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Prompt Injection Detection for Email Agents Through Attack Chain Modeling
arXiv cs.CL — Computation and Language
Factual evidence
What the source reports
Researchers propose an attack chain modeling framework to detect indirect prompt injection in LLM email assistants during tool use.
Inspect the evidence
- Inclusion basis
- Enterprise AI
- Publisher and source type
- arXiv cs.CL — Computation and Language · RESEARCH
- Published by source
- 28 September 2026
- Collected by OneBench
- 29 Sept 2026, 03:01 UK
Stored source excerpt
arXiv:2609.30657v1 Announce Type: cross Abstract: Large language model email assistants are particularly vulnerable to indirect prompt injection because untrusted email content can be retrieved into…
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The factual summary is a OneBench synthesis, not a quotation or independent verification. Collection time is not publication time. Open the source for its full context; related reporting can share the same underlying announcement.
OneBench interpretation
Institutional assessment
So what
Multi-step attack chain detection addresses security risks in autonomous agent workflows that standard binary text classifiers fail to catch.
Do what
Review prompt injection controls with the team responsible for agentic workflow security.