cs.CR · 2026-09-23 · No. 122

Cryptography and Security, 2026-09-23.

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

01 — The papers

5 entries
  1. 01

    A2M: Trace-Optimized Agent Hijacking in the MCP Ecosystem

    Laizhen Li, Xuan Wang, Peicheng Zhao, Juanjuan Zhao, Kejiang Ye, Cheng-zhong Xu, Xitong Gao

    cs.CR · cs.AI

    Agents using the Model Context Protocol (MCP) rely on semantic matching to select tools from third-party servers, exposing a semantic supply-chain risk through attacker-controlled metadata and outputs. We introduce A2M (Attraction-to-Manipulation), a two-stage black-box framework for hijacking MCP agents. The Attraction phase optimizes tool metadata to increase invocation probability; the Manipulation phase uses execution traces to refine...

    arxiv.org/abs/2609.26761 · PDF

  2. 02

    From Alignment to Access Control: A Framework for GenAI Policy Enforcement

    Nathalie Baracaldo

    cs.CR · cs.AI

    Generative AI (GenAI) applications have flourished enabling users to chat with large language models, and to create agents to act on their behalf for a variety of tasks. The pace of development of capabilities in this field is incredibly fast with security and safety taking a back seat. Unfortunately, the slower pace at which security and safety mechanisms have evolved has led to real incidents. Policy enables the definition of desirable...

    arxiv.org/abs/2609.26682 · PDF

  3. 03

    HYDRA: Proactive Android Malware Drift Adaptation via Hierarchical Graph Contrastive Learning

    Han Chen, Hanchen Wang, Hongmei Chen, Lu Qin, Wenjie Zhang, Ying Zhang

    cs.CR · cs.LG

    Concept drift, driven by the rapid evolution of Android malware, severely degrades the performance of machine learning detectors. Current adaptation strategies are often reactive, responding only after performance has dropped and imposing a significant manual annotation burden, or they are proactive but rely on unstable adversarial training and incomplete, single-level graph representations. To overcome these limitations, we propose HYDRA...

    arxiv.org/abs/2609.26352 · PDF

  4. 04

    On the security and privacy of LLMs in Mobility

    Mauro Conti, Lorenzo Perinello, Umberto Salviati

    cs.CR · cs.AI

    The mobility sector is undergoing a paradigm shift driven by advances in Generative Artificial Intelligence. With a global market valued at approximately 2.9 trillion dollars annually, considering only cars, the integration of these technologies has the potential to impact more than 1.5 billion vehicles worldwide. As Large Language Models (LLMs) are increasingly adopted in mobility, concerns about cybersecurity, privacy, and reliability...

    arxiv.org/abs/2609.26295 · PDF

  5. 05

    Toward Responsible AI-Augmented Cyber Defense: Pattern Recognition, Defense-in-Depth, and the Case for Human-AI Collaboration

    Mustafa S. Aljumaily, Hayder Kareem Abed, Nawar S. Alseelawi

    cs.CR · cs.AI

    Cybersecurity literature has extensively documented the operational benefits of artificial intelligence (AI) for threat detection, incident response, and prevention, while raising qualitative concerns about over-automation, algorithmic bias, and analyst-skill erosion. What remains largely absent is a formal, falsifiable model connecting three constructs that recur across this literature: Defense-in-Depth Theory, the Artificial Intelligence...

    arxiv.org/abs/2609.25921 · PDF

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