Security & Cryptography · Computer Science & AI
Cryptography & Security
The newest papers in Cryptography & Security that ReadSearch has read, newest first. Each links to its own page, with its authors, date, where it was published and the original.
1,372 papers, from 4 September 2026 to 5 November 2026: 1,372 arXiv preprints.
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Newest papers, page 41
Walking the Embedding Space: Datastore Extraction from Multimodal RAG
Multimodal Retrieval-Augmented Generation (MRAG) has emerged as a reliable and cost-effective technique of grounding the generative capabilities of Multimodal Large Language Models (MLLMs) into…From Network Intrusion Detection to Blockchain-Backed Endpoint Detection and Response: Mapping the Landscape of Decentralized Detection-and-Response Architectures
While the literature on blockchain-assisted intrusion detection and prevention systems (IDS/IPS) for Internet of Things (IoT) and Industrial Internet of Things (IIoT) networks is mature, existing…A Structured State Space Sequence Model for Multi-Class Classification of Malware
By 2030, Internet of Things (IoT) devices are projected to reach 40 billion, with fast-paced technological advancements in fields such as industry, healthcare, agriculture, automobiles, and…A Hybrid Approach to Malware Detection: Integrating Few-Shot Model-Agnostic Meta-Learning with Autoencoders
Ransomware has emerged as a major cybersecurity threat, with incidents increasing in frequency and impact across critical sectors.Can AI Oversight Be Zero Knowledge?
AI systems increasingly produce outputs from confidential data, such as a fitness-for-duty assessment from medical records or the predicted properties of a drug candidate from its secret structure.Homomorphic Advantage Operator: Stabilizing Reinforcement Learning Under Fully Homomorphic Encryption Constraints
Privacy-preserving machine learning presents significant deployment challenges on the cloud for intelligent systems with confidential data.On the pseudorandomness of simple quantum processes
Can simple processes appear highly complex?Lower Bounds for Preprocessing Attacks on Quantum Cryptography
We prove near-optimal lower bounds for preprocessing attacks on quantum cryptography in the random oracle model.Quantum Advantage for Two-Party Differential Privacy
We introduce information-theoretically private quantum protocols for two-party Hamming distance when both parties must output the same estimate.KaliBench: A Fine-Grained Benchmark for Cybersecurity Tool Use on Kali Linux with Runtime-Free Verifiable Rewards
LLMs are increasingly applied to cybersecurity workflows, where they are expected to translate analysts' intent into tool invocations.Fast Models, Slow Evidence: A Paired and Self-Audited Evaluation of System-1 Decision Models for LLM Agent Harnesses
Agent harnesses make many small, typed decisions per task: which model to call, which tool to use, whether retrieved text is relevant, whether an input carries an injection.From Mathematical to Executable Certificates for Machine Unlearning
Machine unlearning is needed when data must be removed because of deletion requests, outdated records, or data-quality concerns, while retraining from scratch can be costly.Intent-Hiding Jailbreaks: An Information-Theoretic Framework for Compositional Attacks
Recent work has shown that large language models (LLMs) can be vulnerable to jailbreak attacks in which harmful intent is obscured through composition with benign tasks.MIRROR: Multipath Quorum Integrity for LLM Multi-Agent Communication
Inter-agent communication is central to Large Language Model Multi-Agent Systems (LLM-MAS), but it introduces an underexplored vulnerability: Agent-in-the-Middle (AiTM) attacks that manipulate…Hop-Decayed Influence: New Vulnerabilities of Structural Auxiliary Indexing in GraphRAG Pipelines with LLM
GraphRAG pipelines construct auxiliary structures during offline indexing--semantic summaries, hierarchical edges, and pre-computed scores--that determine how retrieval is prioritised at query time.Mitigating Private Data Leakage in LLMs with Whiteout
Modern large language models (LLMs) are trained on massive, largely unfiltered datasets, including content scraped from nearly every accessible website and user inputs.Evaluating and Improving the Robustness of Large Language Models to Input Sequence Variations
Large language models (LLMs) in production systems face prompt injections, trojans (backdoors), and manipulation of automatic quality metrics.SideKernel: A Usable microVM Sandbox for AI Coding Agents on macOS
AI coding agents are untrusted system components, yet they require autonomy on the developer machines they run on.Out of Sync, Out of Sight: Phantom State Attacks against IIoT Intrusion Detection
Machine learning-based intrusion detection systems (IDS) are critical for securing Industrial Internet of Things (IIoT) environments.Pincer: Resource Authorization for Agents using a Digital Twin
Coding agents have become increasingly long-horizon, autonomous, reliant on general-purpose shell and maintain their own persistent memory for self-improvement.