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 46
Janus: Evidence-Before-Effect Sagas and Offline-Verifiable Provenance for Agentic LLMs
Agentic large language models (LLMs) now move money through tools, yet the record of what they did is usually a trace their own process emits beside the effect.VirusCascade: Hijacking Collaborative Reflection in LLM-Powered Recommender Agents
Advancing beyond traditional static scoring models, LLM-powered agentic recommender systems (LLM-ARS) instantiate users and items as autonomous agents, whose semantic states are dynamically refined…Privacy in Personalized AI Is a System Property, Not Just a Model Property
In personalized AI applications, such as conversational assistants and recommender systems, users interact not with models in isolation but with broader systems that access, infer, and reuse user…HARDE: Optimizing Agent Harnesses for Runtime Risk Detection and Execution Control
Large language model (LLM) agents are vulnerable to safety risks such as injected malicious instructions or misleading information, motivating runtime defenses that prevent unsafe action in…Aegis: Generative Gradient Masking for Privacy-Preserving Medical Federated Learning
Federated learning (FL) has become a foundational paradigm for multi-institutional medical AI, allowing hospitals and research centers to jointly train diagnostic models without exchanging patient…LogiC-Diff: Embedding Security Properties Into AI-Enabled Cyber-Physical Systems
AI-enabled Cyber-Physical Systems (CPS) are highly vulnerable to adversarial and anomalous inputs, where small perturbations can induce cascading errors and unsafe control actions.On Removing Interaction from Quantum Proofs
An important open question in quantum cryptography is the construction of publicly-verifiable NIZKs for QMA.The Geometry of Harmfulness in Multi-Turn Attacks
Large language models (LLMs) remain vulnerable to adversarial attacks that circumvent safety alignment to elicit harmful outputs.A Competing-Hazards Systematization of Loss of Control in Autonomous Agents
Leading AI developers have reported agents acting beyond their approved limits, which a United Nations panel described as an early warning of loss of human control.Evaluating Whether GPT-6 Astra Performs Unsanctioned Supply-Chain Attacks
This technical report presents an alignment evaluation developed and performed by the UK AI Security Institute for assessing whether advanced AI systems take unsanctioned actions outside the scope…Security-Enhanced Seed-Based Weight Quantization for Large Language Models
Large language models (LLMs) incur substantial storage, memory-bandwidth and energy costs, motivating compact weight representations.SecureVibe: Making Vibe Coding More Secure
As vibe coding becomes increasingly capable and widespread, security vulnerabilities in even functionally correct solutions are a growing concern.Alignment via Training Against Probes Without Losing Monitorability
Models are usually aligned based on their observed outputs, using demonstrations, preference data, or reward signals.Refusal Localizes, the Damage Relocates: Safety Layers Under Few-Sample Fine-Tuning
Fine-tuning adapts aligned large language models (LLMs) to downstream tasks, but a few dozen harmful examples can remove their refusal of harmful requests.Actions with Receipts: Jointly Binding Claims, Evidence, and Execution for Replayable Tool-Agent Auditing
Tool-using agents can expose citations and execution logs while leaving a critical association unaudited: whether the claim shown to a user is the claim emitted by the committed execution and…Authorization for Self-Modifying AI Agent Populations: Conserving Authority across Replacement, Forking, and Rollback
Self-modifying AI agents can replace, fork, and roll back identity-bearing software while descendants remain executable.Removing the NEEDLE in the Haystack: Backdoor Removal in LLMs via Weight Orthogonalisation
Backdoor attacks can be implanted in Large Language Models (LLMs) during training, causing unwanted behaviour when a trigger appears in the input.Fault-Tolerant Budget Conservation in Distributed Multi-Agent Delegation
Resource limits are becoming an authorization boundary for AI agents that delegate work across concurrent and failure-prone workers.Faultless: A Program Equivalence Technique for Validating and Evaluating Neural Decompilers
Neural decompilers are machine learning models which perform the process of decompilation, lifting code from a lower-level language to a higher one.Certified Multi-Source Integrity for Structured Agent Actions
LLM agents increasingly take privileged, often irreversible structured actions, such as paying an invoice.