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 66
DRIFT: Removing Diffusion Watermarks by Deflecting the Generative Trajectory
Diffusion watermarking embeds verifiable signals into the generative process and commonly verifies them by recovering trajectory-dependent evidence, making the marks robust to conventional…ACEA: An Adversarial Co-Evolution Arena for Head-to-Head Red-Team and Blue-Team LLM Testing
Automated red-team attacks and blue-team defenses for large language models (LLMs) are advancing quickly.Revoked but Still Authoritative: An Empirical Study of Revocation Enforcement in Agent-Memory Systems
Long-running language-model agents depend on persistent memory.HoneyRoute: Honeypot-Model Routing for Adversarial LLM Serving
We introduce HoneyRoute, an inference-serving layer that detects whether an incoming request is malicious and, if so, routes it to a dedicated honeypot model, shielding production while the…Do Input-Level Defenses Transfer to Observation-Level Attacks on VideoLLMs?
Video Large Language Models (VideoLLMs) are increasingly deployed in safety-critical applications such as content moderation and video analytics.VeriScene: Reconstructing Crime Scenes from Legal Evidence via World-Model Agent
World models take multimodal inputs like text, photos, and diagrams to generate dynamic scenes in accordance with the laws of physics, thus opening a compelling application: fusing multimodal legal…Structural Jailbreaks Generalize but Do Not Compound: A cross-provider and multilingual study of Involuntary In-Context Learning
Aligned language models fail under two independent pressures: the structural jailbreak class recently formalized as Involuntary In-Context Learning (IICL), which reframes a harmful request as the…Topological Fraud Detection in Latent Transaction Spaces
Working entirely on topologically anonymized embeddings, we perform fraud detection using iterative rounds of unsupervised filtering followed by supervised sniping.When Topology Betrays Privacy: Lattice-Based Reconstruction Attacks on Secure Aggregation in Decentralized Federated Learning
Secure Aggregation (SA) is widely regarded as a strong defense against model-update leakage in Federated Learning (FL), as it reveals only aggregate results while hiding individual updates.ZK-Trace: Certified Collusion Tracing with Zero-Knowledge Credentials for Federated GNSS Interference Monitoring
Federated global navigation satellite system (GNSS) monitoring distributes a proprietary classifier to partly trusted stations, any of which may leak its copy.Benchmark Scores Are Pipeline-Dependent: A Reliability Audit of Cybersecurity LLM Benchmarks
Large language model (LLM) benchmarks are often treated as fixed datasets with stable scores, yet their outcomes depend on configurable evaluation pipelines.Evidence-Grounded Retrieval for Investigation Hunt Lead Generation from CTI Reports
Threat hunting increasingly depends on converting unstructured knowledge (e.g., Cyber Threat Intelligence reports) into actionable hunt leads: concise, investigable hypotheses grounded in observable…In RAG We Trust? Measuring Robustness of Retrieval-Augmented Generation Under Document Poisoning
Retrieval-augmented generation (RAG) grounds a language model in retrieved documents, which reduces hallucination but creates a new attack surface: if retrieved text is tampered with, the model may…Encrypt What Matters: When Selective Homomorphic Inference Is Efficient
Fully homomorphic encryption (FHE) enables inference on private data without revealing it to the server, but evaluating an entire input under FHE is expensive.X-amine509: Predicting the Practical Risk Level of Enterprise X.509 Certificates
Enterprises managing large X.509 certificate inventories face a prioritization problem: deterministic analysis tools that precisely identify standards violations are indispensable for remediation…An Experimental Evaluation of Multimodal Prompt Injection Attacks on Agentic AI Frameworks
Agentic AI frameworks let a language model plan, keep memory, and call tools that reach real files, mail, and services.Concept drift mitigation through community and spectral graph analysis for the detectionof cyberattacks in network traffic
In network traffic, legitimate behaviours and attack techniques evolve jointly - the phenomenon known as 'concept drift' [1].Adaptive Distributed Physical-Layer Authentication and Attack Detection in 6G Non-Terrestrial Networks via Causal Meta-Learning
Physical-layer authentication (PLA) in non-terrestrial networks (NTNs) is challenged by severe Doppler shifts, long delays, and fast channel variations, which cause distribution shifts and degrade…Differentially Private Average Treatment Effect Estimation by Propensity Score Blocking
Average treatment effect (ATE) estimation in observational studies is a fundamental statistical tool used frequently in social science, medicine, and other fields.Adaptive Diffusion Freezing: Privacy-preserving Diffusion Models Against Membership Inference Attacks
Diffusion models have achieved remarkable success in generative tasks across various areas, however their training process raises significant privacy concerns, particularly under membership…