arXiv (All)
open access
Business Compromise Detection with Agentic AI and LLM-driven Knowledge Discovery
Detecting compromised business ad accounts is a challenge in digital advertising, as attackers exploit hijacked accounts to launch fraudulent campaigns. Large Language Model (LLM) agents show promise for integrity enforcement, but hallucinated mistakes on hard cases create business friction. In a st
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Usable Agent Discovery for Decentralized AI Systems#138904
- SoK: Rethinking Jailbreaking in the Era of Agentic AI: Attacks, Defenses, and Practical Consideration#1000363
- Evaluating AI Models' Capability to Automate Voice Phishing Attacks#363202
- SENTINEL-RL: Offloading Topological Reasoning from LLM Agents in the Security Operations Center#927025
- Agent-Facing Information Design in LLM Tool Registries: A Preregistered Test of Rhetoric, Position and Structure#1143778
Record · ID 1116974
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive