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 57
ActGov: Governing LLM Agent Actions via Policy-Constrained Validation
Large language model (LLM) agents increasingly execute long-horizon workflows through external tools, allowing untrusted outputs to influence subsequent actions and exceed user authorization.Beyond Predictable Paths: Redefining AI Security Incident Reporting for Agents
AI agents are being deployed rapidly, accompanied by a growing number of AI-specific attacks and corresponding incidents.MIRAGE: Full-Body Bystander Privacy for Smart Glasses with Consent-Based Restoration
Video recording on smart glasses exposes more than faces.Trust in Edge-Enabled IoT Security: Features, Challenges and Research Directions
Providing autonomous intelligence, pervasive connectivity and usability to human life and industry has led to the emergence of the Internet of Things (IoT).Reasoning Topology Matters: A Controlled Study of LLM-Based Cybersecurity Analysis
Large Language Models (LLMs) are increasingly used in cybersecurity, where accurate analysis often requires multi-step and context-dependent reasoning over complex and heterogeneous data.Decoding Guardrails: XAI-Guided Perturbation Analysis of Prompt Injection Detection
Large language models (LLMs) are increasingly deployed in production systems, raising concerns about their exposure to adversarial manipulation through prompt injection and jailbreak attacks.Benchmarking Neural Defend ARCAS 1B: A Foundational Multimodal Deepfake Detection Model
AI-generated imagery evolves faster than benchmark-specific detector evaluations, making a single score an incomplete account of generalization.Making Agents More Consistent: Skills Should Form Habits for Repeat Tasks
On repeated work, agents are inconsistent.SSP-Bench: A Hybrid Data Generation Framework for Safety, Security, and Privacy Evaluation
Evaluation of large language models (LLMs) for safety, security, and privacy (SSP) relies heavily on static benchmarks, which suffer from score saturation, data contamination, and aggregation…ZeroGate: Trust-Preserving Fast Paths for Governed AI Agent Runtimes
Moving authorization earlier can shorten an agent's dispatch boundary without removing authorization work.Specification Before Generation: A Pre-Registered, Five-Model Paired Evaluation of a Specification Frame for LLM-Generated Code in Money, Time, Idempotency, and Access Tasks
Code generated by large language models passes security checks at a rate that has barely moved in four years.StyleAT: Defending Face Recognition Against Semantic Attacks
With face-recognition models now embedded in everyday authentication and surveillance, recent works have pinpointed a critical weakness: these models remain acutely vulnerable to adversarial…When the Agent Becomes the Kernel: A Systematization of Security on the Path to AI-Native Operating Systems
Large language model agents are now privileged principals that take consequential actions: editing code repositories, operating inboxes, completing purchases.TriFleetRCA: On-Premise LLM Root Cause Analysis for Kubernetes
Root cause analysis at a remote site is slow: evidence is scattered across pod logs, Kubernetes events and cluster-level objects, and many operators cannot send production logs to a hosted model at…SyzHarness: Patch-Based Kernel Bug Reproduction with LLM-Synthesized Fuzzing Harnesses
Automated kernel vulnerability reproduction is essential for bug triage, patch validation, and regression testing, but still lacks an effective and efficient solution.Connecting the Dots in Agentic AI Security: A Cross-Dimensional Threat Taxonomy, Evaluation Maturity, and Open Challenges
Agentic AI extends LLM security beyond generated content to persistent state, autonomous actions, tool use, and interactions with humans and other agents.Agents That Edit Documents: Measuring Agentic PDF Forgery Against a Non-Agentic Control
AI agents that carry a multi-step computer task through on their own became ordinary tools in the past year, and the same autonomy is available to anyone whose task is harmful.TRACTOR Benchmark for Evaluating C to Rust Translators
Memory-safety vulnerabilities remain a persistent source of security risk in critical software, much of which is implemented in memory-unsafe languages such as C and C++.From Capability to Assurance in Autonomous Penetration-Testing Harnesses: A Framework and Reference Implementation
Research on large language model agents for penetration testing is evaluated almost entirely by capability: whether the agent captures a flag or reproduces a proof of concept.MATE: Policy-Aware Security Auditing for Mobile Agents via Synthesis-Driven Trajectory Learning
Mobile agents powered by foundation models now automate complex, multi-step workflows on real devices, but their trajectories can violate app-specific security policies.