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 38
Guess My Weight: Profiled Side-Channel Recovery of Floating-Point Neural-Network Weights
Neural-network parameters deployed on embedded devices may be exposed through physical side-channel leakage during inference.Reactivating Alignment: Defending LLMs from Jailbreaks via Intention-Aware Input-Output Matching
Large language models (LLMs) remain vulnerable to jailbreak attacks that conceal harmful intent within complex adversarial prompts.The Same Zero: Why Identical ASR Can Imply Different Guarantees in LLM-Agent Security
LLM-agent security has produced a dense landscape of defenses - prompt hardening, content filters, permission gates, sandboxes - yet no framework tells a deployer what a defense actually guarantees…Quantum Machine Learning Protection of Military Quantum Key Distribution Against Cryptographically Camouflaged Attacks
Quantum key distribution proves its protocol secure and says nothing about the hardware beneath it, so military and government operators fielding it for command-and-control keys monitor the channel…StegoMemory: Agentic Memory Acts as Covert Steganographic Channel
Is agentic memory robust against stealthy steganographic attacks?When Talk Isn't Code: Comparing LLM Agents That Simulate and Develop Software
LLMs are used both to simulate network protocol implementations, as honeypots do, and to write them.Indistinguishability Lifting for Keyed Oracles, Compressed Ideal Cipher, and More Applications
Cryptographic security proofs often involve an adversary interacting with a larger, keyed oracle that consists of (potentially exponentially) many independent instances of a smaller, base oracle.Penumbra: Sample-Efficient Adversarial Search for Regulatory Obligations
Agents are entering finance, healthcare and law, sectors where a violation leaves no lexical signature and carries real penalties.Not Self-Decidable: LLMs Cannot Draw the Boundary of What an Agent Verifier Can Check
A verifier for an agent faces rules of two kinds: the ones a fixed check can settle and the ones that require a judge.APEX: Active Protection at Execution Boundaries for LLM Agents
Indirect prompt injection (IPI) hides adversarial instructions in content that large language model (LLM) agents read at runtime.Where Quantum Fourier Sampling Stops Short: A Three-Gate Audit Protocol for Delay-PUF Security Models
Quantum Fourier sampling may help audit the spectral learnability of delay-based physical unclonable functions (PUFs).When Normalization Selects the Sign: Auditing Robustness Ablations in Quantum Attention
Removing an input-scaling module changes both a classifier and the perturbations reaching its encoder.Differential Privacy of Gradient Descent on Perturbed Objectives
Objective perturbation adds a random linear term to a regularized empirical risk and releases the exact perturbed minimizer.Inner Momentum for Differentially Private Muon
Differentially private training clips each per-example gradient before adding noise.RMCW: A Deletion-Robust Watermark Based on Reed--Muller Codes for Language Models
Large Language Model (LLM) watermarking provides a lightweight mechanism for identifying text generated by a specific model, but its robustness remains fragile under post-processing attacks.AgentTrap: Stateful Feedback Deception against Autonomous Penetration Testing Agents
Autonomous penetration testing agents conduct multi-step attacks by continuously adapting their plans and actions to target responses.Frequency Is Not Sensitivity Identifying Safety-Sensitive Experts in Sparse MoE LLM
Suppressing a small set of routed experts can weaken the safety behavior of a sparse Mixture-of-Experts (MoE) language model without retraining.Beyond Predefined Sinks: Security-Aware Dependency Analysis for LLM Agents
Large language model (LLM)-based agents increasingly connect model-generated decisions to security-sensitive software capabilities such as command execution, filesystem access, network…SecJev: Bringing Security Expertise to System One Decision Models
Security workflows need models that turn complex observations and explicit policies into decisions.Securing Computer-Use Agents Against Branch Steering Attacks
Modern Computer Use Agents (CUAs) directly interact with graphical user interfaces and execute third-party web tools, exposing them to indirect prompt injection across every rendered page and tool…