Offensive tool determination strategy R.I.D.D.L.E. + (C)
- URL: http://arxiv.org/abs/2511.12704v1
- Date: Sun, 16 Nov 2025 17:44:21 GMT
- Title: Offensive tool determination strategy R.I.D.D.L.E. + (C)
- Authors: Herman Errico,
- Abstract summary: Intentional threats are a major risk factor related to vulnerabilities in critical infrastructure assets.<n>This research proposes a methodology that can be added as an additional phase in the risk assessment process.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Intentional threats are a major risk factor related to vulnerabilities in critical infrastructure assets, and an accurate risk assessment is necessary to analyze threats, assess vulnerabilities, and evaluate potential impacts on assets and systems. This research proposes a methodology that can be added as an additional phase in the risk assessment process. The method introduces an extra analytical parameter concerning offensive tool characteristics, improving the understanding of intentional threats. The methodology is presented using clear and accessible language suitable for a broad audience. It is based on an approach described as an "offensive tool determination strategy," summarized by the acronym R.I.D.D.L.E.+C, which refers to the variables used in the analysis: resistance, intrusion timing, damage, disruption timing, latency, efficiency, and cost. These variables are evaluated using open-source intelligence. Each variable is assigned a specific range of values according to its potential impact on the targeted asset. A matrix is then provided for practical application, which can reveal unexpected vulnerabilities and offer a more granular framework for decision-making and security planning.
Related papers
- Toward Quantitative Modeling of Cybersecurity Risks Due to AI Misuse [50.87630846876635]
We develop nine detailed cyber risk models.<n>Each model decomposes attacks into steps using the MITRE ATT&CK framework.<n>Individual estimates are aggregated through Monte Carlo simulation.
arXiv Detail & Related papers (2025-12-09T17:54:17Z) - SafeRBench: A Comprehensive Benchmark for Safety Assessment in Large Reasoning Models [60.8821834954637]
We present SafeRBench, the first benchmark that assesses LRM safety end-to-end.<n>We pioneer the incorporation of risk categories and levels into input design.<n>We introduce a micro-thought chunking mechanism to segment long reasoning traces into semantically coherent units.
arXiv Detail & Related papers (2025-11-19T06:46:33Z) - RADAR: A Risk-Aware Dynamic Multi-Agent Framework for LLM Safety Evaluation via Role-Specialized Collaboration [81.38705556267917]
Existing safety evaluation methods for large language models (LLMs) suffer from inherent limitations.<n>We introduce a theoretical framework that reconstructs the underlying risk concept space.<n>We propose RADAR, a multi-agent collaborative evaluation framework.
arXiv Detail & Related papers (2025-09-28T09:35:32Z) - Preliminary Investigation into Uncertainty-Aware Attack Stage Classification [81.28215542218724]
This work addresses the problem of attack stage inference under uncertainty.<n>We propose a classification approach based on Evidential Deep Learning (EDL), which models predictive uncertainty by outputting parameters of a Dirichlet distribution over possible stages.<n>Preliminary experiments in a simulated environment demonstrate that the proposed model can accurately infer the stage of an attack with confidence.
arXiv Detail & Related papers (2025-08-01T06:58:00Z) - A Survey on Model Extraction Attacks and Defenses for Large Language Models [55.60375624503877]
Model extraction attacks pose significant security threats to deployed language models.<n>This survey provides a comprehensive taxonomy of extraction attacks and defenses, categorizing attacks into functionality extraction, training data extraction, and prompt-targeted attacks.<n>We examine defense mechanisms organized into model protection, data privacy protection, and prompt-targeted strategies, evaluating their effectiveness across different deployment scenarios.
arXiv Detail & Related papers (2025-06-26T22:02:01Z) - Real-Time Detection of Insider Threats Using Behavioral Analytics and Deep Evidential Clustering [0.0]
We propose a novel framework for real-time detection of insider threats using behavioral analytics combined with deep evidential clustering.<n>Our system captures and analyzes user activities, applies context-rich behavioral features, and classifies potential threats.<n>We evaluate our framework on benchmark insider threat datasets such as CERT and TWOS, achieving an average detection accuracy of 94.7% and a 38% reduction in false positives compared to traditional clustering methods.
arXiv Detail & Related papers (2025-05-21T11:21:33Z) - Enhancing Cyber Security Through Predictive Analytics: Real-Time Threat Detection and Response [0.8883733362171032]
This study evaluates the application of predictive analytics for real-time cyber-attack detection and response.<n>We analyzed key features such as attack type, packet length, anomaly scores, protocol usage, and geo-location patterns to assess their predictive value.
arXiv Detail & Related papers (2024-07-15T16:11:34Z) - Threat-Informed Cyber Resilience Index: A Probabilistic Quantitative Approach to Measure Defence Effectiveness Against Cyber Attacks [0.36832029288386137]
This paper introduces the Cyber Resilience Index (CRI), a threat-informed probabilistic approach to quantifying an organisation's defence effectiveness against cyber-attacks (campaigns)
Building upon the Threat-Intelligence Based Security Assessment (TIBSA) methodology, we present a mathematical model that translates complex threat intelligence into an actionable, unified metric similar to a stock market index, that executives can understand and interact with while teams can act upon.
arXiv Detail & Related papers (2024-06-27T17:51:48Z) - Dynamic Vulnerability Criticality Calculator for Industrial Control Systems [0.0]
This paper introduces an innovative approach by proposing a dynamic vulnerability criticality calculator.
Our methodology encompasses the analysis of environmental topology and the effectiveness of deployed security mechanisms.
Our approach integrates these factors into a comprehensive Fuzzy Cognitive Map model, incorporating attack paths to holistically assess the overall vulnerability score.
arXiv Detail & Related papers (2024-03-20T09:48:47Z) - Mapping LLM Security Landscapes: A Comprehensive Stakeholder Risk Assessment Proposal [0.0]
We propose a risk assessment process using tools like the risk rating methodology which is used for traditional systems.
We conduct scenario analysis to identify potential threat agents and map the dependent system components against vulnerability factors.
We also map threats against three key stakeholder groups.
arXiv Detail & Related papers (2024-03-20T05:17:22Z) - On the Security Risks of Knowledge Graph Reasoning [71.64027889145261]
We systematize the security threats to KGR according to the adversary's objectives, knowledge, and attack vectors.
We present ROAR, a new class of attacks that instantiate a variety of such threats.
We explore potential countermeasures against ROAR, including filtering of potentially poisoning knowledge and training with adversarially augmented queries.
arXiv Detail & Related papers (2023-05-03T18:47:42Z)
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.