Weaponizing Language Models for Cybersecurity Offensive Operations: Automating Vulnerability Assessment Report Validation; A Review Paper

• URL: http://arxiv.org/abs/2505.04265v1
• Date: Wed, 07 May 2025 09:14:55 GMT
• Title: Weaponizing Language Models for Cybersecurity Offensive Operations: Automating Vulnerability Assessment Report Validation; A Review Paper
• Abstract summary: Large Language Models (LLMs) have emerged as a highly promising tool for defensive and offensive cybersecurity-related strategies.<n>This paper investigates the capabilities of LLMs in automating and improving the validation process of the Vulnerability Assessment (VA) report.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: This, with the ever-increasing sophistication of cyberwar, calls for novel solutions. In this regard, Large Language Models (LLMs) have emerged as a highly promising tool for defensive and offensive cybersecurity-related strategies. While existing literature has focused much on the defensive use of LLMs, when it comes to their offensive utilization, very little has been reported-namely, concerning Vulnerability Assessment (VA) report validation. Consequentially, this paper tries to fill that gap by investigating the capabilities of LLMs in automating and improving the validation process of the report of the VA. From the critical review of the related literature, this paper hereby proposes a new approach to using the LLMs in the automation of the analysis and within the validation process of the report of the VA that could potentially reduce the number of false positives and generally enhance efficiency. These results are promising for LLM automatization for improving validation on reports coming from VA in order to improve accuracy while reducing human effort and security postures. The contribution of this paper provides further evidence about the offensive and defensive LLM capabilities and therefor helps in devising more appropriate cybersecurity strategies and tools accordingly.

Related papers

• Execution-State-Aware LLM Reasoning for Automated Proof-of-Vulnerability Generation [36.950993500170014]
  We present DrillAgent, an agentic framework that reformulates PoV generation as an iterative hypothesis-verification-refinement process.<n>We evaluate DrillAgent on SEC-bench, a large-scale benchmark of real-world C/C++ vulnerabilities.
  arXiv  Detail & Related papers  (2026-02-14T03:17:27Z)
• Scalable and Robust LLM Unlearning by Correcting Responses with Retrieved Exclusions [49.55618517046225]
  Language models trained on web-scale corpora risk memorizing and exposing sensitive information.<n>We propose Corrective Unlearning with Retrieved Exclusions (CURE), a novel unlearning framework.<n>CURE verifies model outputs for leakage and revises them into safe responses.
  arXiv  Detail & Related papers  (2025-09-30T09:07:45Z)
• LLM Embedding-based Attribution (LEA): Quantifying Source Contributions to Generative Model's Response for Vulnerability Analysis [1.8780665584728753]
  Large Language Models (LLMs) have been widely adopted for cybersecurity threat analysis.<n>Retrieval-Augmented Generation (RAG) pipelines mitigate the problem by injecting up-to-date authoritative sources into the model context.<n>This work proposes LLM Embedding-based Attribution (LEA) -- a novel, explainable metric to paint a clear picture on the 'percentage of influence' the pre-trained knowledge vs. retrieved content has for each generated response.
  arXiv  Detail & Related papers  (2025-06-12T21:20:10Z)
• AegisLLM: Scaling Agentic Systems for Self-Reflective Defense in LLM Security [74.22452069013289]
  AegisLLM is a cooperative multi-agent defense against adversarial attacks and information leakage.<n>We show that scaling agentic reasoning system at test-time substantially enhances robustness without compromising model utility.<n> Comprehensive evaluations across key threat scenarios, including unlearning and jailbreaking, demonstrate the effectiveness of AegisLLM.
  arXiv  Detail & Related papers  (2025-04-29T17:36:05Z)
• LLM-Safety Evaluations Lack Robustness [58.334290876531036]
  We argue that current safety alignment research efforts for large language models are hindered by many intertwined sources of noise.<n>We propose a set of guidelines for reducing noise and bias in evaluations of future attack and defense papers.
  arXiv  Detail & Related papers  (2025-03-04T12:55:07Z)
• Securing Large Language Models: Addressing Bias, Misinformation, and Prompt Attacks [12.893445918647842]
  Large Language Models (LLMs) demonstrate impressive capabilities across various fields, yet their increasing use raises critical security concerns. This article reviews recent literature addressing key issues in LLM security, with a focus on accuracy, bias, content detection, and vulnerability to attacks.
  arXiv  Detail & Related papers  (2024-09-12T14:42:08Z)
• 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)
• Exploring Automatic Cryptographic API Misuse Detection in the Era of LLMs [60.32717556756674]
  This paper introduces a systematic evaluation framework to assess Large Language Models in detecting cryptographic misuses. Our in-depth analysis of 11,940 LLM-generated reports highlights that the inherent instabilities in LLMs can lead to over half of the reports being false positives. The optimized approach achieves a remarkable detection rate of nearly 90%, surpassing traditional methods and uncovering previously unknown misuses in established benchmarks.
  arXiv  Detail & Related papers  (2024-07-23T15:31:26Z)
• Defending Large Language Models Against Attacks With Residual Stream Activation Analysis [0.0]
  Large Language Models (LLMs) are vulnerable to adversarial threats.<n>This paper presents an innovative defensive strategy, given white box access to an LLM.<n>We apply a novel methodology for analyzing distinctive activation patterns in the residual streams for attack prompt classification.
  arXiv  Detail & Related papers  (2024-06-05T13:06:33Z)
• A Case Study of LLM for Automated Vulnerability Repair: Assessing Impact of Reasoning and Patch Validation Feedback [7.742213291781287]
  We present VRpilot, a vulnerability repair technique based on reasoning and patch validation feedback. Our results show that VRpilot generates, on average, 14% and 7.6% more correct patches than the baseline techniques on C and Java.
  arXiv  Detail & Related papers  (2024-05-24T16:29:48Z)
• ReEval: Automatic Hallucination Evaluation for Retrieval-Augmented Large Language Models via Transferable Adversarial Attacks [91.55895047448249]
  This paper presents ReEval, an LLM-based framework using prompt chaining to perturb the original evidence for generating new test cases. We implement ReEval using ChatGPT and evaluate the resulting variants of two popular open-domain QA datasets. Our generated data is human-readable and useful to trigger hallucination in large language models.
  arXiv  Detail & Related papers  (2023-10-19T06:37:32Z)
• Do-Not-Answer: A Dataset for Evaluating Safeguards in LLMs [59.596335292426105]
  This paper collects the first open-source dataset to evaluate safeguards in large language models. We train several BERT-like classifiers to achieve results comparable with GPT-4 on automatic safety evaluation.
  arXiv  Detail & Related papers  (2023-08-25T14:02:12Z)
• Automatically Correcting Large Language Models: Surveying the landscape of diverse self-correction strategies [104.32199881187607]
  Large language models (LLMs) have demonstrated remarkable performance across a wide array of NLP tasks. A promising approach to rectify these flaws is self-correction, where the LLM itself is prompted or guided to fix problems in its own output. This paper presents a comprehensive review of this emerging class of techniques.
  arXiv  Detail & Related papers  (2023-08-06T18:38:52Z)

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.