Surveys
18 resourcesSurveys & Meta
Literature surveys, systematizations of knowledge, and meta-analyses
When Agents Handle Secrets: A Survey of Confidential Computing for Agentic AI
Javad Forough, Marios Kogias, Hamed Haddadi
Agentic AI systems, specifically LLM-driven agents that plan, invoke tools, maintain persistent memory, and delegate tasks to peer agents via protocols such as MCP and A2A, introduce a threat surface that differs materially from standalone model inference. Agents accumulate sensitive context, hold credentials, and operate across pipelines no single party fully controls, enabling prompt injection, context exfiltration, credential theft, and inter-agent message poisoning. Current defenses operate
A Survey on the Security of Long-Term Memory in LLM Agents: Toward Mnemonic Sovereignty
Zehao Lin, Chunyu Li, Kai Chen
Research on large language model (LLM) security is shifting from "will the model leak training data" to a more consequential question: can an agent with persistent, long-term memory be continuously shaped, cross-session poisoned, accessed without authorization, and propagated across shared organizational state? Recent surveys cover memory architectures and agent mechanisms, but fewer center the epistemic and governance properties of persistent, writable memory as the reason memory is an independ
Token Economics for LLM Agents: A Dual-View Study from Computing and Economics
Yuxi Chen, Junming Chen, Chenyu He + 9 more
As LLM agents evolve, tokens have emerged as the core economic primitives of Agentic AI. However, their exponential consumption introduces severe computational, collaborative, and security bottlenecks. Current surveys remain fragmented across system optimization, architecture design, and trust, lacking a unified framework to evaluate the fundamental trade-off between output quality and economic cost. To bridge this gap, this survey presents the first comprehensive survey of Token Economics. By u
Toward Web 4.0: Bidirectional Trust between AI Agents and Blockchain
Yunfeng Xia, Chao Li, Lei Li + 4 more
Autonomous AI agents are increasingly deployed on blockchain platforms, yet the design space that governs their interaction remains poorly understood. This convergence, where autonomous agents operate on and within decentralized systems, is a defining feature of the emerging Web~4.0 paradigm. This paper presents a Systematization of Knowledge organized around a bidirectional trust framework. In the B $\boldsymbol{\rightarrow}$ A direction, we examine how blockchain provides trust infrastructure
<b>Inovasi Pembelajaran Fisika Di Era Digital: Suatu Tinjauan Sistematis Terhadap Model Dan Teknologi Pembelajaran Modern</b>
Muhammad Jailani
The low effectiveness of conventional physics instruction in enhancing students’ conceptual understanding and engagement has become a major challenge in higher education physics learning. Therefore, this study aims to analyze patterns of innovation in digital-era physics learning and examine their implications for pedagogical practice, educational policy, and future research directions. This study employed a qualitative literature review approach by examining scholarly articles indexed i
The State of Safety and Security Measures in Public Schools: A Comparative Study of African and European Countries from 2006-2024
Tinyiku David Ngoveni
School violence remains one of the serious challenges in African and European countries. It is also regarded as a persisting symptom cutting across almost different countries. The qualitative research approach and non-empirical research design, specifically a systematic review were adopted. For data collection, literature reviews were conducted from five (05) countries, consisting of three (3) African countries, including: 1) South Africa (SA), 2) Ghana, and; 3) Zimbabwe and other two (2
DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models
Boxin Wang, Weixin Chen, Hengzhi Pei + 7 more — NeurIPS 2023
Comprehensive trustworthiness evaluation of GPT models across 8 dimensions including toxicity, bias, robustness, privacy, fairness, and machine ethics.
A Survey on Large Language Model (LLM) Security and Privacy: The Good, The Bad, and The Ugly
Yifan Yao, Jinhao Duan, Kaidi Xu + 3 more — High-Confidence Computing
Comprehensive survey covering LLM security and privacy from three perspectives: beneficial applications of LLMs for security, attacks against LLMs, and defensive techniques.
TrustLLM: Trustworthiness in Large Language Models
Lichao Sun, Yue Huang, Haoran Wang + 2 more — ICML 2024
Comprehensive study of LLM trustworthiness across truthfulness, safety, fairness, robustness, privacy, and machine ethics with benchmarks.
Prompt Injection Attack Against LLM-Integrated Applications
Yi Liu, Gelei Deng, Yuekang Li + 6 more — ACM Computing Surveys
First comprehensive survey of prompt injection attacks against LLM-integrated applications, categorizing attacks and defenses with a unified framework.
Adversarial Attacks and Defenses in Large Language Models: Old and New Threats
Leo Schwinn, David Dobre, Stephan Gunnemann + 1 more — arXiv preprint
Systematizes adversarial attacks and defenses for LLMs, connecting them to the classical adversarial ML literature while identifying LLM-specific threats.
A Comprehensive Survey of Attack Techniques, Implementation, and Mitigation Strategies in Large Language Models
Aysan Esmradi, Daniel Wankit Yip, Chun Fai Chan — arXiv preprint
Surveys attack techniques across the LLM lifecycle including training, fine-tuning, and inference, with comprehensive mitigation strategies.
Machine Unlearning for Large Language Models: A Survey
Zheyuan Liu, Guangyao Dou, Zhaoxuan Tan + 2 more — arXiv preprint
Surveys machine unlearning techniques for LLMs including methods for forgetting specific training data, complying with data deletion requests, and maintaining model utility.
The Emerged Security and Privacy of LLM Agent: A Survey with Case Studies
Feng He, Tianqing Zhu, Dayong Ye + 3 more — arXiv preprint
Surveys security and privacy challenges specific to LLM-based agents, covering agent architectures, attack surfaces, and defense mechanisms.
OWASP AI Security and Privacy Guide
Rob van der Veer, OWASP AI Exchange Team — OWASP Foundation
Comprehensive guide for AI security and privacy including threat analysis, controls, and regulatory mapping for AI systems.
Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations (NIST AI 100-2e2025)
Apostol Vassilev, Alina Oprea, Alie Fordyce + 1 more — NIST
NIST's authoritative taxonomy of adversarial ML attacks and mitigations covering evasion, poisoning, privacy, and abuse attacks against AI systems.
Generative AI Security: Theories and Practices
Ken Huang, Yang Wang, Ben Goertzel + 3 more — Springer
Comprehensive textbook covering generative AI security from foundations to advanced topics including LLM threats, defenses, privacy, and governance.
Identifying and Mitigating the Security Risks of Generative AI
Clark Barrett, Brad Boyd, Elie Burzstein + 20 more — Foundations and Trends in Privacy and Security
Comprehensive treatment of generative AI security risks across the ML lifecycle with a focus on practical mitigations and deployment considerations.