Prompt-injection research
Assessment outcome
total 3/6 (actionability 0 + authority 1 + currency 2)
Eligibility gates
HTTP 200, 779 words of full text extracted
The source is an academic paper entirely about Large Language Models and adversarial prompt injection attacks.
Addresses security risks, vulnerabilities, and safety implications of LLM integrations.
The paper explicitly lists the academic authors who produced the research.
No indicators of supersession, retraction, or repeal are present on the page.
Scored criteria
Actionability
0/2Anchor 0: Provides description and analysis only of indirect prompt injection attacks without naming specific binding obligations.
Authority
1/2Anchor 1: Written by individual researchers/practitioners published as an unreviewed preprint on arXiv.
Authors: Kai Greshake, Sahar Abdelnabi, Shailesh Mishra, Christoph Endres, Thorsten Holz, Mario Fritz
Currency
2/2computed: newest date 2026-08-11 (via http-header), ~0.0y old; within the 60-month academic_research window for perspective 'Academic' → 2