Imagen de Portada

Evaluating Prompt Injection Defenses for Educational LLM Tutors: Security-Usability-Latency Trade-offs

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autor principal:	Maiorano, Alexandre Cristovão
Formato:	Preprint
Publicado:	2026
Materias:	Cryptography and Security Artificial Intelligence Machine Learning
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

How Not to Detect Prompt Injections with an LLM
por: Choudhary, Sarthak, et al.
Publicado: (2025)

Checkpoint-GCG: Auditing and Attacking Fine-Tuning-Based Prompt Injection Defenses
por: Yang, Xiaoxue, et al.
Publicado: (2025)

Formalizing and Benchmarking Prompt Injection Attacks and Defenses
por: Liu, Yupei, et al.
Publicado: (2023)

LeakSealer: A Semisupervised Defense for LLMs Against Prompt Injection and Leakage Attacks
por: Panebianco, Francesco, et al.
Publicado: (2025)

Trust No AI: Prompt Injection Along The CIA Security Triad
por: Rehberger, Johann
Publicado: (2024)

An Empirical Evaluation of LLM-Generated Code Security Across Prompting Methods
por: Kharma, Mohammed, et al.
Publicado: (2026)

RedVisor: Reasoning-Aware Prompt Injection Defense via Zero-Copy KV Cache Reuse
por: Liu, Mingrui, et al.
Publicado: (2026)

A Systematic Literature Review on LLM Defenses Against Prompt Injection and Jailbreaking: Expanding NIST Taxonomy
por: Correia, Pedro H. Barcha, et al.
Publicado: (2026)

Logic layer Prompt Control Injection (LPCI): A Novel Security Vulnerability Class in Agentic Systems
por: Atta, Hammad, et al.
Publicado: (2025)

MarkTune: Improving the Quality-Detectability Trade-off in Open-Weight LLM Watermarking
por: Zhao, Yizhou, et al.
Publicado: (2025)

Security in the Fine-Tuning Lifecycle of Large Language Models: Threats, Defenses,Evaluation, and Future Directions
por: Li, Wenjuan, et al.
Publicado: (2026)

PIArena: A Platform for Prompt Injection Evaluation
por: Geng, Runpeng, et al.
Publicado: (2026)

Prompt Injection Attacks on Large Language Models in Oncology
por: Clusmann, Jan, et al.
Publicado: (2024)

Attention Tracker: Detecting Prompt Injection Attacks in LLMs
por: Hung, Kuo-Han, et al.
Publicado: (2024)

Evaluation of Prompt Injection Defenses in Large Language Models
por: Deep, Priyal, et al.
Publicado: (2026)

SHIELD: Secure Hypernetworks for Incremental Expansion Learning Defense
por: Krukowski, Patryk, et al.
Publicado: (2025)

Attacks and Defenses Against LLM Fingerprinting
por: Kurian, Kevin, et al.
Publicado: (2025)

Systematically Analyzing Prompt Injection Vulnerabilities in Diverse LLM Architectures
por: Benjamin, Victoria, et al.
Publicado: (2024)

A Critical Evaluation of Defenses against Prompt Injection Attacks
por: Jia, Yuqi, et al.
Publicado: (2025)

AEGIS : Automated Co-Evolutionary Framework for Guarding Prompt Injections Schema
por: Liu, Ting-Chun, et al.
Publicado: (2025)

LLM Security: Vulnerabilities, Attacks, Defenses, and Countermeasures
por: Aguilera-Martínez, Francisco, et al.
Publicado: (2025)

The Autonomy Tax: Defense Training Breaks LLM Agents
por: Li, Shawn, et al.
Publicado: (2026)

BrowseSafe: Understanding and Preventing Prompt Injection Within AI Browser Agents
por: Zhang, Kaiyuan, et al.
Publicado: (2025)

The Defense Trilemma: Why Prompt Injection Defense Wrappers Fail?
por: Bhatt, Manish, et al.
Publicado: (2026)

Measuring Real-World Prompt Injection Attacks in LLM-based Resume Screening
por: Zhang, Mohan, et al.
Publicado: (2026)

Seed Hijacking of LLM Sampling and Quantum Random Number Defense
por: You, Ziyang, et al.
Publicado: (2026)

PoolFlip: A Multi-Agent Reinforcement Learning Security Environment for Cyber Defense
por: Cadet, Xavier, et al.
Publicado: (2025)

Enhancing Security in Deep Reinforcement Learning: A Comprehensive Survey on Adversarial Attacks and Defenses
por: Yichao, Wu, et al.
Publicado: (2025)

AgentDojo: A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents
por: Debenedetti, Edoardo, et al.
Publicado: (2024)

Can Adversarial Code Comments Fool AI Security Reviewers -- Large-Scale Empirical Study of Comment-Based Attacks and Defenses Against LLM Code Analysis
por: Thornton, Scott
Publicado: (2026)

Accuracy-Privacy Trade-off in the Mitigation of Membership Inference Attack in Federated Learning
por: Ahamed, Sayyed Farid, et al.
Publicado: (2024)

Generalist++: A Meta-learning Framework for Mitigating Trade-off in Adversarial Training
por: Wang, Yisen, et al.
Publicado: (2025)

DRIFT: Dynamic Rule-Based Defense with Injection Isolation for Securing LLM Agents
por: Li, Hao, et al.
Publicado: (2025)

PromptArmor: Simple yet Effective Prompt Injection Defenses
por: Shi, Tianneng, et al.
Publicado: (2025)

RITA: Automatic Framework for Designing of Resilient IoT Applications
por: Pessoa, Luis Eduardo, et al.
Publicado: (2024)

The Task Shield: Enforcing Task Alignment to Defend Against Indirect Prompt Injection in LLM Agents
por: Jia, Feiran, et al.
Publicado: (2024)

MPC-Minimized Secure LLM Inference
por: Rathee, Deevashwer, et al.
Publicado: (2024)

CommandSans: Securing AI Agents with Surgical Precision Prompt Sanitization
por: Das, Debeshee, et al.
Publicado: (2025)

A Multi-Agent LLM Defense Pipeline Against Prompt Injection Attacks
por: Hossain, S M Asif, et al.
Publicado: (2025)

Architecting Secure AI Agents: Perspectives on System-Level Defenses Against Indirect Prompt Injection Attacks
por: Xiang, Chong, et al.
Publicado: (2026)