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 24
Quiet Triggers, Loud Footprints: A Tri-Space Measurement Study of Backdoor Stealthiness
Backdoor attacks on deep neural networks are commonly evaluated by attack success rate and benign accuracy, yet their stealthiness remains inconsistently defined and measured.CoRedSkill: Stealthy Skill Composition Attacks via AST-Guided Injection
Agents extend their capabilities through reusable skills that package instructions, executable code, and resources.GeoCloak: An Agentic Framework for Semantics-Preserving Geolocation Privacy via Peripheral Clue Manipulation
The rapid advancement of large vision language models (LVLMs) has made geolocation inference from images highly accurate and accessible.P2Patch: Exploit-Guided Vulnerability Repair
Large language models are increasingly deployed as autonomous agents for automated program repair, patching software vulnerabilities through multi-step reasoning and tool use.From Training to Escalation: Forensic Analysis and Local Recreation of RL/RLHF-Induced Privilege Escalation in AI Agents (CVE-2026-65616)
Mid-2026 reporting on autonomous AI evaluation incidents claimed that frontier large language model (LLM) agents escaped server containment through emergent coordination and cloud infrastructure…Quantum-Aware Key Rank for Side-Channel Attacks evaluation
Side-channel attacks on block ciphers rarely recover the key outright: they leave a residual key space ordered by subkey posteriors, and evaluation laboratories report the key rank of the true key…CascadeLedger: Counterfactual Auditing of Information and Resource Integrity in LLM Cascades
We introduce CascadeLedger, a counterfactual execution-audit framework for budgeted language-model cascades.Counterfactual Neighborhood Risk Fields: Evidence-Grounded Selective Safety for Vision-Language Models
Representation-based safety defenses for vision-language models (VLMs) often learn to separate harmful from benign inputs, but separation alone does not establish what a risk score means or when it…INFUSE: A Unified Surface Benchmark for Indirect Prompt Injection in Agentic Systems
LLM-based agents increasingly execute long-horizon, end-to-end workflows involving shell commands, code modification, dependency installation, and interaction with remote services.Privacy-Preserving Text Inference for Health-Misinformation Classification and Semantic Retrieval
Health-misinformation analysis presents a dual challenge: misleading claims can affect public well-being, while systems designed to detect them may expose sensitive user-submitted text.Screwtape: Adversarial Transcript Editing for Monitor Red-Teaming
We present Screwtape, a pipeline that converts real agent transcripts into attacks that evade action-only monitors.Practical wavelength selection for quantum-augmented networks over shared optical fiber
In a quantum augmented network, sharing optical fiber with classical traffic requires quantum wavelength selection to balance logical fidelity, block acceptance, and classical data reception.Enhanced Detective: A Dual-Stage Proactive Defense Against Internal Poisoning Attacks in Financial Fraud Detection Using Federated Learning
With recent developments in Artificial Intelligence (AI), the adoption of distributed learning paradigms such as Federated Learning (FL) has emerged.SkillScreen: High-Recall Screening of Malicious Agent Skills with Decision Models
Agent skills turn natural-language instructions into actions, creating a security challenge: legitimate automation and malicious behaviour can look deceptively similar.Same Attack, Different Judge: A First Controlled Adversarial Comparison of Jev, LLM Judges, and Purpose-Built Safety Classifiers
TypeSafe AI's Jev is a new kind of safety judge.ProtoArena: Can AI Agents Discover and Exploit Real-World Cross-Component Protocol Vulnerabilities?
Large language model (LLM) agents are increasingly used for vulnerability analysis, yet existing benchmarks provide limited coverage of exploitation that depends on interactions among network…Cost-Efficient Privacy Enforcement for Email-Generating LLM Agents under Strategic Information Requests
LLM agents now act as delegated communication intermediaries: they read private context, draft messages, and send externally visible emails on behalf of a user.Privacy-Preserving GNN Inference on Financial Transaction Graphs via Homomorphic Encryption
Anti-money laundering (AML) in financial networks requires modeling transaction patterns that span multiple institutions.CompanionSafe: From Multi-turn Safety Evaluation to Guardrails for AI Companions
AI companions provide users with emotional support and ongoing companionship, but their sustained relational interactions also introduce distinct safety risks.Holding Agents Accountable with Verifiable Agent Insurance
While AI agents are useful for many tasks, they regularly misbehave in destructive ways, such as deleting important data.