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20242026
most citedComparing Rationality Between Large Language Models and Humans: Insights and Open Questions

3 citations · 5 across the 11 of their papers we have counts for

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cs.CR2026

Reachability-Based Capability Confinement for LLM Agents under Indirect Prompt Injection

Wujie Xiong, Rabimba Karanjai, Yang Lu +2

Large language model agents place outputs from external skills into their execution context, allowing attacker-controlled data to influence later privileged actions. Existing defen…

cs.CR2026

When Agents Act on Web3: An Attack-Surface Survey of MCP, Skills, and Tool Calling

Rabimba Karanjai, Yang Lu, Nour Diallo +4

AI agents increasingly act rather than merely read: across the Model Context Protocol (MCP) ecosystem, the share of deployed tools that modify external state has risen from 27% to…

cs.CR2026

PACE: Policy-Attested Contract Execution for Safe AI Agents in Decentralized Finance

Rabimba Karanjai, Yang Lu, Richard Williamson +5

Autonomous AI agents are emerging as interfaces for decentralized finance (DeFi) actions such as swaps, lending operations, and yield management. Because these agents rely on large…

cs.CR2026

Context Contamination in LLM Analysis of Network Security Logs: Poison with Passive Prompt Injection and Mitigation Evaluation

Rabimba Karanjai, Yang Lu, Hemanth Hegadehalli Madhavarao +2

Large Language Models are increasingly deployed in Security Operations Centers for log analysis tasks including summarization, alert triage, and threat investigation. These systems…

cs.CR2026

zkRansomware: Proof-of-Data Recoverability and Multi-round Game Theoretic Modeling of Ransomware Decisions

Xinyu Hou, Yang Lu, Rabimba Karanjai +2

Ransomware is still one of the most serious cybersecurity threats. Victims often pay but fail to regain access to their data, while also facing the danger of losing data privacy. T…