INFUSE: A Unified Surface Benchmark for Indirect Prompt Injection in Agentic Systems

Reported by arXiv cs.CR (Thuy Dung Nguyen, Jingyi Guo, Rana Seda Dubauskas, Sreepriya Damuluru, Taylor T. Johnson, Kevin Leach)

Summary by Cyber Security News16 Oct 2026, 06:00 CEST

About this summary. This is a short, independently written summary of an article first published by arXiv cs.CR. Cyber Security News did not report or verify the underlying story. Read the original: https://arxiv.org/abs/2610.20776

arXiv:2610.20776v1 Announce Type: new Abstract: LLM-based agents increasingly execute long-horizon, end-to-end workflows involving shell commands, code modification, dependency installation, and interaction with remote services. This growing autonomy expands their exposure to indirect prompt injection (IPI), where adversarial instructions embedded in external content are interpreted as legitimate task directives.

Read the full article at arXiv cs.CR →

Source attribution: headline and facts are from arXiv cs.CR (arxiv.org). Summary method: excerpt of the source description. See our source attribution policy.

Related coverage

APEX: Active Protection at Execution Boundaries for LLM Agents

Defusing Explosive Prompts: Understanding and Preventing Trigger-Based Prompt Injections in LLM Agents

GitHub adopts cheaper AI model to catch secrets developers leave in code

MemForm: Benchmarking Memory Formation Poisoning in LLM Agents

Inside the Exchange Inspector: How Tenable uses OpenAI GPT cyber models to review open-source AI agents

ASPIRE: Agentic Safety & Prompt Injection Red-teaming Engine