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arXiv2026

A Scenario-Based Evaluation of CRQC+AI Vulnerability Spectrum for TLS 1.3 Cryptographic Dependencies

This paper evaluates quantum and AI-accelerated risks to TLS 1.3 cryptographic dependencies under an evidence-tiered model, distinguishing mechanism-backed threats (Shor algorithm against RSA and ECC) from contingency-backed risks to lattice-based post-quantum cryptography (PQC) and hypothesis-only risks to hash-based and symmetric primitives. We do not identify any known breaks of ML-KEM, ML-DSA, SLH-DSA, or AES-256. Instead, we use explicit scenario assumptions, organized as a four-scenario capability model with parameters and pseudocode for reproducibility, to stress-test migration timelines accompanied by parameter sensitivity analysis and explicit falsification analysis. The primary methodological contribution is a reproducible scenario-estimation instrument together with its explicit update mechanics: every parameter is a named, anchored quantity that can be varied and the model rerun; a stated protocol maps observed conformance to, or deviation from, the modeled curves onto revisions of specific parameters, so progressive refinements can be tested against accumulating historical data. The paper is a methodological companion to quantum resource-estimation studies and to expert-elicitation timeline surveys such as the Global Risk Institute quantum threat reports, with its revision rules stated explicitly. As of mid-2026, the model does not show any NIST-approved algorithms as broken. Instead, the vulnerability spectrum under different scenarios shows RSA risk crossing the 50% threshold between 2030-2032 and the PQC risk becoming a non-zero risk after 2032-2035 under contingency scenarios conditional on the unproven dimension-collapse. We urge PQC migration as mandatory per the 2030 and 2031 federal deadlines and by Mosca HNDL reasoning, and that crypto-agility and hybrid cryptographic deployment be considered necessary complements to any PQC migration efforts.

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arXiv2026

Long-Range Indirect Control-Flow Prediction in Stripped Binaries via Dual Virtual Hubs and Multi-Task Graph Learning

Recovering indirect control-flow (ICF) edges is fundamental to binary security analysis, yet existing methods struggle with long-range dependencies, isolate different ICF types, and are often evaluated under protocols vulnerable to label noise and data leakage. We present ICFlowNet, a unified framework for long-range ICF prediction in stripped binaries. ICFlowNet introduces candidate-aware Dual Virtual Hubs, a Global Code Hub and a Global Data Hub, to create short routing paths between distant code and data evidence, and combines them with multi-task graph learning to jointly model indirect calls, indirect tail calls, jump tables, and returns. To enable credible evaluation, we further develop a leakage-aware, noise-controlled pipeline with package-level splits, function-level mnemonic-hash deduplication, and a clean test protocol built from dynamic positives and absolute negatives. Using this pipeline, we construct a dataset of 15,901 unique stripped x86-64 binaries, including 1,351 with dynamic ground truth. Experiments show that simply scaling static supervision yields only marginal gains, whereas our structural and multi-task designs are essential: Dual Virtual Hubs improve long-range F1 by up to 9.13 points, multi-task learning adds up to 5.81 points, and the final model outperforms prior baselines by more than 13 F1 points on long-range indirect calls while adding only 11.44 percent topological overhead.

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arXiv2026

Optimizing Credential Blast Radius Through Trust Boundaries and Delegation Under Post-Quantum Authentication Costs

Partitioning interacting services into independently rooted trust domains limits issuer-compromise reach while increasing calls across trust boundaries. Post-quantum replacements for public-key authentication and key-establishment mechanisms can increase crossing latency on constrained or lossy paths. We formulate the joint selection of trust domains and credential-derivation structures under policy and latency constraints, linking separate service-interaction and credential-derivation graphs through domain assignment. Credential blast radius measures weighted service impact after compromise. A linear upper bound supports optimization, while a joint event model gives exact expected impact. We identify when risk from issuers trusted across domains can be incorporated into this linear score, avoiding separate issuer-propagation calculations for each candidate. Although the general problem is NP-hard, we identify restricted cases that can be solved efficiently and exactly. Joint optimization yields lower blast radius than choosing boundaries first in 195 of 230 exhaustive synthetic comparisons, especially under chained delegation. A trace-derived replay used measured post-quantum costs, synthetic risk inputs, a fixed derivation family, and one to six trust domains. Under independent compromise events, mean expected impact was up to 36% lower than with one domain within the latency budget. The framework turns risk assumptions and measured crossing costs into candidate trust-domain and credential-derivation designs.

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arXiv2026

MOLE: Detecting Insider Threats in AI Agents

Model misalignment, prompt injection, or operator misuse could lead AI agents operating frontier-lab accounts to exfiltrate model weights, poison training data, or weaken release gates. Existing benchmarks do not test whether defenders can detect this activity among routine work under a limited review budget. We introduce MOLE, an open benchmark of 150 AI-operated accounts sharing 9 stateful services over 30 workdays, with 12 threats and 8 corpora from four models totaling roughly 20 billion tokens. Of 39 agent models, 72% complete most assigned harmful objectives and agent refusal does not predict completion. MOLE enables comparison of 40 monitors across corpus generators, observability levels, and threats; even the best evaluated monitor in our single-day audit-event comparison misses nearly half of completed harm. MOLE also enables monitor development: benchmark-guided search improves a mid-tier monitor by 49-64%, while selective use of a stronger monitor improves budget-AUC by 10% over applying it to every account-day at comparable modeled cost.

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arXiv2026

AgentDrift: A Step-Labeled Benchmark of Injection-Hijacked LLM Agent Trajectories

LLM agents complete tasks by issuing sequences of tool calls, and every observation they read is a channel through which an indirect prompt injection can enter. A successful injection has a characteristic shape when the trajectory is read in order: a benign prefix gives way to actions that serve the attacker rather than the user. Existing benchmarks measure whether such attacks succeed against live agents, and existing guard models judge a trace as a whole; no public corpus labels, step by step, where an injection enters a trajectory and which steps it corrupts. We present AgentDrift, a benchmark of 12,536 synthetic tool-call trajectories over five agent domains in which every one of the 71,024 steps carries one of four labels: benign, injection point, hijacked, or failed injection. The corpus contains 4,000 benign, 5,536 attacked, 1,500 failed-attack, and 1,500 hard-negative trajectories; attacked trajectories follow three compliance patterns whose label strings obey a stated regular grammar. Failed attacks carry an injection the agent resisted, and hard negatives carry legitimate content that resembles an attack, so a detector must separate attempt from success and deviation from novelty. Trajectories were generated by a single open model under category-specific protocols, enforced by a closed-vocabulary structural validator, screened by an LLM judge, and audited by hand on 1,200 trajectories; we show that the LLM judge was itself fooled by the hard negatives. A surface-feature logistic regression recovers only 55.4% of attacks (F1 0.647), including only 8.2% of partial hijacks and 23.1% of delayed executions, so nearly half of the attacks require modeling the behavioral sequence. We measure template concentration, attack-goal-family collapse, and world-identity leakage in the generated data, and release the corpus with its documentation under CC BY 4.0.

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arXiv2026

Lightweight Detection of Electromagnetic Signal Injection Attacks on Image Sensors

Electromagnetic signal injection attacks (ESIA) pose a growing threat to image sensors, which are increasingly used in different intelligent systems. By emitting electromagnetic interference, adversaries can manipulate pixel values, potentially misleading downstream artificial intelligence (AI) models and causing unsafe decisions in these systems. We present a lightweight detection method that leverages optically black pixels, which are non-exposed pixels already present in many modern image sensors, to identify the attacks. Our detection approach achieves an area under the receiver operating characteristic curve (ROC-AUC) of up to 99.6\% and an Equal Error Rate (EER) as low as 0.027 across diverse attack conditions. Our method requires minimal computational overhead and no hardware modifications, making it a practical and effective defense for securing vision-based systems against ESIA.

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arXiv2026

CIPHER: Benchmarking Cross-record Inference over Privacy-Hardened Evidence Records

Reasoning over privacy-constrained records requires combining structured attributes with evidence from free-text narratives. We introduce CIPHER (Cross-record Inference over Privacy-Hardened Evidence Records), a benchmark of expert-validated questions from consumer-finance, clinical, and law-enforcement records. The questions cover common tabular operations and include executable SQL supervision. We evaluate retrieval, prompting, table-specialist, and hybrid symbolic-neural systems under native redaction and surrogate-based evidence restoration. All system families exhibit substantial failures even when supporting records are provided. Most errors arise from incorrect record selection and predicate interpretation rather than arithmetic execution. Privacy transformations have non-uniform effects, sometimes obscuring necessary evidence and sometimes reducing distraction. CIPHER provides a reproducible testbed for diagnosing these failures and assessing how transformations of sensitive text affect reasoning over hybrid records.

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arXiv2026

TrojanWorld: Backdooring World-Model Agents via Imagination Steering

World models increasingly serve as the predictive core of model-based reinforcement learning agents, enabling them to simulate future dynamics and reason over imagined trajectories before acting. Their substantial training demands make pretrained world models attractive for distribution and reuse, exposing downstream systems to model supply chain threats. Backdoor attacks offer a targeted and stealthy means of exploiting such supply chains, yet their threat to interactive world-model agents remains largely unexplored. To fill this gap, we present TrojanWorld, a backdoor framework for world-model agents that induces attacker-specified behavior by steering internal imagination. A physical object placed in the scene acts as the trigger, enabling deployment-time activation through the agent's native observation pipeline without digitally manipulating the observation stream. To achieve effective, stealthy, and persistent control, TrojanWorld combines Decision-Reflective Induction to steer trigger-conditioned imagination toward attacker-specified actions using decision feedback, Clean Behavior Anchoring to preserve trigger-free predictive and behavioral fidelity, and Causal Propagation to sustain the induced preference along subsequent trajectories after the trigger disappears. Together, these mechanisms establish an end-to-end attack chain from physical perception through corrupted imagination to malicious action selection. Experiments with the TD-MPC2, DreamerV3, and R2-Dreamer systems across the DeepMind Control, MetaWorld, MyoSuite, and RoboDesk benchmarks show that under trigger activation, TrojanWorld achieves a target-action deviation as low as 0.026 while retaining at least 98.8% of the corresponding clean performance. Even after trigger removal, the compromised agent can remain trapped in the induced behavioral trajectory, continuing to execute attacker-specified actions.

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arXiv2026

AgentLeak: Cloning Stronger LLM Agent Capabilities onto Weaker Agents Beyond Skill Stealing

Large language model (LLM) agents increasingly achieve long-horizon tasks by combining foundation models with explicit skills and implicit procedural knowledge acquired through execution. The resulting task-solving capabilities have become valuable proprietary assets, raising a new security question: can a substantially weaker attacker-controlled agent acquire the capabilities of a stronger proprietary agent through limited black-box interaction? Existing skill-stealing attacks recover explicit skill artifacts, yet we show that artifact leakage does not necessarily transfer capability: a weaker agent may possess the same skills but still fail because it lacks procedural behaviors implicitly realized by the stronger agent. Our key insight is that the skill execution gap itself forms a leakage surface, where missing behaviors are exposed through observable differences between successful victim executions and failed attacker executions. Based on this, we present AgentLeak, a black-box capability-cloning attack that identifies capability-critical behaviors from these execution differences and incorporates them into attacker-side skills, while keeping the attacker's model, harness, and tools unchanged. Across 20 task scenarios comprising 600 instances, diverse agent systems, and multiple backbone models, AgentLeak improves task pass rates by over 40% compared with direct skill reuse and recovers more than 80% of the victim--attacker capability gap. Our findings reveal a confidentiality risk in LLM agents: protecting explicit artifacts alone is insufficient, as observable execution behavior can leak the procedural knowledge required to reconstruct proprietary task-solving capabilities in low-capability and attacker-controlled agents.

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arXiv2026

Frequency-Domain Mixing Data Augmentation for Malicious Traffic Detection

The strong dynamics of network traffic often force malicious traffic detection models to handle out-of-distribution data. Typically, deep learning-based malicious traffic detection models require a large amount of high-quality training data. However, owing to challenges such as high labeling difficulty and resource consumption, existing datasets often suffer from insufficient diversity and fail to capture evolving traffic patterns, leading to poor out-of-distribution generalization ability of the trained models. Data augmentation has been widely adopted to improve data diversity and model generalization. Recently, frequency-domain mixing augmentation has shown promising performance because it effectively perturbs data while preserving key structural information. This approach shows potential for enhancing malicious traffic detection models. However, existing studies lack theoretical interpretation of the mixing mechanism, and do not adapt to the characteristics of network traffic. In this paper, we first conduct a theoretical analysis of the current frequency-domain mixing method, revealing its underlying principles and limitations. We further propose an improved frequency-domain mixing-based data augmentation method for network traffic data, which enhances the diversity of sequence features in network traffic and improves the out-of-distribution generalization of malicious traffic detection models. Extensive experiments on multiple artificial and real-world datasets demonstrate that our method substantially improves detection performance across diverse network environments and outperforms other data augmentation approaches.

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arXiv2026

The Oversight Gap: What LLM Safety Monitors Miss, and Why It Is Not Capability

Several properties safety monitors are asked to certify, among them cross-tenant noninterference, sandbagging and evaluation awareness, are 2-safety hyperproperties, witnessed only by two executions. The standard consequence is a binary impossibility: one trace cannot decide them. We replace the binary with a measurement. A tight bound puts the balanced accuracy of any single-trace monitor at $\tfrac12+\tfrac12\,TV(P_0,P_1)$, turning undecidability into a graded detectability frontier and defining an oversight gap: a monitor's shortfall below it. On a leak family with closed-form $TV$, nine LLM monitors are optimal at $TV=0$ but capture little signal as $TV$ grows; at $TV=1$, where a 20-line membership check scores $100\%$, they average $60.9\%$. That shortfall is mostly not capability: naming what to check closes $61\%$ of it while leaving the $TV=0$ control at chance. The same split runs through a $2{\times}2$ factorial: an imagined second run leaves monitors at chance ($50.4\%$) while the same rule on an executed second run reaches $90.0\%$, and a stored oracle without a comparison procedure yields only $68.2\%$. Information and procedure are each necessary and neither is capability. Under nondeterminism, replay tracks a closed-form $k$-replay curve only under the right projection, and a projection frontier shows the resulting dilemma is forced: narrow misses $98.6\%$ of off-channel leaks, broad flags $75.7\%$ of clean traffic, and attainable accuracy decays like $1/(qm)$ in the benign-variation rate and the channel count. Finally, two frontier LLM judges certified an earlier version of our own benchmark as sound while a sign test found a directional bias ($p=2.7\times10^{-5}$) that invalidated three of our findings. Construction validity for hyperproperty benchmarks should be proved mechanically, not audited by models.

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arXiv2026

Enhancing Privacy, Neglecting Harms: An Analysis of Real-World Digital Privacy Incidents

Privacy-enhancing technologies (PETs) have emerged as a technical means for providing individuals with greater control over their information. Yet despite the growing deployment of PETs, people continue to experience privacy harms. In this work, we revisit our understanding of privacy incidents and the realities of those experiencing privacy harms, to assess whether the goals and abilities of PETs are misaligned with the harms people face. For our study, we collect news articles that correspond to a sample of 257 real-world privacy incidents. We employ content analysis over the articles to develop a new information flow model that encompasses the complexity of data flows and their relation to resulting harms. We demonstrate that our model captures both established and novel aspects of privacy incidents and their mitigations. In particular, it captures why consent is often insufficient to prevent privacy violations, how harms emerge from complex interactions among multiple entities and actions, and reveals a flaw in our understanding of PETs: a focus on enabling functionalities still permits the harms inherent in those functionalities. Moreover, we find that the entities best positioned to implement harm-preventing measures for the incidents in our sample are the least incentivized to do so. Overall, our model and analysis identify limitations of privacy technology research for harm prevention and further identifies paths for transforming how we approach the advancement of these technologies.

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arXiv2026

Towards a Resilience-Theoretic Foundation for Adversarial Robustness in Industrial Control System Anomaly Detection

Anomaly-based intrusion detection systems in industrial control systems (ICS) and operational technology (OT) environments are increasingly required to meet formal resilience criteria: absorbed adversarial disturbances, graceful degradation under sustained attack, and certified system-level guarantees. Existing resilience frameworks for cyber-physical systems define absorb-recover-adapt trajectories at the architectural level but do not treat machine learning anomaly detectors as first-class components, leaving a gap between component-level robustness evaluation and system-level resilience certification. In this paper, we establish that adversarial robustness in ICS anomaly detection is a specific instantiation of system resilience, and formalise this connection by mapping four resilience constructs, i.e. disturbance class, absorption capacity, recovery trajectory, and degradation function, onto the adversarial machine learning setting. We derive a compositional resilience bound for heterogeneous ICS detection networks, showing that the binding constraint on system-level resilience is the coupling-adjusted absorption capacity of each node along the attack path, not the per-node capacity -- so the binding node need not be the weakest one. Empirical validation on the BATADAL water distribution system benchmark demonstrates that the resulting metrics surface operationally significant phenomena invisible to standard benchmarks: the absorption-degradation divergence under adversarial training, and the paradox that hardening the binding node in isolation reduces system-level resilience. Implications for ICS architecture design and certification standards are discussed.

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arXiv2026

Merging Cyber Threat Intelligence Through Retrieval-Augmented Generation and Small Language Models for Rich Threat Representation

Modern cybersecurity operations rely on CTI collected from heterogeneous sources, including semi-structured threat representations, IoCs, and narrative technical reports. However, these artifacts are often insufficient in isolation to reconstruct how an attack unfolds, under which conditions each step is feasible, and which traces it leaves behind. In practice, analysts must manually correlate partial evidence scattered across multiple and only partially structured sources, delaying the design of effective prevention, detection, and response actions. To address this gap, we propose an automated pipeline that derives an actionable representation of a cyberattack from heterogeneous CTI sources. The pipeline combines a RAG architecture with a locally deployable SLM, used to consolidate such evidence and infer missing operational details. Starting from a semi-structured threat representation and auxiliary CTI documents, the pipeline produces an enriched Attack Graph that captures a coarse, tactic-aligned progression of the attack and annotates each step with explicit pre-conditions and post-conditions, and an enriched description. This representation supports prevention by exposing execution requirements, detection by highlighting observable traces, and response by clarifying the temporal progression of the attack. Then, due to the lack of validated datasets with ground-truth information on the temporal evolution of real-world attacks, we test the complete pipeline on 10 real-world case studies spanning multiple threat types, including backdoors and staged downloaders delivered via phishing. A manual assessment across 10 real-world case studies provides initial evidence that the generated graphs are consistent with expected attack progressions, indicating that the proposed approach can support analysts by consolidating dispersed CTI evidence into a structured and actionable view of attacks.

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arXiv2026

Robust Decentralized Personalized Federated Learning via Prediction-Constrained Neighborhood Collaboration

This paper proposes a robust decentralized personalized federated learning method R-DPFL, that enables clients to reduce the impact of Byzantine attacks via robust neighborhood direction estimation and history-based update trend prediction, rather than purely aggregating client models as in the existing work. In R-DPFL, each client first computes the current-round model update by aggregating the received neighborhood update vectors. It then predicts what this update should be based on its historical values and local model changes. Finally, R-DPFL computes the difference between these two quantities, adaptively clips this difference, and adds it to the local update. We prove convergence of the learning process through rigorous analysis and show that honest clients maintain stable personalized descent dynamics under Byzantine neighbor perturbations without requiring consensus among neighboring models. Extensive experiments on CIFAR-10 demonstrate that RDPFL consistently outperforms state-of-the-art decentralized and personalized federated learning baselines under heterogeneous and adversarial settings.

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arXiv2026

Construction and Natural Language Querying of a Cybersecurity Knowledge Graph

Cybersecurity vulnerability information is distributed across numerous platforms and databases, making it difficult for researchers and practitioners to obtain a unified and structured understanding of existing threats. This is a critical issue in cybersecurity, where timely access to accurate vulnerability information directly impacts risk assessment and decision-making. While previous work has shown that knowledge graphs are effective for organizing vulnerability data, a major research gap remains in their accessibility, as querying such graphs typically requires expertise in graph query languages like Cypher. This paper aims to address this gap by proposing an approach that combines the construction of a cybersecurity knowledge graph with natural language-based interrogation. The proposed methodology relies on data collected from the National Vulnerability Database (NVD)(1) through its REST API and models vulnerabilities, products, vendors, severity metrics, weaknesses, and references using the Labeled Property Graph paradigm in Neo4j. The knowledge graph is deployed on Neo4j Aura Cloud and queried through an AI-assisted interface that translates natural language queries into Cypher language. The key contribution of this work is demonstrating that natural language querying significantly lowers the barrier to interacting with cybersecurity knowledge graphs, enabling more intuitive exploration and analysis of vulnerability data, and thereby enhancing their practical usefulness for a broader range of users in the cybersecurity field.

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arXiv2026

Attestream: Usage-Aware Intermittent Data Distribution with Verifiable Lifecycle Provenance for Machine-Learning Data Streams

Providers of continuously produced, commercially valuable data -- sensor streams, telemetry, and other feeds sold as machine-learning training material -- cannot observe whether delivered data is actually used, while data that keeps flowing to inactive consumers enlarges the leakage surface without producing value. We present Attestream, a blockchain-based architecture for intermittently delivered dataset streams that couples continued delivery to verifiable usage reporting. Every lifecycle event -- dataset preparation, dual-signed delivery, derivative creation (e.g., a model), and derivative distribution -- is appended to an on-chain registry as a non-repudiable, mutually linked lifecycle record. The mechanism requires provable transfer, not tokenization: plain contract storage, ERC-721 tokens, and anchored off-chain receipts are interchangeable representations of the same protocol. A usage-aware gate suspends a consumer's stream when no derivative-creation record is registered within a reporting window; evaluated lazily inside the next delivery transaction, monitoring adds no dedicated transactions. A modality-pluggable fingerprinting layer binds any leaked copy to the dual-signed delivery record of the responsible consumer, instantiated for tabular/geospatial records, images, and documents. We implement the registry as a Solidity contract with EIP-712 dual signatures and evaluate it: a full lifecycle round costs 657k gas with plain records ($0.13 on rollups; ERC-721 tokenization adds ~30k gas per record), and over a 50-consumer pool leak attribution reaches 100% from 40 leaked table rows under moderate noise, survives JPEG recompression to quality 30, and tolerates paraphrase rates up to 30% for documents.

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arXiv2026

SoK: Secure Software-Based Multi-Domain Data Segregation

Modern mission-critical coordination demands seamless communication across multiple domains. Traditionally, Voice Communication Systems (VCS) have relied on physically separated Red/Black architectures to ensure voice and data segregation. While these hardware-based methods provide strong security assurances and remain foundational in high-security contexts, they can introduce significant complexity and scalability challenges as mission parameters expand into highly dynamic, multi-domain integrations. As defence, emergency response, and critical infrastructure operations increasingly require interoperable, flexible, and cost-efficient communication environments, there is a growing need to understand whether software-based approaches can provide comparable assurance while supporting modern operational requirements. This SoK characterises a transition to software-based Multi-Domain Data Segregation (MDDS) by integrating systems security, networking, and cryptography. Its goal is to consolidate existing research, identify shared architectural patterns, and address the security challenges facing next-generation high-assurance software-based VCS architectures. By assessing software-defined and virtualized approaches, this SoK supports the development of scalable, high-assurance VCS architectures that facilitate secure real-time coordination across diverse operational domains. Specifically, this SoK examines the security implications of Software-Defined Networking, Network Slicing, Separation Kernels, and Cross-Domain Solutions while addressing challenges posed by quantum computing through Post-Quantum Cryptography (PQC). This SoK provides a comprehensive analysis of the shift from hardware isolation to software segregation, serving as a crucial foundation for researchers and industry stakeholders aiming to enhance secure and adaptable VCS infrastructures for mission-critical operations.

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