{"total":34,"rows":[{"id":"2503.21460","title":"Large Language Model Agent: A Survey on Methodology, Applications and Challenges","created":"2025-03-27","cited_by_count":205,"area":"agents"},{"id":"2504.18575","title":"WASP: Benchmarking Web Agent Security Against Prompt Injection Attacks","created":"2025-04-22","cited_by_count":157,"area":"agents"},{"id":"2506.14866","title":"OS-Harm: A Benchmark for Measuring Safety of Computer Use Agents","created":"2025-06-17","cited_by_count":111,"area":"agents"},{"id":"2503.09648","title":"A Survey on Trustworthy LLM Agents: Threats and Countermeasures","created":"2025-03-12","cited_by_count":96,"area":"agents"},{"id":"2507.06134","title":"OpenAgentSafety: A Comprehensive Framework for Evaluating Real-World AI Agent Safety","created":"2025-07-08","cited_by_count":75,"area":"agents"},{"id":"2505.21936","title":"RedTeamCUA: Realistic Adversarial Testing of Computer-Use Agents in Hybrid Web-OS Environments","created":"2025-05-28","cited_by_count":46,"area":"agents"},{"id":"2510.04885","title":"RL Is a Hammer and LLMs Are Nails: A Simple Reinforcement Learning Recipe for Strong Prompt Injection","created":"2025-10-06","cited_by_count":36,"area":"agents"},{"id":"2512.23738","title":"Enforcing Temporal Constraints for LLM Agents","created":"2025-12-25","cited_by_count":34,"area":"agents"},{"id":"2506.23844","title":"A Survey on Autonomy-Induced Security Risks in Large Model-Based Agents","created":"2025-06-30","cited_by_count":29,"area":"agents"},{"id":"2510.09781","title":"Building a Foundational Guardrail for General Agentic Systems via Synthetic Data","created":"2025-10-10","cited_by_count":29,"area":"agents"},{"id":"2510.23883","title":"Agentic AI Security: Threats, Defenses, Evaluation, and Open Challenges","created":"2025-10-27","cited_by_count":28,"area":"agents"},{"id":"2502.20383","title":"Why Are Web AI Agents More Vulnerable Than Standalone LLMs? A Security Analysis","created":"2025-02-27","cited_by_count":27,"area":"agents"},{"id":"2506.13666","title":"We Should Identify and Mitigate Third-Party Safety Risks in MCP-Powered Agent Systems","created":"2025-06-16","cited_by_count":27,"area":"agents"},{"id":"2506.01616","title":"MLA-Trust: Benchmarking Trustworthiness of Multimodal LLM Agents in GUI Environments","created":"2025-06-02","cited_by_count":23,"area":"agents"},{"id":"2510.06445","title":"A Survey on Agentic Security: Applications, Threats and Defenses","created":"2025-10-07","cited_by_count":20,"area":"agents"},{"id":"2601.09923","title":"CaMeLs Can Use Computers Too: System-level Security for Computer Use Agents","created":"2026-01-14","cited_by_count":17,"area":"agents"},{"id":"2603.15714","title":"How Vulnerable Are AI Agents to Indirect Prompt Injections? Insights from a Large-Scale Public Competition","created":"2026-03-16","cited_by_count":17,"area":"agents"},{"id":"2603.20625","title":"ACRFence: Preventing Semantic Rollback Attacks in Agent Checkpoint-Restore","created":"2026-03-21","cited_by_count":10,"area":"agents"},{"id":"2606.10749","title":"Toward Secure LLM Agents: Threat Surfaces, Attacks, Defenses, and Evaluation","created":"2026-06-09","cited_by_count":7,"area":"agents"},{"id":"2608.02657","title":"Your Agentic LLMs Secretly Encode Indirect Prompt-Injection Exposure in Hidden States","created":"2026-08-01","cited_by_count":7,"area":"agents"},{"id":"2509.24240","title":"Takedown: How It's Done in Modern Coding Agent Exploits","created":"2025-09-29","cited_by_count":6,"area":"agents"},{"id":"2605.23989","title":"Towards trustworthy agentic AI: a comprehensive survey of safety, robustness, privacy, and system security","created":"2026-05-17","cited_by_count":5,"area":"agents"},{"id":"2510.06222","title":"Inducing State Anxiety in LLM Agents Reproduces Human-Like Biases in Consumer Decision-Making","created":"2025-08-30","cited_by_count":3,"area":"agents"},{"id":"2601.10758","title":"Agents at Risk: How Users Unwittingly Undermine LLM Safety","created":"2026-01-14","cited_by_count":3,"area":"agents"},{"id":"2603.18762","title":"ClawTrap: A MITM-Based Red-Teaming Framework for Real-World OpenClaw Security Evaluation","created":"2026-03-19","cited_by_count":3,"area":"agents"},{"id":"2604.08005","title":"Preference Redirection via Attention Concentration: An Attack on Computer Use Agents","created":"2026-04-09","cited_by_count":3,"area":"agents"},{"id":"2604.12461","title":"CIA: Inferring the Communication Topology from LLM-based Multi-Agent Systems","created":"2026-04-14","cited_by_count":3,"area":"agents"},{"id":"2607.05277","title":"Untrusted Content Masking for Web Agents with Security Guarantees","created":"2026-07-06","cited_by_count":3,"area":"agents"},{"id":"2606.13385","title":"Who Pays the Price? Stakeholder-Centric Prompt Injection Benchmarking for Real-world Web Agents","created":"2026-06-11","cited_by_count":2,"area":"agents"},{"id":"2601.20375","title":"LLM-AutoDP: Automatic Data Processing via LLM Agents for Model Fine-tuning","created":"2026-01-28","cited_by_count":1,"area":"agents"},{"id":"2605.24069","title":"When the Manual Lies: A Realistic Benchmark to Evaluate MCP Poisoning Attacks for LLM Agents","created":"2026-05-22","cited_by_count":1,"area":"agents"},{"id":"2608.02018","title":"Invisible Ink Threats: Adversarial Goals Behind Legitimate Tasks in Computer-Use Agents","created":"2026-08-03","cited_by_count":1,"area":"agents"},{"id":"2510.27140","title":"Measuring the Security of Mobile LLM Agents under Adversarial Prompts from Untrusted Third-Party Channels","created":"2025-10-31","cited_by_count":0,"area":"agents"},{"id":"2606.28666","title":"Why Trust Your Agent? Empirical Security Gains from TRiSM-Guided Agentic Workflows in Healthcare","created":"2026-06-27","cited_by_count":0,"area":"agents"}],"next_cursor":null}