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 34
Mission-Aware Attestation Envelopes for Time-Critical Autonomous Action: A Hardware-in-the-Loop V2I Study
An autonomous system that asks for a privileged physical action is usually gated on integrity evidence: a platform proves what it is running, and the request is granted or refused on that basis.CredLeakBench: Evaluating Credential Leakage and Recovery in LLM Agents
Language model agents are increasingly deployed to automate everyday digital chores from managing emails and social media to handling banking and bills allowing users to step away from supervision.Contextualization of Third-Party Cloud Security Findings
Finding severity is the main driver of how security teams prioritize remediation.Multi-Aspect Runtime Verification for Simulation-Based V&V of LLM-Enabled Autonomous Agents
LLM-based agents are entering decision-support roles in defence staff work, where the obligations they must respect are already written down and binding, and where retraining is not available as a…How Fragile Is On-Device Language Model Safety? Localizing Safety-Critical Parameters for Sparse Fault Analysis
As small language models (SLMs) are increasingly deployed on resource-constrained and on-device platforms, including as components of agentic systems, the integrity of locally stored model…Removing Information Content Does Not Certify Tamper Resistance in Open-Weight Models
Does removing harmful information make open-weight models resistant to fine-tuning attacks?Sigma-Hunter: A Domain-Specific Language Model for Threat Hunting and Detection Engineering
Detection engineers must translate threat reports, forensic observations, and hunt hypotheses into precise, testable rules.Visual Memory Attacks Can Persist Through The KV Cache
Modern language model systems operate autonomously over increasingly long contexts containing untrusted text and images.Cost of Delay for Post-Quantum Migration: Putting Classical and Harvest-Now-Decrypt-Later Risk on One Ordered List
Organisations deciding which assets to migrate to post-quantum cryptography first, and how that work competes with a backlog of classical findings, lack a common unit: post-quantum scores are…Quad-State Safety Evaluation of Open-Weight Large Language Models on Non-Canonical Inputs
Standard safety evaluations of large language models assess harmful requests written in canonical plain text, while models in real-world deployment routinely receive inputs containing emojis…SwarmReconGuard: Black-Box Detection of Distributed Collective Reconnaissance by Individually Benign-Looking Agent Populations
Autonomous and agentic clients can distribute reconnaissance across many identities so that each request remains valid, low-rate, and benign-looking while the population collectively acquires broad…Certified Corruption Budgets: Anytime-Valid Leaderboard Claims under Adaptive Rigging
Public leaderboards for AI models are read continuously, and attackers can see every published standing.Visual Grounding Safety in Vision-Language Models
Vision-language models (VLMs) are increasingly trained to generate structured outputs like points and bounding boxes that downstream interfaces, agents, and robots can act on, yet safety alignment…SimpleMark: Fast Multi-Bit Text Watermarking under f -Divergence Constraints
We introduce a framework for multi-bit text watermarking with security defined directly through $f$-divergence from the base language model distribution.Agentic-ZTA: A Multi-Agent Architecture for Autonomous Zero Trust Enforcement
Agentic AI is emerging as a promising paradigm for automating complex cybersecurity decisions, yet its use in enforcing zero trust introduces significant challenges in safety, reliability, and…Online AutoML: Evaluating Poisoning Attacks on Adversarial Training Defense Strategy in IoT Networks
Machine learning (ML)-powered poisoning attack vectors are adversarial maneuvers whereby an attacker intentionally inserts, corrupts, or alters training data to distort an ML model's learning process.Adaptive-Shot Hybrid Quantum Anomaly Detection for Tactile Internet Security: Reliability-Aware Measurement Allocation Under Resource Constraints
Tactile Internet (TI) security analytics must balance reliable thresholded decisions with constrained computational and measurement resources.Runaway Reaction: When Benign Skills Compose into Malicious Behavior
Agent skills package task-specific knowledge and procedures that can be composed to support complex agent tasks, while public marketplaces provide a growing pool of reusable skills.Backdooring Sparse Autoencoders
Sparse autoencoders (SAEs) are increasingly used not only to interpret language models but also to intervene on their internal representations.Cross-Lingual Transferability of Training Data Extraction Attacks to Recover Memorized PII
The robustness of Personally Identifiable Information (PII) protection in Large Language Models (LLMs) is a critical concern, yet the risks associated with cross-lingual data extraction remain…