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◬ AI & Machine Learning Jun 08, 2026
DiBS: Diffusion-Informed Branch Selection

arXiv:2606.06518v1 Announce Type: new Abstract: Sudoku is a representative constraint satisfaction problem that requires global structural reasoning under strict discrete constraints. The existing wor…

arXiv AI Read →
◬ AI & Machine Learning Jun 08, 2026
Detecting and Mitigating Bias by Treating Fairness as a Symmetry Operation

arXiv:2606.06514v1 Announce Type: new Abstract: Machine learning systems deployed in high stakes socioeconomic settings routinely display bias. We formalize bias as a symmetry breaking operation: a cl…

arXiv AI Read →
◬ AI & Machine Learning Jun 08, 2026
An Expanded Synthetic Conversation Dataset for Multi-Turn Smishing Detection

arXiv:2606.06879v1 Announce Type: cross Abstract: Our prior work introduced COVA, a synthetically generated multi-turn conversational smishing dataset of 3,201 labeled conversations, establishing base…

arXiv Security Read →
◬ AI & Machine Learning Jun 08, 2026
Unified Safe In-context Image Generation in Multimodal Diffusion Transformers via Restricting Unsafe Information Flows

arXiv:2606.06875v1 Announce Type: cross Abstract: Diffusion transformers (DiTs) equipped with multimodal attention (MM-Attn) have become a dominant paradigm for image generation. However, preventing t…

arXiv Security Read →
◬ AI & Machine Learning Jun 08, 2026
Hearing the Unspoken: Language Model Priors for Acoustic Adversarial Attacks

arXiv:2606.06833v1 Announce Type: cross Abstract: Automatic Speech Recognition (ASR) systems operating in real-time settings must process acoustic input under strict temporal constraints, where transc…

arXiv Security Read →
◬ AI & Machine Learning Jun 08, 2026
The Economics of Proof-of-Useful-Work

arXiv:2606.06700v1 Announce Type: cross Abstract: Proof-of-work (PoW) blockchains rely on computational expenditure to secure a ledger supporting a native cryptocurrency. In existing systems such as B…

arXiv Security Read →
◬ AI & Machine Learning Jun 08, 2026
Beyond the Canonical Protocol: Quantum Encrypted Cloning from Secret-Sharing Access Structures

arXiv:2606.06552v1 Announce Type: cross Abstract: Quantum encrypted cloning shows that an unknown quantum state can be distributed into multiple encrypted copies without contradicting the no-cloning t…

arXiv Security Read →
◬ AI & Machine Learning Jun 08, 2026
Online Safety Regulation Increases Privacy Risk: Evidence from the UK Online Safety Act

arXiv:2606.05273v1 Announce Type: cross Abstract: Governments worldwide are increasingly regulating digital platforms to reduce online harms, particularly those affecting children. However, access res…

arXiv Security Read →
◬ AI & Machine Learning Jun 08, 2026
Verifiable and Confidential DNN Inference on Low-End Edge Devices

arXiv:2606.07470v1 Announce Type: new Abstract: Deploying deep neural network (DNN) inference on low-end edge devices raises two key challenges: protecting model confidentiality against a potentially …

arXiv Security Read →
◬ AI & Machine Learning Jun 08, 2026
Lost in Migration: Exposing Android Framework Vulnerabilities in Parallel Java-Kotlin Implementations

arXiv:2606.07420v1 Announce Type: new Abstract: Android has adopted Kotlin alongside Java across apps and core system components. During this shift, we observe parallel implementations in the Android …

arXiv Security Read →
◬ AI & Machine Learning Jun 08, 2026
On the Shoulders of Giants: Empowering Automated Smart Contract Auditing via the GiAnt Corpus

arXiv:2606.07363v1 Announce Type: new Abstract: High-quality smart contract auditing datasets are crucial for evaluating security tools and advancing smart contract security research. Two major limita…

arXiv Security Read →
◬ AI & Machine Learning Jun 08, 2026
Empirical Evaluation of Large Language Models for Migration of Code Fragments to Post-Quantum Cryptography

arXiv:2606.07341v1 Announce Type: new Abstract: The transition to post-quantum cryptography (PQC) requires not only replacing vulnerable cryptographic primitives, but also refactoring the surrounding …

arXiv Security Read →
◬ AI & Machine Learning Jun 08, 2026
Defending Jailbreak Attacks on Large Language Models via Manifold Trajectory Kinetics

arXiv:2606.07335v1 Announce Type: new Abstract: Jailbreak prompts can bypass alignment guardrails in large language models (LLMs) and elicit unsafe outputs, making reliable deployment-time detection c…

arXiv Security Read →
◬ AI & Machine Learning Jun 08, 2026
Authorized and Verifiable Searchable Encryption Based on Public Key Equality Test for Cloud Storage

arXiv:2606.07319v1 Announce Type: new Abstract: Cloud storage revolutionizes data management but raises conflicts between functionality and privacy. Public Key Encryption with Equality Test (PKEET), a…

arXiv Security Read →
◬ AI & Machine Learning Jun 08, 2026
Rethinking IoT Intrusion Detection: Augmenting Routing Metrics with Radio Features

arXiv:2606.07282v1 Announce Type: new Abstract: Machine learning-based intrusion detection systems (IDS) for RPL-based IoT networks often rely solely on routing layer features, which provide only a pa…

arXiv Security Read →
◬ AI & Machine Learning Jun 08, 2026
Synthetic APTs: the Collapse of TTP-Based Attribution

arXiv:2606.07158v1 Announce Type: new Abstract: Cyber Threat Intelligence CTI attribution relies on identifying the Tactics, Techniques, and Procedures TTPs that distinguish one threat actor from anot…

arXiv Security Read →
◬ AI & Machine Learning Jun 08, 2026
From Privacy to Workflow Integrity: Communication-Graph Metadata in Autonomous Agent Interoperability

arXiv:2606.07150v1 Announce Type: new Abstract: Agent-interoperability protocols such as A2A and MCP standardize what agents say to one another, but assume address-based transport over HTTP(S). Such t…

arXiv Security Read →
◬ AI & Machine Learning Jun 08, 2026
MalSkillBench: A Runtime-Verified Benchmark of Malicious Agent Skills

arXiv:2606.07131v1 Announce Type: new Abstract: AI coding agents such as Claude Code and Gemini CLI increasingly extend themselves with third-party skills: markdown packages bundling natural-language …

arXiv Security Read →
◬ AI & Machine Learning Jun 08, 2026
Fast Bounded-Independence Functions and Their Duals

arXiv:2606.07009v1 Announce Type: new Abstract: We continue the study of {\em fast} functions, computable by linear-size circuits, that share useful properties of random functions. Motivated by crypto…

arXiv Security Read →
◬ AI & Machine Learning Jun 08, 2026
The Sound of Malware: A Memory Forensics Approach for Android Malware Analysis via Audio Signals

arXiv:2606.07005v1 Announce Type: new Abstract: Android malware analysis is currently facing increasing challenges in achieving robust classification and detecting stealth attacks. Modern threats empl…

arXiv Security Read →
◬ AI & Machine Learning Jun 08, 2026
HAVE: Host Active Verification Engine for Closing the Contextual Reality Gap in Security Digital Twins

arXiv:2606.06968v1 Announce Type: new Abstract: Security Digital Twins (SDTs) provide continuously updated virtual replicas of infrastructure for threat simulation, yet they rely on theoretical CVSS s…

arXiv Security Read →
◬ AI & Machine Learning Jun 08, 2026
DPAgent-in-the-Middle: Agentic Defense and Repair Against AI-Groomed Deceptive Patterns

arXiv:2606.06914v1 Announce Type: new Abstract: Privacy deceptive patterns in web interfaces systematically manipulate users into disclosing personal data, yet existing defenses are fragmented, static…

arXiv Security Read →
◬ AI & Machine Learning Jun 08, 2026
Blockchain Infrastructure for Intelligent Cyber--Physical--Social Systems:Post-Quantum Security, Interoperability, and Trustworthy Data Economies in the Era of Embodied AI

arXiv:2606.06895v1 Announce Type: new Abstract: The deployment of embodied artificial intelligence via world-model-based robotics presents a transformative opportunity for blockchain infrastructure, e…

arXiv Security Read →
◬ AI & Machine Learning Jun 08, 2026
FDM: A Framework for Decision-making to build ML-based Malware detection systems

arXiv:2606.06894v1 Announce Type: new Abstract: Selecting appropriate machine learning (ML) configurations for malware detection is a complex, multi-criteria problem. Model choice, feature engineering…

arXiv Security Read →
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