arXiv (All)
open access
Your Mailbox Is Mine: Prompt Injection Attacks Against Real-World LLM Email Agents
Large Language Model (LLM) email agents have emerged as pivotal autonomous assistants, serving as a critical root of trust for digital identity by managing sensitive communications and authentication workflows. Despite their importance, the prompt injection (PI) resilience of the real-world LLM emai
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
- Can Prompt Anonymity Protect Your Identity From LLM Providers?#1118976
- CAITLYN: Can LLM Agents Autonomously Synthesize Defenses against Emerging Injection Attacks?#784763
- CAITLYN: Can LLM Agents Autonomously Synthesize Defenses against Emerging Injection Attacks?#783173
- Prompt Injection Detection for Email Agents Through Attack Chain Modeling#1105216
- Leveraging Soft Prompts for Privacy Attacks in Federated Prompt Tuning#1117295
Record · ID 1036558
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