Western governments have adopted an assortment of counter-hybrid threat measures to defend against hostile actions below the conventional military threshold. The impact of these measures is unclear because of the ambiguity of hybrid threats, their cross-domain nature, and uncertainty about how countermeasures shape adversarial behavior. This paper offers a novel approach to clarifying this impact by unifying previously bifurcating hybrid threat modeling methods through a (multi-agent) influence diagram framework. The model balances the costs of countermeasures, their ability to dissuade the adversary from executing hybrid threats, and their potential to mitigate the impact of hybrid threats. We run 1000 semi-synthetic variants of a real-world-inspired scenario simulating the strategic interaction between attacking agent A and defending agent B over a cyber attack on critical infrastructure to explore the effectiveness of a set of five different counter-hybrid threat measures. Counter-hybrid measures range from strengthening resilience and denial of the adversary's ability to execute a hybrid threat to dissuasion through the threat of punishment. Our analysis primarily evaluates the
Large Language Models (LLMs) are currently being integrated into industrial software applications to help users perform more complex tasks in less time. However, these LLM-Integrated Applications (LIA) expand the attack surface and introduce new kinds of threats. Threat modeling is commonly used to identify these threats and suggest mitigations. However, it is a time-consuming practice that requires the involvement of a security practitioner. Our goals are to 1) provide a method for performing threat modeling for LIAs early in their lifecycle, (2) develop a threat modeling tool that integrates existing threat models, and (3) ensure high-quality threat modeling. To achieve the goals, we work in collaboration with our industry partner. Our proposed way of performing threat modeling will benefit industry by requiring fewer security experts' participation and reducing the time spent on this activity. Our proposed tool combines LLMs and Retrieval Augmented Generation (RAG) and uses sources such as existing threat models and application architecture repositories to continuously create and update threat models. We propose to evaluate the tool offline -- i.e., using benchmarking -- and onl
The increasing sophistication of technology systems makes traditional threat modeling hard to scale, especially for small organizations with limited resources. This paper develops and evaluates AegisShield, a generative AI enhanced threat modeling tool that implements STRIDE and MITRE ATT&CK to automate threat generation and provide systematic assessments. By integrating real time threat intelligence from the National Vulnerability Database and AlienVault Open Threat Exchange, AegisShield produces streamlined and accessible threat descriptions. Our assessment of 243 threats from 15 case studies and over 8000 AI generated threats shows that AegisShield reduces complexity (p less than 0.001), yields outputs semantically aligned with expert developed threats (p less than 0.05), and achieves an 85.4 percent success rate in mapping threats to MITRE ATT&CK techniques (p less than 0.001). Automating and standardizing threat modeling helps under resourced organizations address risk earlier and supports wider adoption of secure by design practices.
Threat detection in Natural Language Processing lacks consistent definitions and standardized benchmarks, and is often conflated with broader phenomena such as toxicity, hate speech, or offensive language. In this work, we introduce ThreatCore, a public available benchmark dataset for fine-grained threat detection that distinguishes between explicit threats, implicit threats, and non-threats. The dataset is constructed by aggregating multiple publicly available resources and systematically re-annotating them under a unified operational definition of threat, revealing substantial inconsistencies across existing labels. To improve the coverage of underrepresented cases, particularly implicit threats, we further augment the dataset with synthetic examples, which are manually validated using the same annotation protocol adopted for the re-annotation of the public datasets, ensuring consistency across all data sources. We evaluate Perspective API, zero-shot classifiers, and recent language models on ThreatCore, showing that implicit threats remain substantially harder to detect than explicit ones. Our results also indicate that incorporating Semantic Role Labeling as an intermediate rep
Previous studies on PTA have focused on analyzing privacy threats based on the potential areas of occurrence and their likelihood of occurrence. However, an in-depth understanding of the threat actors involved, their actions, and the intentions that result in privacy threats is essential. In this paper, we present a novel Privacy Threat Model Framework (PTMF) that analyzes privacy threats through different phases. The PTMF development is motivated through the selected tactics from the MITRE ATT\&CK framework and techniques from the LINDDUN privacy threat model, making PTMF a privacy-centered framework. The proposed PTMF can be employed in various ways, including analyzing the activities of threat actors during privacy threats and assessing privacy risks in IoT systems, among others. In this paper, we conducted a user study on 12 privacy threats associated with IoT by developing a questionnaire based on PTMF and recruited experts from both industry and academia in the fields of security and privacy to gather their opinions. The collected data were analyzed and mapped to identify the threat actors involved in the identification of IoT users (IU) and the remaining 11 privacy threa
Air and missile defense, naval force protection, and critical-infrastructure security require rapid allocation of scarce effectors against multiple incoming threats when neutralization is uncertain and observed only after a firing round. We formulate this defensive-allocation problem as a Markov decision process in which a commander assigns a fixed amount of effector capacity each round to heterogeneous threats under three operational objectives: minimizing expected threat-clearance time, maximizing the probability of clearance by a deadline, and maximizing effective assignments before a deadline. Rather than expensive full dynamic optimization, we study simple time-oblivious policies suited to fast implementation. Fair allocation, which ignores threat difficulty and spreads fire evenly, is highly effective: it is optimal for all three objectives under homogeneous threats or low-capacity engagements. Moreover, when effector capacity scales at least linearly with the number of threats, its threat-clearance time trails that of the optimal policy by at most a constant number of rounds. We further develop threat-difficulty-aware greedy policies for each objective, including a constant-
Cybersecurity increasingly relies on threat hunters to proactively identify adversarial activity, yet the cognitive work underlying threat hunting remains underexplored or insufficiently supported by existing tools. Building on prior studies that examined how threat hunters construct and share mental models during investigations, we derived a set of design propositions to support their cognitive and collaborative work. In this paper, we present the Threat Hunter Board, a prototype tool that operationalizes these design propositions by enabling threat hunters to externalize reasoning, organize investigative leads, and maintain continuity across sessions. Using a design science paradigm, we describe the solution design rationale and artifact development. In addition, we propose six design heuristics that form a solution-evaluation framework for assessing cognitive support in threat hunting tools. An initial evaluation using a cognitive walkthrough provides early evidence of feasibility, while future work will focus on user-based validation with professional threat hunters.
Threat modeling (TM) is an important aspect of risk analysis and secure software engineering. Graphical threat models are a recommended tool to analyze and communicate threat information. However, the comparison of different graphical threat models, and the acceptability of these threat models for an audience with a limited technical background, is not well understood, despite these users making up a sizable portion of the cybersecurity industry. We seek to compare the acceptability of three general, graphical threat models, Attack-Defense Trees (ADTs), Attack Graphs (AGs), and CORAS, for users with a limited technical background. We conducted a laboratory study with 38 bachelor students who completed tasks with the three threat models across three different scenarios assigned using a Latin square design. Threat model submissions were qualitatively analyzed, and participants filled out a perception questionnaire based on the Method Evaluation Model (MEM). We find that both ADTs and CORAS are broadly acceptable for a wide range of scenarios, and both could be applied successfully by users with a limited technical background; further, we also find that the lack of a specific tool for
ICS environments are vital to the operation of critical infrastructure such as power grids, water treatment facilities, and manufacturing plants. However, these systems are vulnerable to cyber attacks due to their reliance on interconnected devices and networks, which could lead to catastrophic failures. Therefore, securing these systems from cyber threats becomes paramount. In this context, threat modeling plays an essential role. Despite the advances in threat modeling, the fundamental gap in the state-of-the art is the lack of a systematic methodology for identifying threats in ICS comprehensively. Most threat models in the literature (i) rely on expert knowledge, (ii) only include generic threats such as spoofing, tampering, etc., and (iii) these threats are not comprehensive enough for the systems in question. To overcome these limitations, we propose a novel evidence-based methodology to systematically identify threats based on existing CVE entries of components and their associated fundamental weaknesses in the form of CWE entries - namely, CVE-CWE pairs - and thereby generate a comprehensive threat list. Furthermore, we have implemented our methodology as a ready-to-use too
Building robust safety guardrails is essential for deploying Large Language Models across diverse real-world applications. However, this goal remains challenging because safety risks span heterogeneous threat domains, while existing datasets cover only fragmented risk subsets and rely on inconsistent taxonomies. Consequently, it remains unclear whether current guardrails can generalize beyond narrow evaluation settings. To better understand the robustness of guardrail models, we first introduce GuardZoo, a unified human-annotated benchmark with 32,460 samples covering 15 distinct unsafe categories. Evaluation on GuardZoo reveals that monolithic guardrails suffer from task interference: different threat domains require distinct decision boundaries that are difficult to compress into a single model. We therefore propose RouteGuard, a router-expert framework that triages each conversation to specialized expert guardrails for threat-specific detection. Experiments show that RouteGuard improves fine-grained threat detection over strong guardrail baselines, generalizes better under out-of-domain evaluation, and supports flexible modular expansion to emerging threats.
As artificial intelligence (AI) becomes deeply embedded in critical services and everyday products, it is increasingly exposed to security threats which traditional cyber defenses were not designed to handle. In this paper, we investigate how cyber threat intelligence (CTI) may evolve to address attacks that target AI systems. We first analyze the assumptions and workflows of conventional threat intelligence with the needs of AI-focused defense, highlighting AI-specific assets and vulnerabilities. We then review and organize the current landscape of AI security knowledge. Based on this, we outline what an AI-oriented threat intelligence knowledge base should contain, describing concrete indicators of compromise (IoC) for different AI supply-chain phases and artifacts, and showing how such a knowledge base could support security tools. Finally, we discuss techniques for measuring similarity between collected indicators and newly observed AI artifacts. The review reveals gaps and quality issues in existing resources and identifies potential future research directions toward a practical threat intelligence framework tailored to AI.
With the ever-changing landscape of cyber threats, identifying their origin has become paramount, surpassing the simple task of attack classification. Cyber threat attribution gives security analysts the insights they need to device effective threat mitigation strategies. Such strategies empower enterprises to proactively detect and defend against future cyber-attacks. However, existing approaches exhibit limitations in accurately identifying threat actors, leading to low precision and a significant occurrence of false positives. Machine learning offers the potential to automate certain aspects of cyber threat attribution. The distributed nature of information regarding cyber threat actors and their intricate attack methodologies has hindered substantial progress in this domain. Cybersecurity analysts deal with an ever-expanding collection of cyber threat intelligence documents. While these documents hold valuable insights, their sheer volume challenges efficient organization and retrieval of pertinent information. To assist the cybersecurity analyst activities, we propose a machine learning based approach featuring visually interactive analytics tool named the Cyber-Attack Pattern
Threat modeling has emerged as a key process for understanding relevant threats within businesses. However, understanding the importance of threat events is rarely driven by the business incorporating the system. Furthermore, prioritization of threat events often occurs based on abstract and qualitative scoring. While such scores enable prioritization, they do not allow the results to be easily interpreted by decision-makers. This can hinder downstream activities, such as discussing security investments and a security control's economic applicability. This article introduces QuantTM, an approach that incorporates views from operational and strategic business representatives to collect threat information during the threat modeling process to measure potential financial loss incurred by a specific threat event. It empowers the analysis of threats' impacts and the applicability of security controls, thus supporting the threat analysis and prioritization from an economic perspective. QuantTM comprises an overarching process for data collection and aggregation and a method for business impact analysis. The performance and feasibility of the QuantTM approach are demonstrated in a real-wo
Computing systems face diverse and substantial cybersecurity threats. To mitigate these cybersecurity threats, software engineers need to be competent in the skill of threat modeling. In industry and academia, there are many frameworks for teaching threat modeling, but our analysis of these frameworks suggests that (1) these approaches tend to be focused on component-level analysis rather than educating students to reason holistically about a system's cybersecurity, and (2) there is no rubric for assessing a student's threat modeling competency. To address these concerns, we propose using systems thinking in conjunction with popular and industry-standard threat modeling frameworks like STRIDE for teaching and assessing threat modeling competency. Prior studies suggest a holistic approach, like systems thinking, can help understand and mitigate cybersecurity threats. Thus, we developed and piloted two novel rubrics - one for assessing STRIDE threat modeling performance and the other for assessing systems thinking performance while conducting STRIDE. To conduct this study, we piloted the two rubrics mentioned above to assess threat model artifacts of students enrolled in an upper-lev
Understanding the modus operandi of adversaries aids organizations in employing efficient defensive strategies and sharing intelligence in the community. This knowledge is often present in unstructured natural language text within threat analysis reports. A translation tool is needed to interpret the modus operandi explained in the sentences of the threat report and translate it into a structured format. This research introduces a methodology named TTPXHunter for the automated extraction of threat intelligence in terms of Tactics, Techniques, and Procedures (TTPs) from finished cyber threat reports. It leverages cyber domain-specific state-of-the-art natural language processing (NLP) to augment sentences for minority class TTPs and refine pinpointing the TTPs in threat analysis reports significantly. The knowledge of threat intelligence in terms of TTPs is essential for comprehensively understanding cyber threats and enhancing detection and mitigation strategies. We create two datasets: an augmented sentence-TTP dataset of 39,296 samples and a 149 real-world cyber threat intelligence report-to-TTP dataset. Further, we evaluate TTPXHunter on the augmented sentence dataset and the cy
Threat modelling is the process of identifying potential vulnerabilities in a system and prioritising them. Existing threat modelling tools focus primarily on technical systems and are not as well suited to interpersonal threats. In this paper, we discuss traditional threat modelling methods and their shortcomings, and propose a new threat modelling framework (HARMS) to identify non-technical and human factors harms. We also cover a case study of applying HARMS when it comes to IoT devices such as smart speakers with virtual assistants.
Modern cars are getting so computerised that ENISA's phrase "smart cars" is a perfect fit. The amount of personal data that they process is very large and, yet, increasing. Hence, the need to address citizens' privacy while they drive and, correspondingly, the importance of privacy threat modelling (in support of a respective risk assessment, such as through a Data Protection Impact Assessment). This paper addresses privacy threats by advancing a general modelling methodology and by demonstrating it specifically on soft privacy, which ensures citizens' full control on their personal data. By considering all relevant threat agents, the paper applies the methodology to the specific automotive domain while keeping threats at the same level of detail as ENISA's. The main result beside the modelling methodology consists of both domain-independent and automotive domain-dependent soft privacy threats. While cybersecurity has been vastly threat-modelled so far, this paper extends the literature with a threat model for soft privacy on smart cars, producing 17 domain-independent threats that, associated with 41 domain-specific assets, shape a novel set of domain-dependent threats in automoti
Collaborative threat intelligence via federated learning (FL) faces critical risks from quantum computing, which can compromise classical encryption methods. This study proposes a quantum-secure FL framework using post-quantum cryptography (PQC) to protect cross-organizational data sharing. We expose vulnerabilities in traditional FL through simulated quantum attacks on RSA encrypted gradients and introduce a hybrid architecture integrating NIST-standardized algorithms CRYSTALS-Kyber for key exchange and CRYSTALS-Dilithium for authentication. Testing on APT attack datasets demonstrated 97.6% threat detection accuracy with minimal latency overhead (18.7%), validating real-world viability. A healthcare consortium case study confirmed secure ransomware indicator sharing without breaching privacy regulations. The work highlights the urgency of quantum ready defenses and provides technical guidelines for deploying PQC in FL systems, alongside policy recommendations for standardizing quantum resilience in threat-sharing networks.
The evolution of cybersecurity has spurred the emergence of autonomous threat hunting as a pivotal paradigm in the realm of AI-driven threat intelligence. This review navigates through the intricate landscape of autonomous threat hunting, exploring its significance and pivotal role in fortifying cyber defense mechanisms. Delving into the amalgamation of artificial intelligence (AI) and traditional threat intelligence methodologies, this paper delineates the necessity and evolution of autonomous approaches in combating contemporary cyber threats. Through a comprehensive exploration of foundational AI-driven threat intelligence, the review accentuates the transformative influence of AI and machine learning on conventional threat intelligence practices. It elucidates the conceptual framework underpinning autonomous threat hunting, spotlighting its components, and the seamless integration of AI algorithms within threat hunting processes.. Insightful discussions on challenges encompassing scalability, interpretability, and ethical considerations in AI-driven models enrich the discourse. Moreover, through illuminating case studies and evaluations, this paper showcases real-world implemen
Cyber threat attribution can play an important role in increasing resilience against digital threats. Recent research focuses on automating the threat attribution process and on integrating it with other efforts, such as threat hunting. To support increasing automation of the cyber threat attribution process, this paper proposes a modular architecture as an alternative to current monolithic automated approaches. The modular architecture can utilize opinion pools to combine the output of concrete attributors. The proposed solution increases the tractability of the threat attribution problem and offers increased usability and interpretability, as opposed to monolithic alternatives. In addition, a Pairing Aggregator is proposed as an aggregation method that forms pairs of attributors based on distinct features to produce intermediary results before finally producing a single Probability Mass Function (PMF) as output. The Pairing Aggregator sequentially applies both the logarithmic opinion pool and the linear opinion pool. An experimental validation suggests that the modular approach does not result in decreased performance and can even enhance precision and recall compared to monolith