Prompt Injection as a Governance Object: Why Prompt Injection, Data, Model, and Memory Poisoning Are Likely to Be Governed as Security Duties Before They Are Criminalised, and a Proposed Ladder That Explains the Sequence

This paper asks whether prompt injection, data poisoning, model poisoning, and the poisoning of persistent artificial intelligence memory should be made illegal, and how each is otherwise becoming a governance object. It argues that the answer is likely no in the narrow sense and yes in the broad one. The review identified no enacted standalone offence or legislative proposal as at the cutoff, and the evidence suggests that a dedicated offence of prompt injection is unlikely in the near term, because the technique is not meaningfully separable from ordinary input, because the same manipulation is indispensable to legitimate security research, and because the vulnerability is largely architectural rather than a property of the attacker's conduct. The duty to defend against these attacks, by contrast, is already forming through security standards, management-system certification, sectoral regulation, and civil liability on the organisations that build and deploy artificial intelligence. To organise the analysis, the paper proposes the Governance Escalation Ladder, a seven-stage heuristic describing a sequence through which a new class of attack may move from technical recognition to eventual legal consequence. The paper locates these attack classes provisionally on that sequence, evidences each stage against current frameworks and law, illustrates the pattern against a comparable legislative choice in a second legal domain, and closes with staged guidance for governance leaders. The contribution is interpretive and organisational rather than doctrinal. Every legal status, standard, demonstration, and item of litigation cited is dated and qualified, and a source-verification register is provided as an appendix.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-07-30
DOI
https://doi.org/10.5281/zenodo.21705077
Primary Topic
Ethics and Social Impacts of AI
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Prompt Injection as a Governance Object: Why Prompt Injection, Data, Model, and Memory Poisoning Are Likely to Be Governed as Security Duties Before They Are Criminalised, and a Proposed Ladder That Explains the Sequence

MAPHI BAYOLO
Zenodo (CERN European Organization for Nuclear Research)
Ethics and Social Impacts of AI
article

Prompt Injection as a Governance Object: Why Prompt Injection, Data, Model, and Memory Poisoning Are Likely to Be Governed as Security Duties Before They Are Criminalised, and a Proposed Ladder That Explains the Sequence

MAPHI BAYOLO
article en

Abstract

This paper asks whether prompt injection, data poisoning, model poisoning, and the poisoning of persistent artificial intelligence memory should be made illegal, and how each is otherwise becoming a governance object. It argues that the answer is likely no in the narrow sense and yes in the broad one. The review identified no enacted standalone offence or legislative proposal as at the cutoff, and the evidence suggests that a dedicated offence of prompt injection is unlikely in the near term, because the technique is not meaningfully separable from ordinary input, because the same manipulation is indispensable to legitimate security research, and because the vulnerability is largely architectural rather than a property of the attacker's conduct. The duty to defend against these attacks, by contrast, is already forming through security standards, management-system certification, sectoral regulation, and civil liability on the organisations that build and deploy artificial intelligence. To organise the analysis, the paper proposes the Governance Escalation Ladder, a seven-stage heuristic describing a sequence through which a new class of attack may move from technical recognition to eventual legal consequence. The paper locates these attack classes provisionally on that sequence, evidences each stage against current frameworks and law, illustrates the pattern against a comparable legislative choice in a second legal domain, and closes with staged guidance for governance leaders. The contribution is interpretive and organisational rather than doctrinal. Every legal status, standard, demonstration, and item of litigation cited is dated and qualified, and a source-verification register is provided as an appendix.

Zenodo (CERN European Organization for Nuclear Research)
Peace, Justice and strong institutions
Openalex Percentile: Top 6%
Ethics and Social Impacts of AI
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.