cs.CR · 2026-06-11 · No. 20

Cryptography and Security, 2026-06-11.

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

    Categorical Robustness Assessment for Machine Learning based Network Intrusion Detection Systems

    Mayank Raj, Nathaniel D. Bastian, Lance Fiondella, Gokhan Kul

    cs.CR · cs.LG

    Network Intrusion Detection Systems (NIDS) heavily utlize Machine Learning (ML) but ML models can be manipulated via adversarial attacks. These attacks add carefully crafted perturbations to network traffic data that leads to misclassifications. While prior work has demonstrated adversarial vulnerabilities in isolated settings, systematic cross-architecture as well as class and category of attack based comparisons under controlled attack...

    arxiv.org/abs/2606.12075 · PDF

  2. 02

    Grammar-Constrained Decoding Can Jailbreak LLMs into Generating Malicious Code

    Yitong Zhang, Shiteng Lu, Jia Li

    cs.CR · cs.AI · cs.CL · cs.SE

    Large Language Models (LLMs) are increasingly used for code generation, raising concerns that they may be misused to produce malicious code. Meanwhile, Grammar-Constrained Decoding (GCD) has been widely adopted to improve the reliability of LLM-generated code by enforcing syntactic validity. In this paper, we reveal a counterintuitive risk: this reliability-oriented technique can itself become an attack surface. We uncover a new jailbreak...

    arxiv.org/abs/2606.11817 · PDF

  3. 03

    MHOT: Height-Optimized Authenticated Data Structure for Blockchain State Commitment

    Sipeng Xie, Qianhong Wu, Minghang Li, Qiyuan Gao, Bo Qin, Qin Wang

    cs.CR · cs.DC · cs.ET

    State root computation dominates (78%) blockchain block processing time. Ethereum's canonical authenticated data structure, i.e., Merkle Patricia Trie (MPT), suffers from severe tree-height growth and is vulnerable to \textit{Nurgle attacks} (SP'24), where adversaries inflate path depth via hash collisions and degrade system performance at negligible cost. Existing defenses increase node fanout (span) to bound tree height, but higher span...

    arxiv.org/abs/2606.11736 · PDF

  4. 04

    T2S: A Rehearsal-Based Approach for Extraction-Resistant Model Watermarking

    Jian-Ping Mei, Weibin Zhang, Ao Yao, Tiantian Zhu, Jie Xiao

    cs.CR · cs.AI

    Model watermarking safeguards AI model intellectual property by embedding distinctive knowledge that induces unique behavioral signatures. The primary technical challenge lies in ensuring watermark robustness against various post-processing attacks on the watermarked model. Model extraction attacks emerge as the most severe threat, where adversaries exploit prediction outputs to train surrogate models that illegally replicate the original...

    arxiv.org/abs/2606.11698 · PDF

  5. 05

    Can Open-Source LLM Agents Replace Static Application Security Testing Tools? An Empirical Assessment

    Derek Yohn, Luke Flancher, Mirajul Islam, Khaled Slhoub

    cs.CR · cs.AI

    This paper explores the value of agentic AI tools for cybersecurity purposes. We evaluate the efficacy of a general-purpose GenAI Large Language Model- (GenAI-) based agent when powered by three different Ollama-hosted general-purpose open source models. We assess each agent's performance using precision, recall, false positive count, and a calculated composite score based upon the interplay of the captured metrics, against the baseline...

    arxiv.org/abs/2606.11672 · PDF

  6. 06

    Runtime Skill Audit: Targeted Runtime Probing for Agent Skill Security

    Tu Lan, Chaowei Xiao

    cs.CR · cs.AI

    Agent skills let LLM agents reuse instructions, resources, tools, and workflows, but they also create a new place for malicious behavior to hide. A skill may look benign in its documentation or code while becoming harmful only when it is invoked with particular user requests, local assets, persistent state, or multi-step tool interactions. This makes purely static vetting brittle. We present Runtime Skill Audit (RSA), a dynamic analysis...

    arxiv.org/abs/2606.11671 · PDF

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