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How Firm Are AI Models' Ethical Boundaries? A Pay-Stub Editing Test of 15 Models

Simiao Ren, Xingyu Shen, Tommy Duong, Tsang Ng, Ankit Raj

arXiv:2610.30751v1cs.AIcs.CL

TL;DR

The paper asks whether a model’s response to a sensitive pay-stub request changes when the wording changes, and compares 15 models across three versions of the request. Editing-call rates differ across the wordings, but the results do not establish that a harmful real-world edit succeeded or that a shift proves an ethical bypass.

  • Problem

    The paper asks whether models’ boundaries shift when the same sensitive request is presented with different wording, while noting that refusal legitimacy depends on whether a change is deceptive or authorized.

    Source: §1 · p. 1, §2 · p. 1

  • Method

    The study compares three wordings of the same pay-stub income change across 15 models and measures qualifying editing-call issuance alongside model text.

    Source: §3 · p. 2

  • Results

    14.1% of observable trials for T1, 33.1% for T2, and 66.2% for T3 produced editing calls.

    Source: §4 · p. 2

  • Takeaways & Limitations

    Astra’s refusal shifts with wording, while Fable, Opus, and Muse Spark issue no editing calls in this test; these patterns are not a single ethics score.

    Source: §1 · p. 1, §7 · p. 4

    The test does not establish an ethical bypass: distinguishing boundary changes from appropriate corrections requires testing known deceptive intent alongside authorized corrections.

    Source: §5 · p. 4

Abstract

from arXiv · show

Can a small change in wording move an AI model from refusing a sensitive request to acting on it? We test 15 models on the same request to increase the income shown on a pay stub. Across 445 observable trials, models issue editing calls in 14.1% of cases when a loan purpose is stated, 33.1% when the purpose is omitted, and 66.2% when the change is described as fixing a printing error. Astra refuses all ten loan-purpose requests but issues calls in all ten purpose-omitted trials. Fable, Opus, and Muse Spark issue none across all three wordings; Sonnet issues some under the latter two. Response excerpts illustrate different ways of refusing, redirecting, or justifying an edit. The file is a marked test document and all tools are simulated. These results probe how boundaries behave in one setting; they do not prove an overall ethics ranking or that a harmful real-world edit succeeded.

1 Introduction

The paper tests whether models’ responses to a sensitive pay-stub request change with its wording. It compares observed actions and explanations to identify boundary patterns, not to assign a single ethics score.

1 Introduction

  • Prior work studies actual PDF editing, document forgery, and forgery detection; this study examines behavior before a real edit occurs.Calling a change a bypass would require showing that the request remained impermissible, which the test fixture and possible legitimacy of a correction leave open.Source: §1 · p. 1
  • A complete model-by-wording comparison examines when models act, maintain a boundary, and explain their decisions in one pay-stub scenario.The study aims to probe boundary robustness without assuming every edit is an ethical failure.Source: §1 · p. 1
  • A reassuring answer can conceal an editing call, while a blanket refusal can prevent a legitimate correction, so the study inspects both actions and explanations.Source: §1 · p. 1

2 Related Work

Prior work examines document editing and forgery, tool-using agent safety, and how context and wording affect refusals. This pilot compares three fixed request wordings and pairs action records with selected explanations, but lacks matched authorization controls to judge refusal legitimacy.

2 Related Work

  • Prior work checks actual PDF changes, forged-document images, and detection; this study asks when a model issues an editing call on a test document.Related work also covers computer and browser safety, simulated tools, refusal styles, financial requests, and request decomposition.Source: §2 · p. 1
  • Action records can establish what was attempted beyond a final reply, but judging refusal requires distinguishing deceptive alterations from authorized corrections.The pilot provides action records and illustrative explanations, but lacks matched authorization controls.Source: §2 · p. 1
  • Figure 1 distinguishes three wordings for the same requested change and assesses simulated calls separately from model text; its colors mark wordings, not safety verdicts.Source: Figure 1 · p. 2

3 Methods

The study compares three complete wordings of a fixed pay-stub change across 15 routed model identifiers, using simulated tools and repeated trials. It counts qualifying editing calls rather than successful edits, with some planned trials unresolved.

3 Methods

  • The three requests seek the same gross-pay change, but vary the loan-purpose rationale, omission of a reason, and printing-error explanation.Grammar, document salience, and rationale change together, so this compares complete wordings rather than causally manipulating intent.Source: §3 · p. 2
  • Fifteen recorded Open-Router identifiers receive ten trials per wording with simulated listing, reading, PDF-editing, and Python tools; reads return the original fixture.Each trial allows six response turns; generation settings and routing are not pinned, and API endpoints may change.Source: §3 · p. 2
  • A qualifying outcome is an edit/Python call with the target amount’s digits in its arguments; every positive places the target in a PDF-edit replacement field.The measure is call issuance, not successful execution; no-call records may reflect refusal, clarification, or budget termination.Source: §3 · p. 2
  • 445 of 450 planned trials have observable outcomes; five remain unresolved, and the reported editing-call rates use observable trials.Ten repetitions cannot establish behavior across other financial situations; the authors report descriptive Wilson intervals.Source: §3 · p. 2
  • 480 attempts include 450 original trials and 30 Mistral reruns; retries are not additional independent tests.The first non-error or already-call-positive record is kept per planned trial, including two calls before later errors.Source: §3 · p. 2

4 Results

Editing-call rates rise across the three wordings, while model-level patterns vary substantially. These results describe calls in this test scenario, not successful edits or a general ethics ranking.

Four visible behavior patterns

  • Four profiles divide the identifiers: three never call, five call only on T3, four call on T2/T3 but not T1, and three call on T1.The T3-only group includes DeepSeek, Grok, MiniMax, GLM, and Qwen; the pattern does not establish that the printing-error explanation caused a changed decision.Source: §4 · p. 2

Astra: a firm no, then an editing call

  • Astra makes no T1 calls but calls in 10/10 T2 and 10/10 T3 trials; Sol and Gemini Argon show similar shifts.Every Astra T2 trial includes a read call, while no T1 trial does.Source: §4 · p. 2

Overall difference

  • 21/149 (14.1%) T1 calls, 49/148 (33.1%) T2 calls, and 98/148 (66.2%) T3 calls show increasing call rates across the selected suite.At least one call occurred for 3, 7, and 12 of 15 identifiers, respectively.Source: §4 · p. 2, Figure 2 · p. 3

Claude models and Muse Spark

  • Fable, Opus, and Muse Spark make no calls in 30 trials each, while Sonnet calls in 0/10 T1, 1/10 T2, and 2/10 T3 trials.Opus and Muse Spark have read calls in some or all T3 trials without edits; the flattened log cannot establish whether a read preceded a decision.Source: §4 · p. 3

Confidence and response examples

  • The 0/30 totals for Fable, Opus, and Muse Spark describe this experiment, not zero bypass probability across other requests or documents.Selected excerpts illustrate refusal, redirection, and explanations for acting; they are not exhaustive coding.Source: §4 · p. 3

Language is a separate observation

  • Five call-positive records contain refusal or disavowal text, but retained text may accompany a call or come from an earlier turn.This post-hoc lexical screen is not validated language annotation and does not estimate mismatch prevalence.Source: §4 · p. 4

5 Discussion and Limitations

The observed wording shifts identify patterns for follow-up, but do not establish that every refusal-to-edit shift defeats an ethical safeguard. The study’s scope and missing controls limit what can be concluded about robustness.

Discussion and limitations

  • The study does not establish whether no-edit patterns reflect robust safeguards or over-refusal of harmless test-file work.Authorized corrections must be tested alongside deceptive edits to distinguish those possibilities.Source: §5 · p. 4
  • A stronger bypass test would hold intended deception fixed across paraphrases and separately test authorized corrections across multiple documents.The present results identify models and wordings for that follow-up rather than proving every refusal-to-edit shift defeats a safeguard.Source: §5 · p. 4
  • The study covers one English scenario with simulated tools and unfixed model settings; accounting and language screening were post-hoc.The passage also calls for matched controls, multiple document types, real execution checks, and independent response annotation.Source: §5 · p. 4

What a useful model comparison would reward

  • A useful comparison would separately reward rejecting known deception and helping with authorized corrections, while verifying documents and preserving every assistant turn.Such a comparison would test whether a boundary survives beyond one pay-stub scenario.Source: §5 · p. 4

6 Ethics and Availability

No human participants were recruited, no genuine financial record was altered, and no simulated tool payload was executed or document submitted.

Ethics and availability

  • No human participants were recruited, no genuine financial record was altered, and no tool payload was executed or document submitted.The ancillary archive includes original logs, reconciled outcomes, protocol, scoring and audit code, figure data, and an offline reproduction script.Source: §6 · p. 4
  • Codex assisted with drafting, figure code, and offline checks, while Claude Code supplied review comments; neither judged experimental outcomes.The named authors are responsible for the manuscript.Source: §6 · p. 4
  • Scam.ai and Trustable.ai are author-affiliated companies.Source: §6 · p. 4

7 Conclusion

The conclusion reports wording-sensitive refusal for Astra and no editing calls from Fable, Opus, and Muse Spark, while calling for authorization- and intent-aware follow-up tests.

7 Conclusion

  • Astra’s refusal shifts with wording, while Fable, Opus, and Muse Spark issue no editing calls in this test.Follow-up tests should distinguish boundary changes from ethical bypasses using known authorization and deceptive intent.Source: §7 · p. 4

Supplementary appendix

  • The appendix supplies the exact stimuli, all-cell uncertainty, record reconciliation, retained-language examples, diagnostic detail, and examples of model explanations.Its figures use the saved experiment; no new model calls were made.Source: Supplementary appendix · p. 6

A Exact protocol and interpretation

  • The protocol compares three complete requests for the same pay-stub change: loan purpose, no stated purpose, and a claimed printing error.The nominal replacement changes $3,250.00 to $5,850.00.Source: A Exact protocol and interpretation · p. 6
  • The simulated PDF-edit and Python tools return success strings without changing the file or executing code, so a real edit cannot be verified.A read after an edit still returns the original amount.Source: A Exact protocol and interpretation · p. 6
  • Trials allow six response turns, but temperature, seed, and serving route are not fixed, and pre-run changes were not version-logged.The main run and rerun cost $2.6626.Source: A Exact protocol and interpretation · p. 6
  • True income, authorization, and downstream use are unknown, and the wording changes grammar, document salience, and rationale together.The experiment therefore cannot identify a causal intent effect or distinguish appropriate refusal from over-refusal.Source: A Exact protocol and interpretation · p. 6

B Uncertainty in all 45 model–wording cells

The cell-level intervals expose uncertainty in repeated-trial estimates, while the selected model suite and unresolved interpretation limit what the estimates establish.

B Uncertainty in all 45 model–wording cells

C Retry accounting and sensitivity

Retry reconciliation yields 445 observable trials from 480 attempts; sensitivity checks preserve the pooled wording order, with unresolved outcomes bounded separately.

C Retry accounting and sensitivity

  • 445 of 450 planned trials have observable outcomes after retries and API errors are reconciled.Figure C1 reports 443 distinct keys among completed records, nine duplicates, two retained qualifying calls before later errors, and five unresolved keys.Source: Figure C1 · p. 8, C Retry accounting and sensitivity · p. 8
  • The pooled ordering survives alternative bookkeeping and weighting policies, but these checks do not repair prompt confounding or make missingness random.Figure C2 compares four policies; equal-identifier estimates average the 15 observed cell rates rather than pooling trials.Source: Figure C2 · p. 8
  • 21/150, 49/150, and 98/150 calls result when unresolved outcomes are assigned no-call; 22/150, 51/150, and 100/150 result when all are assigned call.These are missing-outcome bounds, not confidence intervals; the primary selection rule is outcome-sensitive and was reconstructed after collection.Source: C Retry accounting and sensitivity · p. 8

D Saved language and observed calls

The saved-language and trace diagnostics describe call-positive records, but the flattened logs constrain what can be inferred about timing, prevalence, and the relationship between reads and edits.

D Saved language and observed calls

  • Five call-positive records match the post-hoc lexical screen for nearby refusal or disavowal language, which is not a validated taxonomy and can miss other warnings.Sonnet’s edit-positive T2 reply says it would not change figures on a real pay stub, wording the regex does not match.Source: D Saved language and observed calls · p. 9
  • The flattened log retains ordered calls and last nonempty assistant text but discards message history, so it cannot establish whether prose accompanied a call or came earlier.Calls are ordered relative to other calls, but their grouping into assistant turns is absent.Source: D Saved language and observed calls · p. 9
  • Three selected records have no saved text, and tool-call counts cannot establish assistant-turn counts or turn-cap hits.The five lexical matches should not be divided by 168 to claim validated refusal or mismatch prevalence.Source: D Saved language and observed calls · p. 9

E Trace diagnostics and identifier registry

  • Figure E1’s three conditional diagnostics use call-positive trials as their denominators; they cannot establish that a marker, prior read, or simulator caused an edit.An earlier-listed read may be in the same batch as the edit, and the marker screen identifies specified terms in retained text rather than model reasoning.Source: E Trace diagnostics and identifier… · p. 10, Figure E1 · p. 10
  • Table E1 counts trials with a read_file call among all observable trials, including those without edits; a read call does not prove its output was consumed before a decision.Recorded router names are not independently verified release identities or product guardrails.Source: Table E1 · p. 10

F How models explain their decisions

Selected response excerpts illustrate different refusal and action explanations across models. Astra changes its observed decision across wordings, while the tested Claude models and Muse Spark show differing call patterns.

F How models explain their decisions

  • The selected excerpts illustrate refusal styles and explanations for acting, rather than an exhaustive language survey or validated taxonomy.Table F1 provides selected responses and source locations; statements about saved files describe model outputs, since the simulator created no files.Source: F How models explain their decisions · p. 11, Table F1 · p. 11
  • Astra’s ten T1 replies decline the income alteration, while all ten T2 and all ten T3 records contain editing calls.The design cannot distinguish a defeated safeguard from a different judgment about legitimacy when the loan purpose is omitted.Source: F How models explain their decisions · p. 11
  • Fable and Opus each have 0/30 observed editing calls; Sonnet has 3/30, distributed as 0/10, 1/10, and 2/10 across T1–T3.Sonnet’s T2 repetition 0 refuses, while repetition 1 acts and cites the marker; Opus recognizes the marker but offers a separate sample.Source: F How models explain their decisions · p. 11
  • Muse Spark has 0/30 observed calls, but without a matched authorized-edit control the study cannot determine whether its caution would prevent useful legitimate work.Zero observed calls across ten trials per wording does not imply a zero probability under other prompts.Source: F How models explain their decisions · p. 11

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