Security & Cryptography · Computer Science & AI
Cryptography & Security
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1,372 papers, from 4 September 2026 to 5 November 2026: 1,372 arXiv preprints.
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Newest papers, page 22
Towards an Intelligent Decision Engine for Smart Contract Deployment
Layer 2 (L2) blockchain solutions emerged in recent years, predominantly in the Ethereum ecosystem, as a viable option for scaling a main blockchain network (L1).SPIDES: Information-Flow Control for Browser Agents
Browser agents automate complex workflows by reading and acting on arbitrary page content, but this flexibility exposes them to prompt injection attacks and unintended data exfiltration.Unbounded Losses, Unchecked Inputs: Measuring the Safeguards of LLM Options-Trading Agents
Large language model (LLM) agents now place options orders at retail brokers, and developers publish open-source options agents faster than anyone has examined them.Predicting CVSS and CWE from CVE Descriptions with Fine-Tuned Small Language Models
Until April 2026, the National Vulnerability Database labeled nearly every published Common Vulnerabilities and Exposures (CVE) entry with a Common Vulnerability Scoring System (CVSS) v3.1 vector, a…RATION: Towards Quantitative Risk-Based Access Control for AI Agents
AI agents increasingly interact with external environments to complete complex user tasks.Efficient Agnostic Learning of Univariate Densities Under Local Differential Privacy
We propose a general framework for non-interactive locally differentially private (LDP) learning of structured univariate distributions that can be approximated by piece-wise polynomials.SHIP: Structured and Hierarchical Insight with Privacy
Extracting insights from textual data while protecting user privacy is increasingly important.Approve What, Exactly? Effect Binding and Trusted Rendering for Agent Step-Up Authorization
AI agents now pause for approval before they send money, delete files or email outsiders, and that approval is usually treated as a second factor.Stronger Judges Do Not See More: Deployment-Integrity Blindness in LLM Evaluation
LLM-agent evaluation reports behavioral scores but leaves the identity of the deployed runtime stack unreported.CARE-SD: Explainable Reasoning Architecture for Local SLM Self-Disclosure Analysis in Textual Data
Cybersecurity interviews uncover critical security experiences, behaviors, and privacy concerns often not fully captured by conventional technical assessments.FedPix: Learning What to Share with Less Leakage in Federated Intelligence
Existing federated intelligence paradigms primarily focus on how models are trained or aggregated, while overlooking a more fundamental question: \textbf{what information should leave the client?}…Clean Data Can Still Carry Backdoors: Support-Persistent Backdoors for Model Reuse
Traditional backdoor attacks inject artificial trigger patterns into training data, causing models to misclassify triggered inputs while maintaining normal behavior on clean samples.Combining LLM and Dynamic Analysis for Universal Testing of Static Application Security Testing Tools
Static application security testing (SAST) tools help detect potential software vulnerabilities, but they themselves contain faults undermining reliability and usability.QuasiLeak-Bench: Evaluating LLM-Based Privacy Detectors on Policy-Conditioned Quasi-Evidence Group Recovery
Natural language messages often contain quasi-identifiers that are weak in isolation but can jointly reveal an individual's private attributes.Evaluating and Improving Static Application Security Testing Tools for Rust
Rust has become a popular systems programming language in recent years because of its strong guarantees on memory and concurrency safety.Matoi: Selective Revalidation of Consequence Evidence for Agent Authorization
Matoi separates reusable consequence evidence from current execution authority and durable occurrence history.NEST: Stale-Robust Tokenized-Stock Measurement for a Crypto-Treasury LETF Stack
Tokenized equities trade while listed markets are closed, so their marks could improve off-hours measurement along layered exposures that link Bitcoin, crypto-treasury stocks, and leveraged ETFs.Pricing the Unprovable: Ambiguity, Turnstile Runs, and the Verifiability Discount in Crypto Protocols
Privacy-preserving protocols can repair a vulnerability without establishing whether it was exploited beforehand, even when no theft is reported and aggregate supply is protected.Federated Art Chase: Privacy Leakage and Data Poisoning in Federated Fine-Tuning
Federated learning (FL) is increasingly used to adapt a pretrained, centrally fine-tuned model to data that remain on user devices, beyond training a model from scratch.BiShield-TEE: On the (In-)Security of Unilateral Weight Obfuscation in On-Device TEE-Shielded LLM Partition
On-device deployment of large language models (LLMs) is increasingly adopted due to latency and privacy requirements, but exposes proprietary models to reverse engineering and intellectual property…