Cybersecurity

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Overview

Cybersecurity studies how to prevent, detect, investigate, and recover from attacks on software, networks, organizations, and cyber-physical systems. Common approaches include signature-based defenses, statistical and deep-learning intrusion detection, graph and temporal analysis, formal verification, threat-intelligence mapping, and reinforcement-learning or language-model agents. Work spans settings such as cloud and IoT networks, industrial control systems, connected vehicles, vulnerability assessment, and security operations, with benchmarks increasingly testing realistic attack chains and operational response. A central tension is that strong results on fixed datasets or simulated environments may not transfer to changing, adversarial deployments, where false alarms, safety, cost, and human oversight also matter.

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355 papers33 papers in the last four weeks, down 20% on the four weeks before. 0.2% of all new papers.

Jul 29, 2026

SecRespond: Benchmarking AI Agents for Real-World Post-Compromise Incident Response

Large Language Model (LLM) agents are increasingly adopted in real-world security operations with access to host artifacts and command-line interfaces (CLIs), making it critical to thoroughly assess their security capabilities. However, existing cybersecurity benchmarks focus on pre-compromise settings where agents are placed in a clean and idealized environment before an attack occurs. This leaves the post-compromise setting underexplored. To address this gap, we introduce SecRespond, the first benchmark for evaluating LLM agents on the post-compromise incident-response workflow. Given a forensic disk snapshot of a compromised host together with the alerts, vulnerability scans, and baseline checks reported by a host security product, agents are required to produce forensic reports on intrusions, baseline risks, and vulnerability risks, together with a remediation plan. We instantiate this task across 10 cyber ranges, each constructed from a distinct compromised cloud host, spanning 4 entry-point types, 21 ATT&CK techniques, and 5 operating systems. We evaluate 23 frontier LLMs on the OpenCode agent harness. Experimental results show that although current agents can reliably uncover the problems exposed by alerts, they struggle to proactively investigate the disk for silent intrusions and to produce comprehensive, verified remediation plans, with no model achieving complete detection and remediation on any single range. This reveals a fundamental bottleneck in building agents for real-world incident response. The benchmark is publicly available at https://github.com/Alibaba-NLP/qqr/tree/main/data/secrespond.

Lehan Wang, Boli Chen, Ruixue Ding +7

Tongyi Lab, Alibaba Group · Alibaba Cloud Computing, Alibaba Group · The Hong Kong University of Science and Technology

LLM Agent EvaluationCybersecurityDigital ForensicsAI Agent Security Benchmarks
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Jul 28, 2026

(EC)2: Event-Centric Explainability for Cybersecurity Through Multi-Agent LLM Investigations

Security operations centers rely on anomaly detection systems to flag suspicious events. Feature-level explanations for anomaly detectors offer limited value for operational investigations. To effectively handle alerts, analysts need to know contextual relationships and need actionable understanding of the entities involved. This paper introduces an event-centric detector-agnostic approach for explaining cybersecurity alerts in small- to medium-sized enterprise networks. We present (EC)2, a multi-agent framework that performs structured, hypothesis-driven investigation to provide explanations grounded in verifiable evidence. Evaluation results show that the proposed framework improves post-detection analysis by generating operationally meaningful explanations, which also enhance event classification accuracy.

Neta Kirmayer, David Tayouri, Andrés Murillo +3

Ben-Gurion University of the Negev, Beer-Sheva, Israel · Fujitsu

Multi-Agent LLM SystemsExplainable Artificial IntelligenceCybersecurity
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Jul 28, 2026

The Disruptive Impact of Large Language Models on Capture the Flag Competitions and the Path Toward Fair Play

Capture the Flag (CTF) competitions are among cybersecurity's most effective training grounds, developing practical skill across cryptography, web exploitation, and binary exploitation. Large language models (LLMs) can now solve a growing share of challenges with minimal human input, raising urgent questions about fairness, the validity of rankings, and whether participation still delivers the learning that justifies the effort. This paper reports a mixed-methods study of LLM impact on modern CTFs, combining a synthesis of published benchmarks, including a recent government evaluation, case studies of live competition across three challenge categories, structured observation of the public channels where the community debates AI use, and semi-structured interviews with experienced players and organisers. We map the current human-machine capability boundary by category, showing that easy and intermediate challenges in cryptography, web, and binary exploitation are now reliably automated while narrower sub-categories continue to resist. We find that community disagreement about whether AI should be permitted is downstream of an undeclared prior question: what a competition is for. Against this backdrop we contribute a four-component safeguard framework, combining tiered competition divisions, LLM-resistant challenge design, telemetry used investigatively, and a draft community code of conduct, together with a decision tool that ties the combination of safeguards to a competition's declared purpose. The argument reaches beyond CTFs to any setting in cybersecurity where a demonstrated result is taken as evidence of an underlying ability.

Michael Macaulay, Harmony Bouabid, Guo Gen Ang +1

WMG, University of Warwick, Coventry, United Kingdom

CybersecurityLLMs for Cybersecurity
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Jul 28, 2026

Cyber-Capable AI Agents: Vulnerabilities, Evaluation Containment, and Defensive Response

Cyber-capable AI agents combine language models with tools, memory, and execution environments to perform multi-step offensive-security tasks. Existing work separately measures cyber capability and catalogs attacks against agent components, but provides less guidance on containing a capable agent within the environments used to evaluate it. This review synthesizes five vulnerability classes at that boundary: multi-step offensive chains, objectives that conflict with sandbox boundaries, supply-chain and credential exposure, persistent command-and-control, and the speed of automated action. We use two separate preliminary incident records: the reported July 2026 Hugging Face/OpenAI evaluation breach and Anthropic's subsequent three-incident evaluation review. A comparative evidence protocol distinguishes record-specific factual claims from the shared systems lesson: the evaluation environment is itself part of the security boundary. Across the taxonomy and records, we examine controls for containment, privilege separation, provenance, and responder access, including the dual-use problem that defensive artifacts may also enable misuse. The review identifies practical priorities for evaluating cyber capability together with the security of the environment in which that capability is exercised.

Abu Bakar Siddik

Department of Computer Science and Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh

AI Agent EvaluationCybersecurityAI Agent SecurityLLM Agent SecurityAI Agent Safety
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Jul 28, 2026

AI Deployment and Cyber Governance Failures in Public-Sector Organizations: A Typological Analysis

The intersection of artificial intelligence adoption, cybersecurity governance, and public sector institutional constraints has not been examined as a unified analytical problem in the existing literature. Studies address AI cybersecurity risks generically, public sector governance independently, and framework adequacy separately. Existing studies have not integrated these three streams to explain specifically how AI adoption causes cybersecurity governance failure in government organizations, nor test existing governance instruments against AI-specific public sector failure causes. This paper ad-dresses that gap. It proposes a seven-domain typology identifying ten specific AI-driven cyber governance failure causes grounded in public sector institutional analysis. It presents a three-pathway failure model showing how accountability failure, opera-tional resilience failure, and compliance failure interact and reinforce each other. It de-livers a structured coverage matrix testing five major governance frameworks (NIST CSF 2.0, ISO/IEC 27001, COBIT, NIST AI RMF, and ISO/IEC 42001) against the typology, finding that no instrument addresses Shadow AI, speed asymmetry, or gov-ernance vacuum at the operational specificity required for public sector application. The paper introduces speed asymmetry as a named structural construct with a specified mechanism. The framework provides the design specification for an AI-enabled cyber-security maturity model for government organizations.

Md Salahuddin, James Rooney, Fida Hasan

School of Professional Studies, University of New South Wales (UNSW), Canberra, Australia · School of Business, University of New South Wales (UNSW), Australia

AI AccountabilityCybersecurityAI Governance
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Jul 28, 2026

SecDrift: Measuring Sector-Conditioned Security Drift in AI-Generated Code

LLMs are increasingly used for code generation in critical infrastructure, yet the security effect of domain-specific prompting is understudied. We present SecDrift, a benchmark measuring sector-conditioned security drift: the change in static-analysis vulnerability rates when prompts are conditioned on industry contexts versus neutral baselines. We evaluate 7 LLMs (6 producing analyzable code) across 8 CISA critical infrastructure sectors and 9 CWE categories with 5 replicates (5,355 evaluations), using a 5-dimension transformation with a matched-baseline condition that holds the task fixed while substituting only domain terminology. Industry prompts naively appear more secure (14.0% vs. 11.4%, -2.7pp), but the gap is not statistically significant (Fisher's exact p = 0.24, Cohen's h = -0.08) and is a composition artifact of two CWE categories: excluding CWE-502 and CWE-22 eliminates and slightly reverses it (+0.4pp, p = 1.00). A mixed-effects logistic regression confirms sector identity is not a moderator and localizes the only detectable condition effect to those two vulnerability types. 0 of 8 sectors show drift distinguishable from baseline, corrected or uncorrected (|h| < 0.15). A placebo on two non-CISA sectors (e-commerce, online education) reproduces the CISA industry rate almost exactly (10.5% vs. 11.4%, p = 0.63): the small pooled pattern reflects generic industry-framing specificity, not critical-infrastructure identity. In contrast, model selection has a large and consistent effect: among full-output models vulnerability rates range from 11.6% to 16.1%, and these differences persist across conditions. Model choice, not prompt framing, is the more reliable security lever. We release the framework, prompts, generated code, findings, human-validation verdicts, and analysis scripts.

Narayanaswami Natraj Bharadwaj, Dhivya Chandramouleeswaran

Independent Researcher USA

CybersecurityLLM SecurityCode Generation EvaluationSecure Code GenerationLLM Code Generation
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Jul 27, 2026

TRACE-CTI: Auditable Post-Extraction Governance of TTP Claims with Knowledge Graphs

Security Operations Centers increasingly rely on automated mapping of Cyber Threat Intelligence reports to MITRE ATT&CK, yet extractor outputs remain fallible and are often stored without the evidence, provenance, and validation history needed to decide whether an individual mapping should be trusted. We present TRACE- CTI, a post-extraction claim-governance framework that preserves run-level Predictions, aggregates them into configuration-level GraphAssertions, materializes setup-deduplicated corroboration as ConsensusAssertions, and exposes only GraphAssertions backed by policy-compliant validation grounds. The framework retains native evidence granularity, complete extraction provenance, versioned trust decisions, and non-destructive revocation history. We evaluate TRACE-CTI on two public CTI corpora comprising 65 reports and 5,303 sentences, using a controlled 2 x 3 matrix of retrievers and generator families, incrementally ingested across six GraphVersions. All setups are incorporated without schema modification; provenance paths remain complete, operational scopes remain disjoint, and every trusted GraphAssertion has an active qualifying validation ground. Cross-generator-family setup pairs exhibit greater output diversity than same-family pairs. At the final graph state, increasing setup support from k >= 1 to six-setup unanimity raises gold-aligned precision from 25.3% to 90.6%, while recall decreases from 88.2% to 16.3%. The graph also directly answers seven questions about provenance, trust, versioning, dependency, disagreement, and review-queue that the evaluated minimal flat output cannot fully answer without enrichment or reprocessing. These results support explicit, auditable governance of extracted TTP claims; the observed corroboration trajectory is descriptive and does not establish statistical independence or a causal model-family effect.

Federico Valletta, Giacomo Longo, Enrico Russo +1

CASD – School of Advanced Defense Studies, Piazza della Rovere 83, 00165 Rome, Italy

KG ConstructionAI AssuranceCyber Threat IntelligenceData ProvenanceCybersecurity
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Jul 27, 2026

DeepFaith: Evidence-Grounded LLMs for Faithful Incident Reporting in Multi-Stage APT Defense

Advanced Persistent Threats (APTs) are difficult to detect and interpret due to their multi-stage and stealthy nature. While recent autonomous defense systems leverage provenance graphs and learning-based models for detection and mitigation, their outputs remain largely machine-oriented and difficult for analysts to interpret. Large language models (LLMs) offer a promising interface for report generation, but often produce hallucinated or weakly grounded content. In this paper, we propose DeepFaith, an evidence-grounded framework for faithful incident reporting in multi-stage APT defense. DeepFaith transforms structured outputs from autonomous defense and explainability modules into natural-language reports that are explicitly aligned with underlying system evidence. The framework integrates a unified evidence representation, evidence-grounded prompting, faithfulness-aware generation, and post-generation verification to ensure that all generated statements are supported. Experiments in a realistic enterprise testbed demonstrate that DeepFaith improves faithfulness from 0.68 to 0.92, reduces unsupported claims from 0.32 to 0.08, and increases temporal consistency from 0.6 to 0.88, while maintaining concise reports and lower error rates than existing template-based and LLM-based solutions. These results show that evidence-grounded generation enables reliable, interpretable, and actionable reporting for security operations centers.

Trung V. Phan, Tri Gia Nguyen, Thomas Bauschert

Chair of Communication Networks, Technische Universität Chemnitz, 09126 Chemnitz, Germany · Department of Information Assurance, FPT University, Da Nang 50509, Vietnam

CybersecurityLLMs for CybersecurityEvidence-Grounded Generation
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Jul 27, 2026

Agentic Cloud Decoys: A Deception-Driven Framework for Autonomous Intrusion Investigation

Cloud telemetry arrives at a scale that, paradoxically, makes intrusion understanding harder rather than easier. Attackers operate through legitimate identity, federated session tokens, and cloud native APIs indistinguishable from routine administration, and analysts spend an incident reconstructing context the logs already contain. We present Cloud Decoy AI Agent, a framework pairing a high fidelity cloud decoy with an autonomous language model agent that compresses the path from suspicious activity to an analyst ready report. Connecting a decoy to an agent is not a wiring exercise. The unit of investigation is the session rather than the event, and the session key is obscured by the identity layering federated credentials introduce. The agent's evidence horizon must be bounded, since an agent free to query full control plane history inherits the cost and false positive profile deception was meant to remove. And cloud telemetry is partly adversary authored, since object keys and user agent strings are attacker chosen values providers record verbatim, which makes any log to prompt path an indirect prompt injection channel that a decoy widens rather than narrows. We address the first two with a session aggregation operator over a pivot tuple drawn only from provider derived fields, and with dynamic prompt generation, a two stage prompt assembly enforcing a grounding invariant by carrying only fields the agent observed. We identify the third as an unaddressed exposure in this class of system, specify the mitigation it requires, and note our prototype does not implement it. Across ten controlled AWS S3 scenarios, nine were reconstructed completely, no report contained an assertion untraceable to an observed artifact, and latency was four to five minutes. We also state what this evaluation does not establish and name the comparisons that would settle it.

Mohan Manivannan, Dalal Alharthi

College of Information Science University of Arizona Tucson, AZ, USA

Autonomous Cyber DefenseCybersecurityLLM Agent Security
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Jul 26, 2026

ADVERSARIAL: And-Inverter Graph-Assisted Hardware Trojan Detection At Scale

Modern System-on-Chip (SoCs) often contain hundreds of millions to tens of billions of gates, making existing Hardware Trojan (HT) detection methods impractical due to their immense scale. The proposed approach incorporates symbolically enabled learning by modeling flattened gate-level netlists as Boolean networks represented as And-Inverter Graphs (AIGs), where all internal nodes are 2-input AND gates and inversions reside on the edges. Each directed connection is expressed as a triple within a Knowledge Graph Embedding (KGE) framework, producing compact, constant-size per-node representations that retain multi-hop structural context. The AIG's bounded fan-in and uniform semantics ensure training and inference complexity scale linearly with edge count, addressing major scalability bottlenecks in HT detection. Symbolically enabled learning across deep datapaths enables the model to differentiate circuit structures from rare and functionally inconsistent connections that signify potential Trojan triggers and payloads. Experiments on large-scale SoC benchmarks demonstrate clear geometric separation between Trojan and benign nodes and practical scalability.

Yaroslav Popryho, Debjit Pal, Inna Partin-Vaisband

Department of Electrical and Computer Engineering, University of Illinois Chicago, Chicago, IL 60607 USA

Cybersecurity
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Jul 26, 2026

The Illusion of Secure LLM Code: Closing the Security Gap via Iterative Reprompting

Large Language Models (LLMs) are increasingly integrated into software development workflows, yet their ability to autonomously generate secure authentication code remains uncertain. This paper evaluates the security architecture of authentication systems generated by five prominent AI coding assistants through a bi-modal assessment framework combining static code analysis and dynamic penetration testing, mapped to NIST SP 800-63B guidelines. The study examines model behavior across four prompting strategies Basic, Secure, NIST-Based, and Reprompting to reflect varying levels of developer guidance. Empirical results demonstrate that code generated from functional or generically secure prompts consistently omits critical protections, particularly concerning brute-force resistance, session management, and robust password handling. While providing explicit, single-shot NIST context significantly improves compliance, the findings reveal that this remains structurally inadequate. Instead, iterative Reprompting: forcing models into a contextual self-auditing loop is strictly required to achieve a comprehensive, defense-in-depth security architecture. Ultimately, this study proves that current AI coding assistants do not produce secure-by-default applications, dictating that enterprise deployments must transition from single-shot prompt engineering to continuous, standards-driven verification pipelines.

Ishpuneet Singh, Shreyas Mahajan, Gurjot Singh +1

Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology, Patiala, India · David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, Canada

LLM PromptingCybersecuritySoftware SecurityLLM SecuritySecure Code Generation
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Jul 25, 2026

Rendering on Real Silicon: GPU Render-Timing as a Passive, AI-Resistant CAPTCHA Signal

Conventional CAPTCHAs pose puzzles that modern AI systems increasingly solve, while behavioral and cryptographic-attestation defenses carry privacy or enrollment costs. We investigate an orthogonal signal: the physical timing behavior of a client's GPU under a controlled WebGL rendering workload. Unlike WebGL fingerprinting, which hashes pixel output into a static device identifier, we measure render-timing dynamics to classify rather than identify, leaking no persistent identifier. We characterize the in-the-wild adversary with a 12-hour passive deployment (207 unsolicited requests; 86% automated; 85% of browser-claiming clients failed HTTP header-consistency checks). We then collect labeled GPU-timing samples through a single public endpoint exercised by real browsers (positive class, 13 distinct GPUs) and by keyed headless automation across a render-backend matrix (negative class). Software-rendered automation -- empirically the dominant real-world adversary -- separates from genuine GPUs by roughly 5x in mean render time. On a confound-controlled comparison (identical GPU family and browser engine, differing only in headless vs. interactive execution), headless automation on real hardware still exhibits a distinct timing signature, separating from human samples by 75-106% on frame jitter, timer-quantization ratio, and coefficient of variation. We report these as pilot-scale findings on a single GPU architecture and outline the cross-architecture collection required to establish generalization.

David Noever, Forrest McKee

PeopleTec, Inc.

Cybersecurity
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Jul 24, 2026

Micro-Segmentation Anomaly Detection in Zero-Trust Software-Defined Network Fabrics

Zero Trust Architecture (ZTA) principles need rigorous network segmentation and ongoing verification to reduce implicit trust and lateral threat propagation. This paper investigates anomaly detection in software-defined networking (SDN) systems by micro-segmentation, using deep learning models to detect harmful actions that evade traditional coarse-grained monitoring. Two models are developed: a Vision Transformer (ViT) and a 1D Convolutional Neural Network (1D-CNN), which are used to both raw and micro-segmented network flow data. Experimental findings from a simulated zero-trust SDN dataset indicate that micro-segmentation substantially improves detection accuracy. The models trained on segmented input demonstrate enhanced accuracy and F1-scores (F1 = 0.95) compared to those utilizing unsegmented raw data (F1 = 0.90). The ViT-based detector marginally surpasses the 1D-CNN, particularly in recognizing nuanced lateral movement patterns that are unnoticed in unprocessed data. These findings highlight the significance of including micro-segmentation inside zero-trust networks to enhance intrusion detection efficacy. Future efforts will broaden this methodology to include extensive real-world network datasets and dynamic online segmentation techniques.

Ashly Joseph

Network Intrusion DetectionCybersecurityZero-Trust SecurityAnomaly Detection
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Jul 24, 2026

Practical Graph Optimisation and AI-Driven Models for Active Directory Security Hardening

Microsoft's Active Directory (AD) is a directory service that enables the IT admin to manage security permissions and control access within a Windows domain network. As a core management system in many of organisation, AD has become a primary target for adversaries. While many solutions for hardening attack graphs exist, these efforts fall short in addressing several key practical challenges specific to the AD attack graph. First, existing models often assume the graph is static, whereas a real-world AD environment is highly dynamic. Second, most proposed solutions are limited to the defensive measure of revoking vulnerabilities (edge removal), while more active defence mechanisms are largely unstudied. Third, because not all remediations are implementable, a practical end-to-end model must incorporate system admin feedback into the prioritisation process. This thesis aims to address these limitations by studying and proposing a number of game-theoretic and optimisation-based decision-making models. First, we propose a honeypot/decoy placement model based on the principle of minimising the number of shortest paths and the number of Domain Admin-reachable nodes. Second, building on this model, we introduce a defence strategy that considers the dynamic/temporal nature of the AD graph, where the objective is to find the location to deploy decoys that maximises the worst-case incident response time. Third, we introduce an adaptive prioritisation model that queries each high-risk attack path to the IT administrator for mediation. Finally, we introduce an end-to-end adaptive prioritisation model that minimises the approval effort of the system admin by finding a general adaptive edge-removal policy that generalises the system admin's decisions to edges with similar risk features. We show that the problems underlying all of the contributed models are computationally intractable.

Huy Q. Ngo

School of Computer and Mathematical Sciences, The University of Adelaide

Autonomous Cyber DefenseCybersecurity
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Jul 22, 2026

Evaluating Large Language Models for Symbolic Security Protocol Analysis

Security protocols verification relies on formal tools such as ProVerif and OFMC. This study evaluates whether large language models (LLMs) can perform comparable analysis. We test GPT and DeepSeek in chat and reasoning modes over three runs on 130 obfuscated AnB/AnBx protocols covering 388 security goals, scored against ProVerif and OFMC. Each provider uses a single model in both modes, switching reasoning on and off, so both contrasts isolate reasoning itself. Chat models achieve 72.7% recall at 27.3% precision for GPT and 69.3% recall at 27.2% precision for DeepSeek. Reasoning models reverse this trade-off, reaching 66.5% precision and 54.5% recall for GPT and 45.4% precision and 57.3% recall for DeepSeek. Enabling reasoning lifts precision from 27.3% to 64.8% for GPT and from 27.2% to 44.4% for DeepSeek on the consolidated verdict. The goal set is imbalanced, with 89 vulnerable goals against 299 secure ones; a trivial always-secure predictor scores 77.1% accuracy, which only GPT reasoning exceeds. All models perform worst on authentication goals: reasoning models detect well under half of injective and non-injective agreement attacks, whereas chat models over-flag them at low precision. Confidentiality is the exception, with F1 up to 95.7% in reasoning mode. Verdicts are unstable across runs: identical on 89.7% of goals for GPT reasoning, 74.0% for DeepSeek reasoning, 70.1% for GPT chat, and 61.6% for DeepSeek chat. Self-reported confidence is uniformly high yet shows no meaningful correlation with correctness. All results rest on a single zero-shot prompt and two model providers, which limits generalisability. On this benchmark, LLMs do not match formal verification, but may serve, at best, as pre-screening filters.

Paolo Modesti, Syed Ahmed, Ioannis Sfyrakis +1

Teesside University, Middlesbrough, United Kingdom

LLM EvaluationCybersecurityFormal VerificationLLMs for Cybersecurity
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Jul 22, 2026

The Ethics of Autonomous AI Agents for Offensive Security

LLM-driven autonomous agents are reshaping offensive security. Unlike traditional penetration-testing tooling - deterministic, narrowly scoped, and operated by trained practitioners - agentic security tools exhibit indeterminacy along three independent dimensions. First, their actions are drawn from a non-deterministic policy whose outputs resist both ex-ante and ex-post explanation. This complicates incident attribution and pre-deployment safety reviews. Second, their impact is open-ended due to their non-deterministic actions, agency of utilized models, and opaque LLM supply-chains. Third, their user population is indeterminate in both size and required skill: the operating skill floor for using or developing offensive capabilities has dropped sharply. These three properties are linked thematically, but are not derivable from one another. Combined with the structural cost asymmetry between offense and defense, they enable the industrialization of offensive capability. The net short-term effect favors attackers, even if the same technology may, in the long run, democratize access to defensive practice. Existing dual-use cybersecurity and AI-ethics frameworks struggle to address this combination. Our work analyzes how moral attribution becomes diffuse between users, tool-makers, and third parties when employing autonomous AI agents for offensive security. We also examine the stakeholder impact of this technology and provide stratified recommendations.

Andreas Happe, Jürgen Cito, Jasmin Wachter

TU Wien, Vienna, Austria · University of Klagenfurt, Klagenfurt, Austria

AI AccountabilityCybersecurityAI Agent SecurityAI Agent GovernanceResponsible AI
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Jul 22, 2026

An Automated Framework for Extracting Reachable Attack Chains from Cyber Threat Intelligence Reports

Cyber Threat Intelligence (CTI) reports richly describe real-world attack processes, but their unstructured narratives cannot be directly used for automated attack-path reasoning. Existing CTI extraction methods focus on indicators, entities, or TTP labels without modeling the execution conditions and resulting states of each attack step, so the extracted knowledge supports neither state matching nor reachability analysis across multi-stage attack chains. This paper proposes an automated framework that extracts reachable attack chains by modeling each attack step as an attack unit of preconditions, an attack behavior, and postconditions. A multi-stage pipeline assisted by large language models (LLMs) extracts attack behavior skeletons, recovers their preconditions and postconditions, normalizes them into predefined predicates, and repairs broken dependencies; the resulting units are compiled into Datalog-style rules for attack-goal reachability reasoning. On a dataset of 20 CTI reports containing 334 human-validated annotated steps, our framework achieves higher annotated-step coverage than representative CTI extraction systems in recovering attack behaviors. Moreover, by explicitly generating preconditions and postconditions, it produces attack units that are more complete and consistent than those generated by end-to-end LLM baselines. On the extracted chains, Datalog inference reaches the specified attack goal in 19 of 20 reports, while backward search yields 34 attack paths under the generated rules. The source code and experimental artifacts are available in an anonymized repository. .

Wenbo Hou, Ning Hu, Xueping Wang +2

Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies, School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, China · New Network Research Department, Peng Cheng Laboratory, Shenzhen, China · Great Bay University, Dongguan, China

Reachability AnalysisCyber Threat IntelligenceCybersecurity
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Jul 20, 2026

Adversarial Robustness of Phishing Email Detection: A Comparative Study of TF-IDF + Logistic Regression and Fine-Tuned DistilBERT

Phishing emails remain one of the most persistent cybersecurity threats, and machine-learning classifiers are widely used to detect them. Most reported detection accuracies, however, are measured on clean, in-distribution test data rather than on emails deliberately altered to evade detection. This paper reports a controlled, pairwise comparison of two phishing-detection approaches a TF-IDF + Logistic Regression baseline and a fine-tuned DistilBERT transformer trained on a unified corpus of 82,255 emails drawn from six public datasets and evaluated under three conditions: normal in-distribution, synthetic phishing, and adversarial phishing. Both models exceeded 98% accuracy on clean data yet degraded sharply under adversarial testing: TF-IDF + LR fell to 64.00% (a 34.59-percentage-point drop) and DistilBERT fell to 63.64% (a 35.40-percentage-point drop) a gap of only 0.36 percentage points, equivalent to a single email in the 275-sample adversarial test set. LIME, SHAP, and attention-rollout analysis indicate the two models relied on different evidence yet showed similar vulnerability. Pairwise error analysis shows the models agreed on 54.9% of adversarial samples but each made a similar number of exclusive errors (24 and 25 respectively), indicating partly complementary rather than identical failure modes. The results show that clean-data accuracy does not predict adversarial robustness, and that adversarial testing should be a standard part of phishing-detection evaluation.

Tanveer Ahmed, Seyedali Pourmoafil

School of Physics, Engineering and Computer Science, University of Hertfordshire, Hatfield, Herts AL10 9AB, United Kingdom

Adversarial RobustnessCybersecurityPhishing DetectionAdversarial Text Attacks
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Jul 17, 2026

Evaluating Open-Weight LLMs for Generating Structured Threat Information for Autonomous Vehicle Vulnerabilities

Connected and Autonomous Vehicles (CAVs) rely on interconnected software and hardware components, including sensors, Electronic Control Units, in-vehicle infotainment systems, and telematics units, where vulnerabilities can compromise assets, users, and vehicle operations. These vulnerabilities are commonly documented as plain text in the Common Vulnerabilities and Exposures (CVE) database; however, security practitioners require structured information about affected assets, types of weaknesses, and attack behaviors to effectively mitigate the risks from these vulnerabilities. To this end, we evaluate open-weight Large Language Models (LLMs) for generating Structured Threat Information Expression (STIX), a well-known structured format for representing threat information, for CAV-related CVEs. We construct a dataset called CAV-STIXGen that maps CAV vulnerability descriptions to STIX domain objects (SDO), STIX relationship objects (SRO), Common Weakness Enumeration (CWE), and MITRE ATT&CK techniques mappings. Using this dataset, we evaluated 11 open-weight LLMs (4B to 120B parameters) across various prompting strategies and temperatures. Single-model configurations achieve F1 scores of 0.94 for SDO, 0.63 for SRO, and 0.99 for CWE mapping, while complete MITRE ATT&CK mapping remains challenging. In a multi-agent setup, Gemma-4-31B and Codestral-22B achieve F1 scores of 0.91 for SDOs and 0.43 for SROs, respectively. Lastly, we analyze CWE and MITRE ATT&CK co-occurrences to identify recurring threat patterns in the CAV domain, demonstrating how AI-assisted vulnerability-to-STIX translation can automate threat intelligence and prioritize defense in transportation security.

Md Erfan, Ahmed Ryan, Md Kamal Hossain Chowdhury +1

Department of Computer Science, The University of Alabama, Tuscaloosa, USA · Alabama Water Institute, The University of Alabama, Tuscaloosa, USA

CybersecurityLLMs for Cybersecurity
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Jul 17, 2026

Reliable Remediation Impact Prediction for Black-Box Security Ratings

Security rating platforms summarize externally observable cyber exposure and are expected to help organizations prioritize remediation. A platform may want to tell an organization how a candidate remediation action would affect its score, but repeatedly exposing exact score responses can reveal information about the hidden scoring engine. We propose a surrogate based approach for remediation score impact prediction that is designed to respect this opacity constraint. The surrogate predicts scores from organization configurations while explicitly representing checkpoint (i.e., a security check) applicability and the observed checkpoint set. A main challenge is that such predictions are not uniformly reliable: they depend on the amount and structure of the observable checkpoint evidence available for a given configuration. To address this, the approach combines applicability-aware surrogate construction, sensitivity analysis under controlled checkpoint restriction, a reliability layer for identifying unstable predictions, and score-impact prediction for supported remediation actions. Explicit modeling of checkpoint applicability is central throughout: it improves score prediction and provides the feature basis used by the reliability layer to identify unstable cases. We evaluate the approach on a real-world dataset of 5,188 organization configurations from a commercial security rating platform. The results show that the applicability-aware surrogate improves score prediction over simpler feature representations. For remediation, the surrogate predicts the score impact of supported actions, while the reliability layer helps identify cases in which these predicted impacts should be interpreted cautiously.

Nada Hanad, Mehdi Acheli, Ali NourEldin +2

SAMOVAR, Telecom SudParis, Institut Polytechnique de Paris, Palaiseau, France

Surrogate ModelingCybersecurity
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Jul 16, 2026

Qubes OS Security in the Public Record

Qubes OS is a revealing case for security measurement because its architecture makes component boundaries security-relevant. We present a protocol-driven longitudinal analysis of 109 public Qubes Security Bulletins (QSBs, 2011--2025), the official Qubes-maintained Xen Security Advisory (XSA) tracker, and a secondary vulnerability-event sensitivity series. The study measures the public advisory record rather than latent vulnerability incidence or realized compromise. The methodology combines audited deterministic component attribution, change-point analysis, overdispersion checks, severity-proxy weighting, censoring sensitivity, documentary latency lower bounds, and baseline-aware evaluation of vulnerability discovery models (VDMs). The results show persistent upstream dependence in that public record. On the official tracker, 113 of 464 XSAs affect Qubes; under primary labeling, 87 of 109 QSBs (79.8%) are attributable to Xen, CPU/microarchitectural, or other upstream components rather than Qubes-core logic, with similar results under weighted views. Change-point analyses identify 2015Q1 as the dominant break in the quarterly advisory series, while post-2018 annual disclosure rates are statistically flat. Poisson inferences are stable under dispersion diagnostics and negative-binomial sensitivity checks. The attribution codebook performs well in a stratified 30-QSB audit, and S-shaped VDMs fit descriptively but do not significantly outperform a rolling-mean baseline in short-horizon forecasts. Overall, the Qubes public advisory record appears stable, but not quiet: disclosure activity plateaus at a higher level than in the earliest years, while the observed burden remains concentrated in upstream trust anchors.

Alfonso De Gregorio

Pwnshow

Software Vulnerability DetectionCybersecuritySoftware Security
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Jul 14, 2026

AI in Cyberpsychology: A systematic literature review of Cybersecurity enhancement by using AI for analyzing psychology of Victims, Attackers, and Defenders

Cybersecurity is the practice of protecting systems, networks, and data from digital attacks. Cyberpsychology (CPSY) is defined as the use of psychology to enhance cybersecurity applications. Since the early 2010s, the evolution of Artificial Intelligence (AI) has increasingly integrated with CPSY, leveraging advanced data analysis to decode the distinct personality traits and behavioral patterns of victims, attackers, and defenders. In this systematic literature review (SLR), we carefully analyze 34 collected research studies of AI usage in cyberpsychology (AI-CPSY) using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) methodology. The review presents a comprehensive taxonomy of the cyber-security applications, the AI methodologies used, and the psychological concepts employed across the studies . We sort the research studies into four cybersecurity applications: Anomaly Detection (AD), Vulnerability Risk Prediction (VRP), Security Awareness Training (SAT), and Authentication/Identity Verification (AIV). Within each application area, studies are further sorted according to the AI method used including machine learning (ML), deep learning (DL), natural language processing (NLP), and reinforcement learning (RL). Furthermore, the review identifies the most commonly utilized psychological concepts, quantify the datasets used in the field, and present their current implementation and deployment status. At last, it detect research gaps, present open challenges, and deduce the trending and most effective and emerging methodologies used across the AI-CPSY landscape.

Georg Thamer Francis, Malek Malkawi, Sevim Eyüpoğlu +2

Istanbul Medipol University, Department of Computer Engineering, Beykoz, 34820, Istanbul, Turkey · Atlas University, Department of Psychology, Kağıthane,, Istanbul, Turkey · University of Calgary, Department of Computer Science, Calgary,, Alberta, Canada +1

Cybersecurity
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Jul 10, 2026

A Knowledge-Based Multi-Agent Framework for Security Control Recommendation

Hardening IT on-premises environments can be a daunting task for teams without access to adequate cybersecurity expertise. In this regard, Decision Support Systems (DSS) with embedded expert knowledge can assist users by guiding them with security recommendations to meet their objectives. This work proposes a Security DSS that recommends security control sub-families given minimal user requirements indicating coverage of different security dimensions. It leverages a curated, unified dataset from both well-known Information Security (InfoSec) and academic sources. This DSS is defined as a non-zero-sum, simultaneous game that is grounded in a Multi-Agent Influence Diagram (MAID) model and explores the decision space over 7 security dimensions or agents, using no-regret online learning to ultimately find the security control sub-families that best fit the requirements while incurring minimal under- and over-provisioning of security resources. This work was validated in terms of performance and accuracy, among others, for varying dataset sizes. It shows exceptional satisfaction coverage results of 99% when using as little as ~65% of the SW-implementable security controls, running in 1.2-35.7 seconds; and more moderate coverage results of 73%-77% when using ~29% of the controls, resolving in 0.8-13.8 seconds.

Carolina Fernández-Martínez, Shuaib Siddiqui, Vanesa Daza

Cybersecurity & Blockchain Research Group, i2CAT Foundation, Barcelona, 08039, Spain · Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, 08018, Spain · Cybersecurity & Blockchain Research Group, i2CAT

Cybersecurity
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Jul 10, 2026

SMETA-ZSL:Semantic Meta-Alignment for Zero-Shot Threat Classification

Cybersecurity systems must adapt rapidly to emerging threats. However, labeled data for new threat categories is unavailable when those threats first appear. Generalized zero-shot learning offers a natural solution by enabling recognition of unseen classes through auxiliary semantic knowledge rather than labeled examples. Large language models are particularly promising in this setting because they can convert unstructured CTI reports into semantic prototypes for emerging threats. However, applying language-driven zero-shot learning to cybersecurity is difficult due to strong semantic overlap between threat descriptions, heterogeneity between behavioral attributes and text, severe class imbalance, and open-set conditions where unseen threats are unknown during training. We propose SMETA-ZSL, that learns semantic prototypes from overlapping language descriptions through contrastive finetuning, aligns behavioral features through episodic meta-learning and knowledge distillation, and performs adaptive routing for generalization across seen-unseen classes. Across 7 benchmarks, SMETA-ZSL delivers the strongest overall generalized zero-shot performance under the strictest inductive setting, surpassing prior methods by 10.8 points on average, with gains up to 18.1 points. Github:https://github.com/Security-And-Intelligence-Lab-UTEP/SMETA-ZSL

Ivan Alejandro Montoya Sanchez, Anantaa Kotal, Aritran Piplai

The University of Texas at El Paso

Zero-Shot LearningCyber Threat IntelligenceMeta-LearningCybersecurityLLMs for Cybersecurity
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Jul 10, 2026

VEXAIoT: Autonomous IoT Vulnerability EXploitation using AI Agents

Internet of Things (IoT) systems are inherently vulnerable due to constrained hardware, outdated firmware, and insecure default configurations, creating a need for scalable and adaptive security testing approaches. While recent adoptions of Large Language Model (LLM) agents have demonstrated promise in penetration testing and Capture-the-Flag (CTF) environments, their application to IoT specific vulnerabilities remains unexplored. This paper presents an autonomous multi-agent framework, referred to as Vulnerability EXploitation using AI Agents (VEXAIoT), for vulnerability discovery and exploitation in IoT environments using LLM-based reasoning and offensive security tools. The framework combines a vulnerability detection agent and an attack execution agent to perform reconnaissance, plan attack sequences, and execute exploits against vulnerable IoT services. The system is evaluated in IoTGoat and Metasploitable environments across ten attack scenarios mapped to OWASP IoT vulnerabilities. Experimental results show attack success rate of up to 100% with low token overhead and average execution times under two minutes for most attacks. Across 260 attack executions, VEXAIoT achieves a 95.0% overall success rate, including 94.5% success in IoTGoat and 96.7% success in Metasploitable2. These results demonstrate the potential for LLM-driven agents to automate IoT vulnerability assessment and offensive security workflows in controlled environments

Katherine Swinea, Kshitiz Aryal, Lopamudra Praharaj +1

Department of Computer Science, Tennessee Tech University, TN, USA. · School of Interdisciplinary Informatics, University of Nebraska Omaha, NE, USA. · Dept. of Mathematics & Computer Science, University of North Carolina at Pembroke, NC, USA.

CybersecurityLLM Agent SecurityAutomated Penetration TestingMulti-Agent System Security
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Jul 10, 2026

Authority-Bound Governance of Heterogeneous AI Security Decisions in Telecom and IoT Networks

Artificial intelligence (AI)-enabled security decision systems in telecom and IoT networks can draw on heterogeneous models whose outputs may trigger operational actions. Recording such decisions on a blockchain does not establish that they are authorised, applicable, policy-consistent, or still valid at execution time. This paper presents governance-2, an authority-bound and fail-closed architecture that separates upstream scientific decision formation from downstream operational enforcement. Each governed case is bound to registered dataset, model, policy, deployment, and optional refiner authorities. Smart-contract checks enforce role separation, authority compatibility, score-to-state and state-to-action consistency, lifecycle validity, replay protection, pause control, authority revocation, and exact-action execution. Evaluation uses two independent branches: a controlled spectrum-access replay with four frozen heterogeneous decision configurations and a measured radio-frequency branch based on WiFiSpectralJam. Across eight frozen measured-data decision streams, governance-2 processes 153,744 stream-case instances derived from 19,218 measured captures while preserving interference-specific semantics and unresolved review states. The full contract rejects all tested invalid operations; stateful invariant testing completes 2,000 generated transaction sequences with zero invariant violations; and single-capability ablation shows that removing an enforcement family exposes its assigned invalid operations while unrelated protections remain active. The results indicate that heterogeneous AI security decisions can share a common governance plane across telecom and IoT settings without redefining their scientific semantics.

Saviz Changizi, Nasibeh Mohammadzadeh, Mohammad Shojafar +1

6G Innovation Centre, Institute for Communication Systems, University of Surrey, UK · Department of Network Engineering, Polytechnic University of Catalonia, Barcelona, Spain

CybersecurityAI GovernanceAccess Control
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Jul 10, 2026

Neuro-Agentic Control: A Deep Learning-based LLM-Powered Agentic AI Framework for Controlling Security Controls

Cyberattacks on operational technology are increasingly causing costly downtime and physical damage, exposing the limitations of traditional rule-based monitoring in industrial IoT environments. While Large Language Models (LLMs) have strong semantic reasoning abilities to assist in decision support, their hallucinatory nature presents unacceptable safety liabilities for closed-loop control. This paper introduces a neuro-agentic control framework, a novel architecture that couples an LLM-based planner (i.e., such as Gemini 2.5 Flash-Lite) with a pre-trained Time-Series Foundation Model (TimesFM), to achieve physics-grounded autonomous defense. The paper introduces a Counterfactual Physics Injection'' mechanism that simulates the impact of LLM-proposed interventions within the numerical latent space of the foundation model before actuation, while allowing the system to reject hallucinatory or unsafe actions. Evaluated on an industrial dataset (e.g., the Secure Water Treatment (SWaT)) in the context of stochastic attack scenarios, the framework exhibited better performance compared to LSTM and TCN baselines. The Neuro-Agentic Loop prevented five breaches (33.3%) below the threshold versus LSTM (26.7%) and TCN (13.3%), with zero physically invalid (hallucinated) actions executed. These results demonstrate the efficacy of using foundation models as deterministic Sentinels'' to safeguard agentic AI in critical infrastructure.

Saroj Gopali, Bipin Chhetri, Deepika Giri +2

Department of Computer Science1, Texas Tech University1 · Cumberland University2 · Advanced Academic Programs Science3, Johns Hopkins University3

Industrial Control System SecurityLanguage Model-Based ControlSafety-Critical ControlCybersecurityAI Agent SecurityAI Agent Safety
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Jul 9, 2026

Detecting Ladder Logic Bombs in IEC 61131-3 PLC Programs using ESBMC-PLC+: A Formal Verification Approach with Trigger Synthesis

A Ladder Logic Bomb (LLB) is malicious control logic in a Programmable Logic Controller (PLC) program that lies dormant until a trigger activates a payload to manipulate actuators, forge sensor readings, or deny operator control. We observe that real malicious logic hides inside function-block bodies, which existing ladder-diagram verifiers drop from their intermediate representation (IR), making bombs invisible to provers. We present ESBMC-LLB, which uses ESBMC-PLC+ as its verification engine and adds a modeling layer that exposes function-block logic and recasts bomb detection as a formal verification problem: a scan-watchdog exposes non-termination payloads, and output wiring exposes actuator-forgery payloads as safety violations. k-induction gives an unbounded proof of bomb-absence across all scans, and the bounded model checker returns a counterexample that is the trigger - guarantees that signature, anomaly, and CFG-triage detectors lack. On the public Iacobelli 2024 dataset, ESBMC-LLB detects all 30 bombs and recovers every trigger; it also detects adaptive triggers (computed, opaque-arithmetic, multi-scan) that evade CFG-triage. We also report the first semantic model-checker evaluation on PLC-Defuser's SWaT corpus: our analog extension makes the full corpus parseable; on v1.0.0, it detects 149/150 bombs (99%) with zero false positives, recovering each trigger; on a later version with nonlinear non-termination bombs, detection drops to 49% as the SMT solver times out. We conclude that semantic model checking and CFG-triage are complementary - the former gives unbounded proofs, adaptive-trigger robustness, and handles Boolean/integer and linear analog logic; the latter leads to nonlinear analog non-termination, and we delineate where each wins.

Pierre Dantas, Lucas Cordeiro, Waldir Junior

Electrical Engineering Federal University of Amazonas (UFAM) Manaus, AM, Brazil

Industrial Control System SecurityCybersecurityBackdoor DetectionFormal VerificationMalware Detection
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Jul 9, 2026

Reverse Engineering Compliance: A Dual-Graph Verification Framework for Auditing Legacy IT Security Concepts

The NIS-2 Directive increases the need for continuous, auditable compliance evidence and motivates a shift from document-based compliance toward machine-readable compliance artifacts. The Open Security Controls Assessment Language (OSCAL) is a standard for this purpose, which the German Federal Office for Information Security (BSI) is adapting with Grundschutz++. However, companies are still managing extensive legacy IT security concepts (IT-SCs), and migrating them without verification could transfer outdated assets into the new format. While existing research primarily addresses the generation of new concepts, there is a lack of a verification framework that extracts legacy IT-SCs into an auditable intermediate representation, deterministically compares the extracted graph with an independently constructed reference state, and exports schema-valid OSCAL artifacts. This paper introduces the Automated Security Concept Structure Extraction and Reverse Topology-checking (ASSERT) Framework, which addresses this gap by using ontology-based extraction of legacy documents into formal document graphs, a five-class graph difference against a verified reference graph, and the export into schema-valid OSCAL outputs for system description and assessment evidence. Using the BSI's RecPlast dataset, we compare a local open-weight model and a commercial model across three configurations with different levels of reference-ontology exposure. The evaluation shows that ASSERT makes document-infrastructure inconsistencies measurable, but reveals a trade-off between discovering undocumented entities and enforcing a schema.

Lea Roxanne Muth, Marian Margraf

Department of Mathematics and Computer Science Freie Universität Berlin Berlin, Germany

Ontology-Guided Information ExtractionCybersecurity
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Jul 9, 2026

From Legacy Documentation to OSCAL: An MCP-Based Agent Pipeline for Threat-Informed Continuous Compliance in Critical Infrastructure

In critical infrastructure, operational technology environments often cannot be actively scanned, and yet active system feedback is needed for risk assessment and compliance. This paper presents a non-invasive, MCP-grounded multi-agent pipeline that converts natural-language system descriptions into source-verified knowledge graph and audit-ready artifacts in the NIST OSCAL format for continuous automated compliance management. The architecture decouples LLM-based reasoning from deterministic knowledge retrieval against authoritative threat-intelligence sources, reducing the risk of fabricated vulnerabilities and hallucinated attack paths. In an evidence-based synthetic scenario of a water utility, the pipeline achieves 0.90 CVE recall and perfect D3FEND recall. It generates a schema-valid OSCAL System Security Plan and an OSCAL Security Assessment Report. Nevertheless, the core insight is not that grounding via MCP eliminates errors (e.g., hallucinations) entirely from the pipeline, but that it shifts errors into the first phase of asset extraction from the natural language description. Here, a single incorrectly extracted entity can lead to genuine but irrelevant CVEs in subsequent stages of the pipeline, which consumes time and resources. However, it makes the remaining risk visible, verifiable, and suitable for a time-efficient manual review, since the infrastructure (e.g., version numbers, OS, etc.) is typically known.

Lea Roxanne Muth, Marian Margraf

Department of Mathematics and Computer Science Freie Universität Berlin Berlin, Germany

CybersecurityLLMs for Cybersecurity
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