cs.CR · 2026-07-01 · No. 40

Cryptography and Security, 2026-07-01.

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

    A Lifecycle and Application-Stack Survey of Large Language Model Vulnerabilities: Attacks, Risks, Defenses, and Open Problems

    Seyed Bagher Hashemi Natanzi, Bo Tang

    cs.CR · cs.AI · cs.GT · cs.LO

    Large language models are no longer only text generators. They are increasingly embedded in retrieval pipelines, enterprise assistants, coding environments, robotic systems, security-operation workflows, and autonomous agents that can read private data, call tools, write files, execute code, and act across organizational boundaries. This shift changes the security problem: risks do not arise from the model weights alone, but from the full...

    arxiv.org/abs/2606.31639 · PDF

  2. 02

    Comparative Analysis of Machine Learning based Intrusion Detection in Realistic IoT Networks

    Rana Alharbi, Chuadhry Mujeeb Ahmed

    cs.CR · cs.AI

    The Internet of Things (IoT) is rapidly growing and expanding into various sectors, such as healthcare, transportation, smart homes, and more. Despite the benefits of using IoT devices, they present several challenges. Given the significant role these devices play in our lives, it is crucial to address issues related to their security and privacy. These devices are limited in resources, which complicates their security and the protection of...

    arxiv.org/abs/2606.31594 · PDF

  3. 03

    CVE-TTP KG: Knowledge Graph Linking Software Vulnerabilities to Attack Behaviors

    Basant Agarwal, Dincy R. Arikkat, Swati Yadav, Serena Nicolazzo, Antonino Nocera, Vinod P

    cs.CR · cs.AI

    In the evolving threat landscape, adversaries exploit software vulnerabilities to launch sophisticated attacks, challenging traditional defenses. Although databases like CVE and NVD provide detailed technical information, they often lack links to attacker behaviors such as tactics and techniques, limiting effective threat interpretation and response. This work bridges this gap by connecting vulnerabilities with behavioral patterns from the...

    arxiv.org/abs/2606.31557 · PDF

  4. 04

    CSO-LLM: Class Subspace Orthogonalization for Post-Training Backdoor Detection and Trigger Inversion in LLMs

    Zhengxing Li, David J. Miller, Guangmingmei Yang, George Kesidis

    cs.CR · cs.AI · cs.LG

    While post-training backdoor detection and trigger inversion schemes have been developed for AIs used e.g. for images, there is a paucity of such methods for LLMs. First, the LLM input space is discrete, with up to 150,000^k k-tuples to consider with k the token-length of a putative trigger. Second, one must blacklist tokens typical of the putative target response (class) of an attack, as such tokens may give false detection signals. However,...

    arxiv.org/abs/2606.31309 · PDF

  5. 05

    The Decomposition Is the Fingerprint: Per-Component Identity for Agent Skills

    Hongliang Liu, Yuhao Wu, Tung-Ling Li

    cs.CR · cs.CL · cs.LG

    AI agents increasingly acquire and execute skills at runtime: bundles of prompt instructions, executable code, and tool declarations fetched from marketplaces and other agents. Governing them needs a stable notion of skill identity, yet cryptographic hashing is engineered to destroy the very similarity we need, as a one-character edit scrambles the digest. We present a compact, locality-sensitive fingerprint that embeds each component of a...

    arxiv.org/abs/2606.31272 · PDF

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