Encryption (SSE) enables efficient search capabilities over encrypted data, allowing users to maintain privacy while utilizing cloud storage. SSE schemes are vulnerable to leakage attacks that exploit access patterns, search frequency, and volume information. We propose a novel approach that leverages large language models (LLMs), specifically GPT-4 variants, to generate synthetic documents that statistically and semantically resemble the real-world dataset of Enron emails.
  arXiv  Detail & Related papers  (2025-04-29T04:23:10Z)
• GenXSS: an AI-Driven Framework for Automated Detection of XSS Attacks in WAFs [0.0]
  Cross-Site Scripting (XSS) attacks target client-side layers of web applications by injecting malicious scripts.<n>Traditional Web Application Firewalls (WAFs) struggle to detect highly obfuscated and complex attacks.<n>This paper presents a novel generative AI framework that leverages Large Language Models (LLMs) to enhance XSS mitigation.
  arXiv  Detail & Related papers  (2025-04-11T00:13:59Z)
• CyberLLMInstruct: A New Dataset for Analysing Safety of Fine-Tuned LLMs Using Cyber Security Data [2.2530496464901106]
  integration of large language models into cyber security applications presents significant opportunities.<n>CyberLLMInstruct is a dataset of 54,928 instruction-response pairs spanning cyber security tasks.<n>Fine-tuning models can achieve up to 92.50 percent accuracy on the CyberMetric benchmark.
  arXiv  Detail & Related papers  (2025-03-12T12:29:27Z)
• XSS Adversarial Attacks Based on Deep Reinforcement Learning: A Replication and Extension Study [7.015365208285838]
  Cross-site scripting (XSS) poses a significant threat to web application security.<n>Deep Learning has shown remarkable success in detecting XSS attacks.<n> adversarial attacks employ mutation-based strategies for different components of XSS attack vectors.
  arXiv  Detail & Related papers  (2025-02-26T12:39:55Z)
• ELBA-Bench: An Efficient Learning Backdoor Attacks Benchmark for Large Language Models [55.93380086403591]
  Generative large language models are vulnerable to backdoor attacks.<n>$textitELBA-Bench$ allows attackers to inject backdoor through parameter efficient fine-tuning.<n>$textitELBA-Bench$ provides over 1300 experiments.
  arXiv  Detail & Related papers  (2025-02-22T12:55:28Z)
• Dancer in the Dark: Synthesizing and Evaluating Polyglots for Blind Cross-Site Scripting [10.696934248458136]
  Cross-Site Scripting (XSS) is a prevalent and well known security problem in web applications.<n>We present the first comprehensive study on blind XSS (BXSS)<n>We develop a method for synthesizing polyglots, small XSS payloads that execute in all common injection contexts.
  arXiv  Detail & Related papers  (2025-02-12T15:02:30Z)
• Evaluating and Improving the Robustness of Security Attack Detectors Generated by LLMs [6.936401700600395]
  Large Language Models (LLMs) are increasingly used in software development to generate functions, such as attack detectors, that implement security requirements.<n>This is most likely due to the LLM lacking knowledge about some existing attacks and to the generated code being not evaluated in real usage scenarios.<n>We propose a novel approach integrating Retrieval Augmented Generation (RAG) and Self-Ranking into the LLM pipeline.
  arXiv  Detail & Related papers  (2024-11-27T10:48:37Z)
• SecAlign: Defending Against Prompt Injection with Preference Optimization [52.48001255555192]
  Adrial prompts can be injected into external data sources to override the system's intended instruction and execute a malicious instruction.<n>We propose a new defense called SecAlign based on the technique of preference optimization.<n>Our method reduces the success rates of various prompt injections to around 0%, even against attacks much more sophisticated than ones seen during training.
  arXiv  Detail & Related papers  (2024-10-07T19:34:35Z)
• How to Craft Backdoors with Unlabeled Data Alone? [54.47006163160948]
  Self-supervised learning (SSL) can learn rich features in an economical and scalable way. If the released dataset is maliciously poisoned, backdoored SSL models can behave badly when triggers are injected to test samples. We propose two strategies for poison selection: clustering-based selection using pseudolabels, and contrastive selection derived from the mutual information principle.
  arXiv  Detail & Related papers  (2024-04-10T02:54:18Z)
• Model X-ray:Detecting Backdoored Models via Decision Boundary [62.675297418960355]
  Backdoor attacks pose a significant security vulnerability for deep neural networks (DNNs) We propose Model X-ray, a novel backdoor detection approach based on the analysis of illustrated two-dimensional (2D) decision boundaries. Our approach includes two strategies focused on the decision areas dominated by clean samples and the concentration of label distribution.
  arXiv  Detail & Related papers  (2024-02-27T12:42:07Z)
• Erasing Self-Supervised Learning Backdoor by Cluster Activation Masking [65.44477004525231]
  Researchers have recently found that Self-Supervised Learning (SSL) is vulnerable to backdoor attacks. In this paper, we propose to erase the SSL backdoor by cluster activation masking and propose a novel PoisonCAM method. Our method achieves 96% accuracy for backdoor trigger detection compared to 3% of the state-of-the-art method on poisoned ImageNet-100.
  arXiv  Detail & Related papers  (2023-12-13T08:01:15Z)
• Tensor Trust: Interpretable Prompt Injection Attacks from an Online Game [86.66627242073724]
  This paper presents a dataset of over 126,000 prompt injection attacks and 46,000 prompt-based "defenses" against prompt injection. To the best of our knowledge, this is currently the largest dataset of human-generated adversarial examples for instruction-following LLMs. We also use the dataset to create a benchmark for resistance to two types of prompt injection, which we refer to as prompt extraction and prompt hijacking.
  arXiv  Detail & Related papers  (2023-11-02T06:13:36Z)
• VELVET: a noVel Ensemble Learning approach to automatically locate VulnErable sTatements [62.93814803258067]
  This paper presents VELVET, a novel ensemble learning approach to locate vulnerable statements in source code. Our model combines graph-based and sequence-based neural networks to successfully capture the local and global context of a program graph. VELVET achieves 99.6% and 43.6% top-1 accuracy over synthetic data and real-world data, respectively.
  arXiv  Detail & Related papers  (2021-12-20T22:45:27Z)

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.