arxiv: 2604.26052 · v2 · submitted 2026-04-28 · 💻 cs.CL

Recognition: unknown

From Prompt Risk to Response Risk: Paired Analysis of Safety Behavior of Large Language Model

Mengya Hu , Qiong Wei , Sandeep Atluri

Authors on Pith no claims yet

Pith reviewed 2026-05-07 16:16 UTC · model grok-4.3

classification 💻 cs.CL

keywords LLM safety evaluationharm severity transitionpaired prompt-response analysiscontent safety taxonomysexual content persistencehelpfulness-harmlessness tradeoffresponse risk

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The pith

LLM responses de-escalate harm severity from the prompt in 61 percent of cases, with sexual content persisting three times more often than hate or violence.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper shifts safety evaluation from binary pass-fail checks to a paired view of how harm levels actually change between a user's prompt and the model's reply. Using 1250 labeled records across hate, sexual, violence, and self-harm categories with ordinal severity, it reports that models lower harm most of the time, hold the same level about a third of the time, and raise it in only 3 percent of cases. Sexual content proves especially sticky because models continue it when the prompt already contains it, rather than introducing new sexual material from neutral inputs. The same analysis also tracks response relevance and finds that escalated-harm replies are always high-relevance while medium-severity replies often drift off-topic, illustrating a concrete helpfulness-harmlessness tension.

Core claim

Through paired transition analysis of 1250 prompt-response records labeled with four harm categories and ordinal severity levels aligned to the Azure AI Content Safety taxonomy, 61 percent of responses de-escalate harm relative to the prompt, 36 percent preserve the same severity, and 3 percent escalate. A per-category persistence and drift-up decomposition shows sexual content is three times harder to de-escalate than hate or violence, driven by persistence on already-sexual prompts rather than introduction of new sexual harm from benign inputs. Joint relevance measurement reveals that all compliance-escalation cases from non-zero prompts are high-relevance on-task content, while medium-sev

What carries the argument

The paired transition analysis that tracks ordinal harm severity changes from prompt to response, together with per-category persistence/drift-up decomposition and joint relevance scoring.

If this is right

Binary safety metrics such as refusal rate or harmful/not-harmful classification miss the dominant pattern of harm reduction or stability.
Sexual content requires targeted handling because its persistence rate is driven by continuation rather than new introduction.
Escalated-harm responses occur only when relevance remains high, showing that increased severity can accompany fully on-task output.
Medium-severity replies in violence and sexual categories exhibit the lowest relevance due to tangential elaborations.
Safety evaluations should incorporate transition tracking to capture how risk actually moves between input and output.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The method could be reused to benchmark whether particular training techniques reduce sexual persistence more than others.
If lower-severity responses systematically lose relevance, users may trade satisfaction for safety in everyday use.
The low overall escalation rate suggests existing models already avoid introducing new harm in most cases, so future gains may come from better continuity control.
Applying the same paired lens to multi-turn dialogues could show whether harm tends to accumulate or resolve across exchanges.

Load-bearing premise

The human labels on the 1250 records accurately and consistently reflect true harm severity levels under the Azure taxonomy without significant bias or disagreement that would change the reported transition percentages.

What would settle it

Independent re-annotation of the same 1250 prompt-response pairs by new raters that produces materially different de-escalation rates or category-specific persistence numbers would falsify the central distributions.

Figures

Figures reproduced from arXiv: 2604.26052 by Mengya Hu, Qiong Wei, Sandeep Atluri.

**Figure 1.** Figure 1: Aggregate prompt→response max-severity transition matrix. Off-diagonal mass above the diagonal = de-escalation; below = escalation. Escalation audit. Manual inspection of the 40 escalation cases surfaces two recurring mechanisms: (1) unsolicited elaboration :a benign or lowharm prompt triggers a response that adds harmful detail not requested; (2) compliance escalation : an already-harmful prompt is answ… view at source ↗

**Figure 2.** Figure 2: Per-category 4×4 prompt→response severity transition matrices. Cells show raw counts; diagonal = severity preserved; above diagonal = de-escalation; below = escalation. Sexual content has the most mass in off-diagonal persistence cells at severity ≥ 1; Violence and Hate show more drift-up (bottom row, non-zero response severity). responses exceed 500 characters, 50.1% exceed 1,000, and 21.5% exceed 2,000, … view at source ↗

read the original abstract

Safety evaluations of large language models (LLMs) typically report binary outcomes such as attack success rate, refusal rate, or harmful/not-harmful response classification. While useful, these can hide how risk changes between a user's input and the model's response. We present a paired, transition-based analysis over 1250 prompt-response records with human-provided labels over four harm categories (Hate, Sexual, Violence, Self-harm) and ordinal severity levels aligned with the Azure AI Content Safety taxonomy. 61% of responses de-escalate harm relative to the prompt, 36% preserve the same severity, and 3% escalate to higher harm. A per-category persistence/drift-up decomposition identifies Sexual content as 3x harder to de-escalate than Hate or Violence, driven by persistence on already-sexual prompts, not by newly introducing sexual harm from benign inputs. Jointly measuring response relevance reveals an empirical signature of the helpfulness-harmlessness tradeoff: all compliance-escalation cases (from non-zero prompts) are relevance-3 (high-quality, on-task content at elevated severity), while medium-severity responses show the lowest relevance (64%), driven by tangential elaborations in Violence and Sexual categories.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The paper shifts safety eval from binary rates to prompt-response harm transitions and finds sexual content sticks more due to persistence, but missing annotation details weaken the percentages.

read the letter

The core contribution is moving past attack-success or refusal binaries to track how harm severity actually changes between prompt and response. On 1250 human-labeled pairs across Hate, Sexual, Violence, and Self-harm, they report 61% de-escalation, 36% same severity, and 3% escalation, with sexual content roughly three times harder to de-escalate because it persists on already-sexual prompts rather than being newly introduced. They also tie this to response relevance and note that escalation cases tend to be high-relevance while medium-severity ones are often tangential. That decomposition and the relevance-harm signature are the genuinely new pieces; prior work mostly stops at overall refusal rates or single-category success metrics. The empirical counts are concrete and the per-category breakdown gives a practical handle for mitigation that binary metrics lack. The main soft spot is exactly what the stress-test flags: no mention of inter-annotator agreement, number of labelers, calibration, or disagreement handling on the ordinal severity scale. Borderline sexual versus violence items could easily shift the 3x claim if labels are noisy, and without those numbers the central statistics rest on unverified human consistency. Sampling method and which models were used are also absent from the abstract, so it is hard to judge how far the patterns generalize. This is the kind of work that belongs in a safety or evaluation track at a conference like ACL or NeurIPS. Readers already running LLM red-teaming or building content filters would get immediate value from the transition lens and the category-specific persistence finding. It deserves a serious referee because the idea is sound and the data volume is reasonable, but the review should focus on forcing the authors to document labeling protocol and release enough detail to let others replicate the percentages. I would bring it to a reading group for the methods discussion but would not cite it yet until the annotation reliability is shown.

Referee Report

2 major / 2 minor

Summary. The paper conducts a paired analysis of 1250 human-labeled prompt-response records from LLMs, using the Azure AI Content Safety taxonomy to assign four harm categories (Hate, Sexual, Violence, Self-harm) and ordinal severity levels. It reports that 61% of responses de-escalate harm relative to the prompt, 36% preserve severity, and 3% escalate, with Sexual content showing 3x greater persistence than Hate or Violence (driven by already-sexual prompts rather than new introductions). It further decomposes by relevance to identify a helpfulness-harmlessness tradeoff signature, where compliance-escalation cases are always high-relevance and medium-severity responses show lowest relevance due to tangential content.

Significance. If the human labels prove reliable, the paired transition framework offers a useful refinement over binary safety metrics by quantifying how risk evolves from prompt to response and exposing category-specific patterns plus relevance-severity interactions. This could support more granular safety tuning and evaluation protocols. The work is purely empirical with no fitted parameters or circular derivations, and the concrete counts from a sizable labeled set are a strength, though reproducibility would benefit from data release.

major comments (2)

[Abstract and dataset construction section] The headline transition statistics (61% de-escalation, 36% preservation, 3% escalation) and the Sexual-category persistence claim (3x harder to de-escalate) are computed directly from the human-assigned ordinal severity labels on the 1250 pairs. No inter-annotator agreement, number of annotators, calibration protocol, or disagreement-resolution procedure is described, which is load-bearing because systematic drift on borderline cases (e.g., Sexual vs. Violence) could artifactually inflate the reported differences and the relevance-severity signature.
[Methods / data collection] Model identities, sampling method for the 1250 records, and prompt sources are not specified. This limits assessment of whether the observed de-escalation rates and category differences generalize or are artifacts of particular model behaviors or prompt distributions.

minor comments (2)

[Abstract] The abstract states 'four harm categories' but the taxonomy alignment and exact severity scale (e.g., how many ordinal levels) should be stated explicitly with a reference to the Azure documentation.
[Analysis section] Clarify whether the relevance labels (relevance-3, etc.) were assigned by the same annotators as the harm labels and whether they used a predefined rubric.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We address each major comment below and commit to revisions that improve transparency without altering the core empirical claims.

read point-by-point responses

Referee: [Abstract and dataset construction section] The headline transition statistics (61% de-escalation, 36% preservation, 3% escalation) and the Sexual-category persistence claim (3x harder to de-escalate) are computed directly from the human-assigned ordinal severity labels on the 1250 pairs. No inter-annotator agreement, number of annotators, calibration protocol, or disagreement-resolution procedure is described, which is load-bearing because systematic drift on borderline cases (e.g., Sexual vs. Violence) could artifactually inflate the reported differences and the relevance-severity signature.

Authors: We agree that annotation reliability details are essential and were omitted from the initial submission. In the revised manuscript we will add a dedicated Methods subsection describing the number of annotators, inter-annotator agreement statistics, calibration procedures, and disagreement-resolution protocol. This addition directly addresses the concern about potential label drift and strengthens the credibility of the reported transition statistics and category-specific patterns. revision: yes
Referee: [Methods / data collection] Model identities, sampling method for the 1250 records, and prompt sources are not specified. This limits assessment of whether the observed de-escalation rates and category differences generalize or are artifacts of particular model behaviors or prompt distributions.

Authors: We acknowledge that these methodological details are necessary for evaluating generalizability. The revised manuscript will expand the Methods section to specify the exact model identities, the sampling procedure used to obtain the 1250 records, and the sources of the prompts. These clarifications will allow readers to assess whether the de-escalation rates and category differences are model- or distribution-specific. revision: yes

Circularity Check

0 steps flagged

No circularity: purely empirical aggregation of human-labeled data

full rationale

The paper's core results (61% de-escalation, 36% preservation, 3% escalation; Sexual category 3x harder to de-escalate) are computed directly as counts and percentages from 1250 prompt-response pairs with human-provided ordinal severity labels aligned to the external Azure taxonomy. No equations, fitted parameters, predictions, or self-citations appear in the derivation chain; the statistics are simple frequency decompositions of the input labels. The analysis is self-contained against the provided dataset with no reduction of outputs to inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claims rest on the reliability of human annotations and the representativeness of the 1250-record sample; no free parameters, mathematical axioms, or new postulated entities are introduced.

axioms (1)

domain assumption Human labels on harm categories and severity levels are accurate and consistent with the Azure AI Content Safety taxonomy
All reported percentages and decompositions depend directly on these labels being treated as ground truth.

pith-pipeline@v0.9.0 · 5518 in / 1287 out tokens · 68861 ms · 2026-05-07T16:16:51.434989+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

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