Saved in:
| Main Authors: | Liu, Honghao, Xu, Chengjin, Jiang, Xuhui, Yang, Cehao, Yin, Shengming, Ma, Zhengwu, Ni, Lionel, Guo, Jian |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2604.09750 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Continual Pretraining on Encrypted Synthetic Data for Privacy-Preserving LLMs
by: Liu, Honghao, et al.
Published: (2026)
by: Liu, Honghao, et al.
Published: (2026)
FedMUA: Exploring the Vulnerabilities of Federated Learning to Malicious Unlearning Attacks
by: Chen, Jian, et al.
Published: (2025)
by: Chen, Jian, et al.
Published: (2025)
Enhancing Code Vulnerability Detection via Vulnerability-Preserving Data Augmentation
by: Liu, Shangqing, et al.
Published: (2024)
by: Liu, Shangqing, et al.
Published: (2024)
White-box Membership Inference Attacks against Diffusion Models
by: Pang, Yan, et al.
Published: (2023)
by: Pang, Yan, et al.
Published: (2023)
The Vulnerability of LLM Rankers to Prompt Injection Attacks
by: Yin, Yu, et al.
Published: (2026)
by: Yin, Yu, et al.
Published: (2026)
ExtendAttack: Attacking Servers of LRMs via Extending Reasoning
by: Zhu, Zhenhao, et al.
Published: (2025)
by: Zhu, Zhenhao, et al.
Published: (2025)
Evaluating Large Language Models for Line-Level Vulnerability Localization
by: Zhang, Jian, et al.
Published: (2024)
by: Zhang, Jian, et al.
Published: (2024)
Multi-Faceted Attack: Exposing Cross-Model Vulnerabilities in Defense-Equipped Vision-Language Models
by: Yang, Yijun, et al.
Published: (2025)
by: Yang, Yijun, et al.
Published: (2025)
Towards Robust Multimodal Large Language Models Against Jailbreak Attacks
by: Yin, Ziyi, et al.
Published: (2025)
by: Yin, Ziyi, et al.
Published: (2025)
Evaluating and Enhancing the Vulnerability Reasoning Capabilities of Large Language Models
by: Lu, Li, et al.
Published: (2026)
by: Lu, Li, et al.
Published: (2026)
Practical Reasoning Interruption Attacks on Reasoning Large Language Models
by: Cui, Yu, et al.
Published: (2025)
by: Cui, Yu, et al.
Published: (2025)
JNI Global References Are Still Vulnerable: Attacks and Defenses
by: He, Yi, et al.
Published: (2024)
by: He, Yi, et al.
Published: (2024)
Learning-based Models for Vulnerability Detection: An Extensive Study
by: Ni, Chao, et al.
Published: (2024)
by: Ni, Chao, et al.
Published: (2024)
Beyond Fidelity: Explaining Vulnerability Localization of Learning-based Detectors
by: Cheng, Baijun, et al.
Published: (2024)
by: Cheng, Baijun, et al.
Published: (2024)
Pop Quiz Attack: Black-box Membership Inference Attacks Against Large Language Models
by: Chen, Zeyuan, et al.
Published: (2026)
by: Chen, Zeyuan, et al.
Published: (2026)
Asymmetry Vulnerability and Physical Attacks on Online Map Construction for Autonomous Driving
by: Lou, Yang, et al.
Published: (2025)
by: Lou, Yang, et al.
Published: (2025)
Revisiting Label Inference Attacks in Vertical Federated Learning: Why They Are Vulnerable and How to Defend
by: Liu, Yige, et al.
Published: (2026)
by: Liu, Yige, et al.
Published: (2026)
VulnRepairEval: An Exploit-Based Evaluation Framework for Assessing Large Language Model Vulnerability Repair Capabilities
by: Wang, Weizhe, et al.
Published: (2025)
by: Wang, Weizhe, et al.
Published: (2025)
Beyond Content Safety: Real-Time Monitoring for Reasoning Vulnerabilities in Large Language Models
by: Wang, Xunguang, et al.
Published: (2026)
by: Wang, Xunguang, et al.
Published: (2026)
Towards Unveiling Vulnerabilities of Large Reasoning Models in Machine Unlearning
by: Chen, Aobo, et al.
Published: (2026)
by: Chen, Aobo, et al.
Published: (2026)
When Reasoning Leaks Membership: Membership Inference Attack on Black-box Large Reasoning Models
by: Hu, Ruihan, et al.
Published: (2026)
by: Hu, Ruihan, et al.
Published: (2026)
Defenses at Odds: Measuring and Explaining Defense Conflicts in Large Language Models
by: Meng, Xiangtao, et al.
Published: (2026)
by: Meng, Xiangtao, et al.
Published: (2026)
Physical Backdoor Attack can Jeopardize Driving with Vision-Large-Language Models
by: Ni, Zhenyang, et al.
Published: (2024)
by: Ni, Zhenyang, et al.
Published: (2024)
A Cross-Modal Prompt Injection Attack against Large Vision-Language Models with Image-Only Perturbation
by: Yang, Hao, et al.
Published: (2026)
by: Yang, Hao, et al.
Published: (2026)
TAPFixer: Automatic Detection and Repair of Home Automation Vulnerabilities based on Negated-property Reasoning
by: Yu, Yinbo, et al.
Published: (2024)
by: Yu, Yinbo, et al.
Published: (2024)
Unveiling the Vulnerability of Private Fine-Tuning in Split-Based Frameworks for Large Language Models: A Bidirectionally Enhanced Attack
by: Chen, Guanzhong, et al.
Published: (2024)
by: Chen, Guanzhong, et al.
Published: (2024)
RLCracker: Evaluating the Worst-Case Vulnerability of LLM Watermarks with Adaptive RL Attacks
by: Huang, Hanbo, et al.
Published: (2025)
by: Huang, Hanbo, et al.
Published: (2025)
VulnLLM-R: Specialized Reasoning LLM with Agent Scaffold for Vulnerability Detection
by: Nie, Yuzhou, et al.
Published: (2025)
by: Nie, Yuzhou, et al.
Published: (2025)
Safety Tax: Safety Alignment Makes Your Large Reasoning Models Less Reasonable
by: Huang, Tiansheng, et al.
Published: (2025)
by: Huang, Tiansheng, et al.
Published: (2025)
Zero Day Attacks: Novel Behaviour or Novel Vulnerability?
by: Jibunoh, Nnamdi, et al.
Published: (2026)
by: Jibunoh, Nnamdi, et al.
Published: (2026)
Exploiting Cross-Layer Vulnerabilities: Off-Path Attacks on the TCP/IP Protocol Suite
by: Feng, Xuewei, et al.
Published: (2024)
by: Feng, Xuewei, et al.
Published: (2024)
Red Teaming Large Reasoning Models
by: Chen, Jiawei, et al.
Published: (2025)
by: Chen, Jiawei, et al.
Published: (2025)
Generative Large Language Model usage in Smart Contract Vulnerability Detection
by: Ince, Peter, et al.
Published: (2025)
by: Ince, Peter, et al.
Published: (2025)
A Novel Classification of Attacks on Blockchain Layers: Vulnerabilities, Attacks, Mitigations, and Research Directions
by: Dwivedi, Kaustubh, et al.
Published: (2024)
by: Dwivedi, Kaustubh, et al.
Published: (2024)
Unknown Attack Detection in IoT Networks using Large Language Models: A Robust, Data-efficient Approach
by: Ali, Shan, et al.
Published: (2026)
by: Ali, Shan, et al.
Published: (2026)
SoK: Understanding Vulnerabilities in the Large Language Model Supply Chain
by: Wang, Shenao, et al.
Published: (2025)
by: Wang, Shenao, et al.
Published: (2025)
ReasoningBomb: A Stealthy Denial-of-Service Attack by Inducing Pathologically Long Reasoning in Large Reasoning Models
by: Liu, Xiaogeng, et al.
Published: (2026)
by: Liu, Xiaogeng, et al.
Published: (2026)
Exploring the Vulnerabilities of Federated Learning: A Deep Dive into Gradient Inversion Attacks
by: Guo, Pengxin, et al.
Published: (2025)
by: Guo, Pengxin, et al.
Published: (2025)
Reconstruction Attacks on Machine Unlearning: Simple Models are Vulnerable
by: Bertran, Martin, et al.
Published: (2024)
by: Bertran, Martin, et al.
Published: (2024)
Privacy-Preserving Federated Learning Scheme with Mitigating Model Poisoning Attacks: Vulnerabilities and Countermeasures
by: Wu, Jiahui, et al.
Published: (2025)
by: Wu, Jiahui, et al.
Published: (2025)
Similar Items
-
Continual Pretraining on Encrypted Synthetic Data for Privacy-Preserving LLMs
by: Liu, Honghao, et al.
Published: (2026) -
FedMUA: Exploring the Vulnerabilities of Federated Learning to Malicious Unlearning Attacks
by: Chen, Jian, et al.
Published: (2025) -
Enhancing Code Vulnerability Detection via Vulnerability-Preserving Data Augmentation
by: Liu, Shangqing, et al.
Published: (2024) -
White-box Membership Inference Attacks against Diffusion Models
by: Pang, Yan, et al.
Published: (2023) -
The Vulnerability of LLM Rankers to Prompt Injection Attacks
by: Yin, Yu, et al.
Published: (2026)