On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models | Paper Citation Record

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This paper cites Securing Large Language Models: Threats, Vulnerabilities and Responsible Practices.

On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models Securing Large Language Models: Threats, Vulnerabilities and Responsible Practices

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This paper cites From Promise to Peril: Rethinking Cybersecurity Red and Blue Teaming in the Age of LLMs.

On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models From Promise to Peril: Rethinking Cybersecurity Red and Blue Teaming in the Age of LLMs

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On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models Unresolved cited work

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On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models Unresolved cited work

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This paper cites The Best Defense is a Good Offense: Countering LLM-Powered Cyberattacks.

On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models The Best Defense is a Good Offense: Countering LLM-Powered Cyberattacks

Reference 5

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On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models Unresolved cited work

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This paper cites Exploring Autonomous Agents through the Lens of Large Language Models: A Review.

On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models Exploring Autonomous Agents through the Lens of Large Language Models: A Review

Reference 7

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This paper cites Ctrl-Z: Controlling AI Agents via Resampling.

On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models Ctrl-Z: Controlling AI Agents via Resampling

Reference 8

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Observation 362c3b4d-8ff3-42a2-9141-879488d4adf4 · outbound

This paper cites Malice in Agentland: Down the Rabbit Hole of Backdoors in the AI Supply Chain.

On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models Malice in Agentland: Down the Rabbit Hole of Backdoors in the AI Supply Chain

Reference 9

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This paper cites An Interpretable N-gram Perplexity Threat Model for Large Language Model Jailbreaks.

On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models An Interpretable N-gram Perplexity Threat Model for Large Language Model Jailbreaks

Reference 10

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On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models Unresolved cited work

Reference 11

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On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models Unresolved cited work

Reference 12

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This paper cites Agentic Workflows for Conversational Human-AI Interaction Design.

On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models Agentic Workflows for Conversational Human-AI Interaction Design

Reference 13

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This paper cites Dynamic Guided and Domain Applicable Safeguards for Enhanced Security in Large Language Models.

On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models Dynamic Guided and Domain Applicable Safeguards for Enhanced Security in Large Language Models

Reference 14

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On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models Unresolved cited work

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This paper cites A Survey of Adversarial Defenses in Vision-based Systems: Categorization, Methods and Challenges.

On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models A Survey of Adversarial Defenses in Vision-based Systems: Categorization, Methods and Challenges

Reference 16

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This paper cites SecAlign: Defending Against Prompt Injection with Preference Optimization.

On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models SecAlign: Defending Against Prompt Injection with Preference Optimization

Reference 17

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On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models do anything now

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On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models Unresolved cited work

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Observation 7d91ad27-fbb6-4124-82e5-65ee3a358257 · outbound

This paper cites Recent Advances in Attack and Defense Approaches of Large Language Models.

On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models Recent Advances in Attack and Defense Approaches of Large Language Models

Reference 21

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This paper cites MAD-Spear: A Conformity-Driven Prompt Injection Attack on Multi-Agent Debate Systems.

On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models MAD-Spear: A Conformity-Driven Prompt Injection Attack on Multi-Agent Debate Systems

Reference 22

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This paper cites Manipulation Attacks by Misaligned AI: Risk Analysis and Safety Case Framework.

On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models Manipulation Attacks by Misaligned AI: Risk Analysis and Safety Case Framework

Reference 23

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This paper cites Agentic AI Security: Threats, Defenses, Evaluation, and Open Challenges.

On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models Agentic AI Security: Threats, Defenses, Evaluation, and Open Challenges

Reference 24

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This paper cites AgentDojo: A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents.

On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models AgentDojo: A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents

Reference 25

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On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models Agentic Services Computing

Reference 26

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On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models Unresolved cited work

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On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models Unresolved cited work

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On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models Responsible AI Agents

Reference 29

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This paper cites Open Challenges in Multi-Agent Security: Towards Secure Systems of Interacting AI Agents.

On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models Open Challenges in Multi-Agent Security: Towards Secure Systems of Interacting AI Agents

Reference 30

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On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models Unresolved cited work

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On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models Safeguarding Large Language Models: A Survey

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On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models Fuzz-Testing Meets LLM-Based Agents: An Automated and Efficient Framework for Jailbreaking Text-To-Image Generation Models

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On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models Unresolved cited work

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On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models Unresolved cited work

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On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models RAID: A Shared Benchmark for Robust Evaluation of Machine-Generated Text Detectors

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On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models Unresolved cited work

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On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models Unresolved cited work

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On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models Whispers in the Machine: Confidentiality in Agentic Systems

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On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models Babel: Open Multilingual Large Language Models Serving Over 90% of Global Speakers

Reference 40

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On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models The helium spread in the Globular cluster 47 Tuc

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On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models Supporting AI/ML Security Workers through an Adversarial Techniques, Tools, and Common Knowledge (AI/ML ATT&CK) Framework

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On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models Unresolved cited work

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On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models What Does the Bot Say? Opportunities and Risks of Large Language Models in Social Media Bot Detection

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On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models Unresolved cited work

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On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models Unresolved cited work

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On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models RAG-MCP: Mitigating Prompt Bloat in LLM Tool Selection via Retrieval-Augmented Generation

Reference 48

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On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models Navigating the Risks: A Survey of Security, Privacy, and Ethics Threats in LLM-Based Agents

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On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models Unresolved cited work

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On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models LLM Multi-Agent Systems: Challenges and Open Problems

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On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models From Words to Actions: Unveiling the Theoretical Underpinnings of LLM-Driven Autonomous Systems

Reference 53

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On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models Unresolved cited work

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On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models Security of AI Agents

Reference 55

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On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses

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On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models Large Language Model Supply Chain: Open Problems From the Security Perspective

Reference 57

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On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models On the Trustworthiness of Generative Foundation Models: Guideline, Assessment, and Perspective

Reference 58

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On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models Prompting the Unseen: Detecting Hidden Backdoors in Black-Box Models

Reference 59

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On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models Unresolved cited work

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On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models Defending Large Language Models against Jailbreak Attacks via Semantic Smoothing

Reference 61

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On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models A Critical Evaluation of Defenses against Prompt Injection Attacks

Reference 62

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Reference 63

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On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models A Systematization of Security Vulnerabilities in Computer Use Agents

Reference 64

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On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models Flooding Spread of Manipulated Knowledge in LLM-Based Multi-Agent Communities

Reference 65

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On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models Towards a Science of Scaling Agent Systems

Reference 66

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On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models Multi-Agent Security Tax: Trading Off Security and Collaboration Capabilities in Multi-Agent Systems

Reference 67

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On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models Fundamentals of Generative Large Language Models and Perspectives in Cyber-Defense

Reference 68

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On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models Temporal Context Awareness: A Defense Framework Against Multi-turn Manipulation Attacks on Large Language Models

Reference 69

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On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models Wolfpack Adversarial Attack for Robust Multi-Agent Reinforcement Learning

Reference 70

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Reference 71

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On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models Security Concerns for Large Language Models: A Survey

Reference 72

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On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models Targeting the Core: A Simple and Effective Method to Attack RAG-based Agents via Direct LLM Manipulation

Reference 73

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On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models Model-Editing-Based Jailbreak against Safety-aligned Large Language Models

Reference 74

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On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models Attack and defense techniques in large language models: A survey and new perspectives

Reference 75

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On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models UniGuardian: A Unified Defense for Detecting Prompt Injection, Backdoor Attacks and Adversarial Attacks in Large Language Models

Reference 76

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On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models Compromising Embodied Agents with Contextual Backdoor Attacks

Reference 77

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On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems

Reference 79

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This paper cites Automatic and Universal Prompt Injection Attacks against Large Language Models.

On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models Automatic and Universal Prompt Injection Attacks against Large Language Models

Reference 80

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Reference 81

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This paper cites The Landscape of Emerging AI Agent Architectures for Reasoning, Planning, and Tool Calling: A Survey.

On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models The Landscape of Emerging AI Agent Architectures for Reasoning, Planning, and Tool Calling: A Survey

Reference 83

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On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models Investigating Privacy Attacks in the Gray-Box Setting to Enhance Collaborative Learning Schemes

Reference 84

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This paper cites Inadequacies of Large Language Model Benchmarks in the Era of Generative Artificial Intelligence.

On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models Inadequacies of Large Language Model Benchmarks in the Era of Generative Artificial Intelligence

Reference 85

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On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models Adversarial Learning in Statistical Classification: A Comprehensive Review of Defenses Against Attacks

Reference 86

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On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models A Trembling House of Cards? Mapping Adversarial Attacks against Language Agents

Reference 87

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Reference 88

Resolution: verified exact; doi, observed 2026-08-15T14:21:44.707023Z

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This paper cites Stealthy Jailbreak Attacks on Large Language Models via Benign Data Mirroring.

On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models Stealthy Jailbreak Attacks on Large Language Models via Benign Data Mirroring

Reference 89

Resolution: verified exact; local_arxiv, observed 2026-08-15T14:21:44.640548Z

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On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models Securing Agentic AI: A Comprehensive Threat Model and Mitigation Framework for Generative AI Agents

Reference 90

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On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models The Attacker Moves Second: Stronger Adaptive Attacks Bypass Defenses Against Llm Jailbreaks and Prompt Injections

Reference 91

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On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models Rethinking Autonomy: Preventing Failures in AI-Driven Software Engineering

Reference 92

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On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models Unresolved cited work

Reference 93

Resolution: verified exact; doi, observed 2026-08-15T14:21:44.568139Z

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Observation b18b2425-c7e8-4444-9bcd-ae47ba148e1f · outbound

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On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models Soft Begging: Modular and Efficient Shielding of LLMs against Prompt Injection and Jailbreaking based on Prompt Tuning

Reference 94

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On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models Measuring Agents in Production

Reference 95

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On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models Unresolved cited work

Reference 96

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This paper cites Real AI Agents with Fake Memories: Fatal Context Manipulation Attacks on Web3 Agents.

On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models Real AI Agents with Fake Memories: Fatal Context Manipulation Attacks on Web3 Agents

Reference 97

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On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models Saltzer & Schroeder for 2030: Security engineering principles in a world of AI

Reference 98

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On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models Automated Red Teaming with GOAT: the Generative Offensive Agent Tester

Reference 99

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On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models Single-Pulse Gamma-Ray Bursts have Prevalent Hard-to-Soft Spectral Evolution

Reference 100

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On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models Jailbreaking and Mitigation of Vulnerabilities in Large Language Models

Reference 101

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On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models Ignore Previous Prompt: Attack Techniques For Language Models

Reference 102

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On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models Stochastic-Shield: A Probabilistic Approach Towards Training-Free Adversarial Defense in Quantized CNNs

Reference 103

Resolution: verified exact; local_arxiv, observed 2026-08-15T14:21:44.418131Z

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Observation df73cb37-0c7b-4e25-9751-b931ded22cc1 · outbound

This paper cites Tricking LLMs into Disobedience: Formalizing, Analyzing, and Detecting Jailbreaks.

On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models Tricking LLMs into Disobedience: Formalizing, Analyzing, and Detecting Jailbreaks

Reference 104

Resolution: unresolved; no resolver link, observed 2026-08-15T14:21:42.411193Z

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