Benchmark Instruction Hierarchy: Authority, Utility, and Injection Detection

Instruction hierarchy requires an assistant to preserve authorized rules when lower-trust text requests a conflicting answer. This benchmark isolates that problem in synthetic single-turn tasks with explicit authority cues and deterministic checks. We distinguish full task success from the presence of listed injection text. Scripted controls expose the limits of either measure alone. An additional simulated agent population sweep compares unchecked responses with an ideal blocker that knows the listed attack strings. The simulated blocker can remove detected attack text without recovering the required answer. These experiments validate measurement behavior on a fixed public task set. They do not estimate learned-model ability, deployment security, or human performance. We provide reproducible outputs, uncertainty intervals, and a scoped roadmap for external validation.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-10-29
DOI
https://doi.org/10.5281/zenodo.23752402
Primary Topic
Adversarial Robustness in Machine Learning
Type
preprint
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preprint

Benchmark Instruction Hierarchy: Authority, Utility, and Injection Detection

Pooria Arab
1 links
Zenodo (CERN European Organization for Nuclear Research)
Adversarial Robustness in Machine Learning
preprint

Benchmark Instruction Hierarchy: Authority, Utility, and Injection Detection

Pooria Arab
preprint en

Abstract

Instruction hierarchy requires an assistant to preserve authorized rules when lower-trust text requests a conflicting answer. This benchmark isolates that problem in synthetic single-turn tasks with explicit authority cues and deterministic checks. We distinguish full task success from the presence of listed injection text. Scripted controls expose the limits of either measure alone. An additional simulated agent population sweep compares unchecked responses with an ideal blocker that knows the listed attack strings. The simulated blocker can remove detected attack text without recovering the required answer. These experiments validate measurement behavior on a fixed public task set. They do not estimate learned-model ability, deployment security, or human performance. We provide reproducible outputs, uncertainty intervals, and a scoped roadmap for external validation.

Zenodo (CERN European Organization for Nuclear Research)
Adversarial Robustness in Machine Learning
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