Literature Database

What Makes Adversarial Examples Transfer Across Deepfake Detectors?

Authors: Rafael M. Mamede, Pedro C. Neto, Ana F. Sequeira | Published: 2026-09-09
Model Robustness
Certified Robustness
攻撃手法の効果

Adversarial Training for Tabular Credit Scoring: A Multi-Attack Robustness Evaluation in P2P Lending

Authors: Gijs A. F. Niewzwaag, Marijn G. S. Veth, Manuele Massei, Marcos R. Machado | Published: 2026-09-09
Model Robustness
攻撃手法の効果
Adversarial Learning

Subgroup Membership Inference Audits of Differentially Private Synthetic Text

Authors: Yidan Sun, Viktor Schlegel, Srinivasan Nandakumar, Siew Kei Lam, Anil Anthony Bharath | Published: 2026-09-09
Differential Privacy
攻撃手法の効果
評価結果

CS-Guard: Benchmarking LLM Guardrails for Code Generation Security

Authors: Jinyang Li, Mingyu Guo, Hung X. Nguyen | Published: 2026-09-09
Alignment
Prompt Injection
監査手法

Kernel-Complexity Edge Sanitization for Training-Free Defense against Structural Graph Attacks

Authors: Yaning Jia, Shenyang Deng, Yaoqing Yang, Chiyu Ma, Wenxuan Xu, Soroush Vosoughi | Published: 2026-09-09
エラー解析
Model Robustness
Structural Learning

PrivAudit: A Dual-Lens Auditing Framework for Website Privacy Practices under the CCPA

Authors: Mohamed Moustafa Dawoud, Riya Aggarwal, Likith Rahul Krishnamurthy, Ram Sundara Raman | Published: 2026-09-09
Data Privacy Management
User Behavior Analysis
監査手法

Cascading Gradient Inversion via LT-Code Inspired Peeling in Federated Learning

Authors: Saeed Shariati, Mohsen Alambardar Meybodi | Published: 2026-09-09
データ再構築攻撃
Poisoning
Model Extraction Attack

Arbitrary Cipher Attacks Against Large Language Models Do Not Require Fine-Tuning

Authors: Thomas Rivasseau | Published: 2026-09-09
Indirect Prompt Injection
Large Language Model
攻撃手法の効果

PrivEscalate: Measuring and Augmenting the Threat of LLM-Automated Linux Privilege Escalation

Authors: Yixuan Liu, Zilong Zhen, Yin Wu, Yi Li | Published: 2026-09-08
Disabling Safety Mechanisms of LLM
Cybersecurity
攻撃手法の効果

Towards Standardized Evaluation of GPU Memory Safety with GMSBench

Authors: Saurabh Singh, Jaewon Lee, Seonjin Na, Hyesoon Kim | Published: 2026-09-08
エラー解析
メモリ効率化手法
評価結果