cs.CR · 2026-07-20 · No. 59

Cryptography and Security, 2026-07-20.

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

01 — The papers

8 entries

01

Evaluating Open-Weight LLMs for Generating Structured Threat Information for Autonomous Vehicle Vulnerabilities

Md Erfan, Ahmed Ryan, Md Kamal Hossain Chowdhury, Md Rayhanur Rahman

cs.CR · cs.AI

Connected and Autonomous Vehicles (CAVs) rely on interconnected software and hardware components, including sensors, Electronic Control Units, in-vehicle infotainment systems, and telematics units, where vulnerabilities can compromise assets, users, and vehicle operations. These vulnerabilities are commonly documented as plain text in the Common Vulnerabilities and Exposures (CVE) database; however, security practitioners require structured...

arxiv.org/abs/2607.16175 · PDF
02

Code-Poisoning Property Inference Attacks

Xukun Luan, Yuhui Gong, Gang Zhang, Zixuan Huang, Yuanguo Bi, Xuesong Li, Jinyan Liu

cs.CR · cs.LG

The flourishing code hosting platforms and coding agents enable even beginners with private data to build tailored Machine Learning (ML) models using available code quickly. The training data for ML models, often regarded as private property (e.g., clinical records, transaction information), is at significant risk of information leakage. Property Inference Attacks (PIAs), as a significant type of privacy attack, aim to expose global property...

arxiv.org/abs/2607.15970 · PDF
03

Natural Backdoor Attacks on Speech Recognition Models

Jinwen Xin, Xixiang Lyu, Jing Ma

cs.CR · cs.LG · cs.SD

With the rapid development of deep learning, its vulnerability has gradually emerged in recent years. This work focuses on backdoor attacks on speech recognition systems. We adopt sounds that are ordinary in nature or in our daily life as triggers for natural backdoor attacks. We conduct experiments on two datasets and three models to validate the performance of natural backdoor attacks and explore the effects of poisoning rate, trigger...

arxiv.org/abs/2607.15724 · PDF
04

Do Agents Dream of False Memories? Black-box Visual Attacks on Long-term Memory in Multimodal AI Agents

Halima Bouzidi, Mboutidem Ekemini Mkpong, Mohammad Abdullah Al Faruque

cs.CR · cs.CV · cs.LG

Multimodal AI agents increasingly rely on persistent long-term memory to ground generation in past visual and textual episodes. We show that unconditional trust in visual data creates a critical vulnerability. We propose Lucid, a black-box adversarial framework that compromises multimodal memory pipelines under a strictly image-bounded threat model, requiring no access to the target MLLM, target retrieval encoder, or the text channel. Lucid...

arxiv.org/abs/2607.15657 · PDF
05

FLINT: Fingerprinting Federated Learning Architectures from 5G PHY-Layer Side Channels

Md Nahid Hasan Shuvo, Mahmudul Hassan Ashik, Moinul Hossain

cs.CR · cs.AI · cs.LG

Federated Learning (FL) over 5G cellular networks protects raw data but remains vulnerable to side-channel leakage. Prior fingerprinting attacks assume packet-level network visibility, an assumption that does not hold at the 5G Physical (PHY) layer, where user payloads are encrypted and Radio Network Temporary Identifiers (RNTIs) may change over time. However, we demonstrate that PHY-layer scheduling metadata broadcast over the Physical...

arxiv.org/abs/2607.15469 · PDF
06

Improving Network Anomaly Detection via Choquet-Integral-Based Feature Aggregation

Abreu Quevedo, Roger Immich, Giancarlo Lucca, Graçaliz Dimuro, Bruno L. Dalmazo

cs.CR · cs.LG

This work investigates a generalized Choquet-integral-based feature aggregation framework to improve anomaly detection in high-dimensional network traffic data. The approach combines adaptive weighting with incremental feature selection to address feature redundancy. Using Random Forest and XGBoost classifiers, we evaluate models trained with both raw and Choquet-aggregated features under varying feature subset sizes. The proposed aggregation...

arxiv.org/abs/2607.15389 · PDF
07

On the Impact of Entropy-based Features

Iuri Mundstock, Abreu Quevedo, Jéferson Campos Nobre, Roben C. Lunardi, Thiago L. T. da Silveira, Bruno L. Dalmazo

cs.CR · cs.LG

Network anomaly detection is increasingly challenging due to the growing diversity and variability of traffic patterns, which are not always well captured by traditional statistical features. In this work, we explore the use of entropy as an additional feature to support supervised network traffic classification. The main idea is to use entropy to represent variability in selected traffic attributes, complementing conventional descriptors...

arxiv.org/abs/2607.15379 · PDF
08

Beyond Success Rate: Cost-Aware Evaluation of Offensive and Defensive Security Agents

Paul Kassianik, Blaine Nelson, Yaron Singer

cs.CR · cs.AI

Security-agent evaluations commonly measure peak offensive capability under generous inference budgets, emphasizing vulnerability discovery, exploit development, penetration testing, and CTF completion. Such measurements are useful but incomplete: in operational security, every reasoning step, tool call, telemetry query, and enrichment request consumes budget. We evaluate language-model security agents through this cost-success lens on...

arxiv.org/abs/2607.15263 · PDF

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