cs.CR · 2026-05-25 · No. 9

Cryptography and Security, 2026-05-25.

6 new papers in cs.CR. Titles, authors, abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →

01 — The papers

6 entries
  1. 01

    Adversarial Vulnerability Under Temporal Concept Drift: A Longitudinal Study of Android Malware Detection

    Ahmed Sabbah, Mohammed Kharma, Radi Jarrar, Samer Zein, David Mohaisen

    cs.CR · cs.AI · cs.LG

    We present a longitudinal, drift-aware evaluation of adversarial robustness across more than a decade of Android applications using static and dynamic feature representations extracted from emulator and real-device executions. The dataset is organized into yearly slices and evaluated under three deployment protocols that emulate realistic learning scenarios: (1) same-year training and testing, (2) cross-year deployment without model updates,...

    arxiv.org/abs/2605.23623 · PDF

  2. 02

    AI Security Research Should Better Incentivize Defense Research

    Youqian Zhang

    cs.CR · cs.AI

    This work examines an imbalance in artificial intelligence (AI) security research: the field tends to produce more work on attacking AI systems than on defending them. Drawing on related academic papers, we find biased attack-to-defense ratios across subfields, including federated learning, speech recognition, membership inference, large language models, etc. The imbalance possibly means far beyond a simple count: attack papers are routinely...

    arxiv.org/abs/2605.23448 · PDF

  3. 03

    Are Frontier LLMs Ready for Cybersecurity? Evidence for Vertical Foundation Models from Dual-Mode Vulnerability Benchmarks

    Vivek Dahiya, Sunny Nehra, Vipul Dholariya, Bhavik Shangari, Chandra Khatri

    cs.CR · cs.AI

    We evaluate whether frontier LLMs are ready for cybersecurity through a dual-mode benchmark: white-box function-level vulnerability detection (VulnLLM-R, across C/Java/Python) and black-box web application security testing (five production-style applications with 118 ground-truth vulnerabilities across 20+ CWE families, which we will open-source). We test six frontier models (GPT-5.4, Codex~5.3, Claude Opus~4.6, Sonnet~4.6, Gemini~3.1~Pro and...

    arxiv.org/abs/2605.23243 · PDF

  4. 04

    PoisonForge: Task-Level Targeted Poisoning Benchmark for Instruction-Tuned LLMs

    Luze Sun, Anshuman Suri, Harsh Chaudhari, Cristina Nita-Rotaru, Alina Oprea

    cs.CR · cs.AI · cs.LG

    When practitioners fine-tune LLMs on unvetted datasets, an adversary can exploit the data supply chain through task-level poisoning: inserting a small number of crafted instruction-response pairs that cause the model to embed attacker-specified entities, such as a country, in outputs for a targeted task family while behaving normally elsewhere. We introduce PoisonForge, a benchmark that parameterizes this threat along four dimensions (bias...

    arxiv.org/abs/2605.23168 · PDF

  5. 05

    Innovations in Cardless Artificial Intelligence Banking: A Comprehensive Framework for Cyber Secure and Fraud Mitigation using Machine Learning Algorithms

    Md Israfeel

    cs.CR · cs.AI · cs.LG · cs.SE

    The advent of cardless artificial intelligence (AI) banking heralds a paradigm shift in the financial landscape, offering users unprecedented security and convenience. This paper outlines a comprehensive framework designed to enhance cybersecurity, introduce auto-generated virtual cards, and mitigate fraud risks within cardless AI banking systems. The framework envisions a future banking architecture that employs AI-powered data cryptography...

    arxiv.org/abs/2605.22604 · PDF

  6. 06

    Measuring Security Without Fooling Ourselves: Why Benchmarking Agents Is Hard

    Sahar Abdelnabi, Chris Hicks, Konrad Rieck, Ahmad-Reza Sadeghi

    cs.CR · cs.AI

    The benchmarks used to evaluate AI agents in security-critical roles suffer from crucial weaknesses. Building on recent empirical evidence, we characterize three core challenges that undermine security evaluations: benchmark vulnerabilities, temporal staleness, and runtime uncertainty. We then outline practical directions toward building more robust and trustworthy evaluation frameworks.

    arxiv.org/abs/2605.22568 · PDF

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