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arxiv: 2606.12291 · v1 · pith:GZJCXXMEnew · submitted 2026-06-10 · 💻 cs.CL

Measuring Epistemic Resilience of LLMs Under Misleading Medical Context

Hongjian Zhou , Xinyu Zou , Jinge Wu , Sean Wu , Junchi Yu , Bradley Max Segal , Tobias Erich Niebuhr , Sara Amro
show 14 more authors
Michael Petrus Sheikh Momin Alexandra M. Cardoso Pinto Rachel Niesen Laura Sophie Wegner Dhruv Darji Jung Moses Koo Joshua Fieggen Kapil Narain Mingde Zeng Lei Clifton Linda Shapiro Fenglin Liu David A. Clifton
This is my paper

Pith reviewed 2026-06-27 09:28 UTC · model grok-4.3

classification 💻 cs.CL
keywords epistemic resiliencelarge language modelsmedical reasoningadversarial robustnessmisleading contextMedMisBenchLLM evaluation
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The pith

LLMs abandon correct medical answers when misleading context is injected into questions they previously answered correctly.

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

The paper tests whether high performance on medical exams means LLMs can safely handle health advice. It defines epistemic resilience as maintaining correct judgment under misleading or adversarial context. To measure this, the authors built MedMisBench with thousands of medical questions and pairs of misleading contexts. Testing across multiple models shows a large drop in accuracy when misleading information is added, especially certain types of formal fabrications. Clinicians reviewing cases see potential for serious harm, pointing to a missing aspect in current LLM medical evaluations.

Core claim

When misleading context is injected into questions that LLMs originally answer correctly, they abandon the correct answer. Across 11 model configurations, mean accuracy falls from 71.1% on original questions to 38.0% under focused misleading context, with 51.5% attack success. The most damaging injections are formal, rule-like fabrications such as authority-framed falsehoods.

What carries the argument

MedMisBench, a collection of 10,932 medical question items and 48,889 misleading context-option pairs that tests whether models preserve correct answers when given misleading medical information.

If this is right

  • Authority-framed falsehoods achieve 69.5% attack success and exception-poisoning claims reach 64.1%.
  • 38.2% of reviewed cases were identified by a clinical panel as having serious potential harm.
  • Existing benchmarks measure what models know but not whether they preserve correct medical judgment under misleading context.

Where Pith is reading between the lines

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

  • If the results hold, LLMs may require new defenses against misleading inputs before deployment in medical settings.
  • Real-world patient interactions could reveal even greater vulnerabilities if the benchmark contexts are less varied than natural misinformation.
  • The focus on medical domain suggests similar resilience tests could apply to other high-stakes areas like legal or financial advice.

Load-bearing premise

The misleading contexts constructed for MedMisBench are representative of the kinds of misleading information that could realistically affect LLM medical decision-making.

What would settle it

A study where LLMs are given misleading context in simulated real patient consultations and their advice is checked against actual medical outcomes or expert review.

Figures

Figures reproduced from arXiv: 2606.12291 by Alexandra M. Cardoso Pinto, Bradley Max Segal, David A. Clifton, Dhruv Darji, Fenglin Liu, Hongjian Zhou, Jinge Wu, Joshua Fieggen, Junchi Yu, Jung Moses Koo, Kapil Narain, Laura Sophie Wegner, Lei Clifton, Linda Shapiro, Michael Petrus, Mingde Zeng, Rachel Niesen, Sara Amro, Sean Wu, Sheikh Momin, Tobias Erich Niebuhr, Xinyu Zou.

Figure 1
Figure 1. Figure 1: Focused false context can redirect a correct medical judgment. An authority-framed [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: MedMisBench turns clean medical QA into paired resilience tests: filtered answer-grounded [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: MedMisBench spans medical reasoning, patient-journey, and agentic tasks. After filtering, 10,932 answer-grounded items remain across 5 source datasets. Selection and filtering. We refer to the injected versions as MEDMISQA, MED￾MISMCQA, MEDMISXPERTQA, MED￾MISJOURNEY, and MEDMISHLE. Across all 5 sources, we retain only items that are answer-grounded, and admit at least 1 semantically valid misleading-contex… view at source ↗
Figure 4
Figure 4. Figure 4: Clean accuracy overstates epistemic resilience. Mean accuracy falls from 71.1% clean to [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Focused delivery drives most resilience loss. Type 1 averages 51.5% ASR versus 18.7% for [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Formal, rule-like falsehoods are most damag [PITH_FULL_IMAGE:figures/full_fig_p008_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Clinician review shows clinical harm is common: [PITH_FULL_IMAGE:figures/full_fig_p009_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Delivery setting changes the resilience test: [PITH_FULL_IMAGE:figures/full_fig_p018_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Applicability filtering makes flips interpretable: accepted items preserve the gold answer [PITH_FULL_IMAGE:figures/full_fig_p019_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Option-aligned generation enables targeted attribution. Type 1 selects one wrong-option [PITH_FULL_IMAGE:figures/full_fig_p019_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Clinician review draws on ge￾ographically diverse judgment from a 14- member panel spanning 7 countries. Given the limited availability of clinician reviewer time and the need for substantial dual-rater overlap, we use the 89 completed-review tasks as a targeted validation and harm-review sample rather than an exhaustive manual review of the full benchmark. MEDMISJOURNEY is not represented because its ite… view at source ↗
Figure 12
Figure 12. Figure 12: Clinician validation supports benchmark-item quality. Rubric A scores are high for base validity, answer preservation, attack fidelity, and clinical plausibility [PITH_FULL_IMAGE:figures/full_fig_p020_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Clinicians review both item validity and response harm using the case, injected context, [PITH_FULL_IMAGE:figures/full_fig_p021_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Clean accuracy does not predict resilience: the [PITH_FULL_IMAGE:figures/full_fig_p024_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: Generator choice does not explain the main pattern: Type 1 remains more damaging than [PITH_FULL_IMAGE:figures/full_fig_p028_15.png] view at source ↗
Figure 16
Figure 16. Figure 16: Provenance findings are stable to cyclic reassignment. Neutral and authority-like framings [PITH_FULL_IMAGE:figures/full_fig_p028_16.png] view at source ↗
Figure 17
Figure 17. Figure 17: Evidence gathering helps but is model-dependent. On HLE-only tasks, search sharply [PITH_FULL_IMAGE:figures/full_fig_p029_17.png] view at source ↗
Figure 18
Figure 18. Figure 18: The defensive instruction is a lightweight resilience intervention: it warns that added [PITH_FULL_IMAGE:figures/full_fig_p030_18.png] view at source ↗
Figure 19
Figure 19. Figure 19: Warnings help but are incomplete. The defensive prompt lowers Type 1 ASR by 10.1–14.0 [PITH_FULL_IMAGE:figures/full_fig_p030_19.png] view at source ↗
read the original abstract

Large language models (LLMs) now reach expert-level scores on medical licensing exams, encouraging the assumption that high scores imply safe medical judgment while patients increasingly use them for health advice. We show this assumption is fragile: when misleading context is injected into questions that LLMs originally answer correctly, they abandon the correct answer. We call the ability to maintain correct judgment under adversarial context epistemic resilience, and introduce MedMisBench to measure it. MedMisBench contains 10,932 medical question items and 48,889 misleading context-option pairs spanning medical reasoning, agentic capability, and patient-journey evaluation. Across 11 model configurations, mean accuracy falls from 71.1% on original questions to 38.0% under focused misleading context, with 51.5% attack success. The most damaging injections are formal, rule-like fabrications: authority-framed falsehoods reach 69.5% attack success and exception-poisoning claims reach 64.1%. A 14-member clinical panel from 7 countries identified serious potential harm in 38.2% of reviewed cases. MedMisBench exposes a structural blind spot in LLM evaluation in medical settings: existing benchmarks measure what models know, but not whether they preserve correct medical judgment under misleading context.

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.

Referee Report

2 major / 1 minor

Summary. The paper claims that LLMs exhibit low epistemic resilience in medical settings: when author-generated misleading contexts are injected into questions the models originally answer correctly, mean accuracy across 11 configurations drops from 71.1% to 38.0% with 51.5% attack success on the new MedMisBench benchmark (10,932 questions, 48,889 context-option pairs). It further reports that authority-framed and exception-poisoning fabrications are most effective and that a 14-member clinical panel flagged serious potential harm in 38.2% of reviewed cases.

Significance. If the benchmark construction is shown to be representative, the work usefully exposes a gap between existing medical QA benchmarks (which test knowledge) and real-world deployment risks (where patients or external sources may supply misleading information). The large scale of MedMisBench and the inclusion of a multi-country clinical harm review are concrete strengths that could support follow-on safety evaluations.

major comments (2)
  1. [Benchmark construction] Benchmark construction section: the paper provides no validation that the 48,889 author-generated misleading contexts are representative of naturalistic medical misinformation (social media, patient reports, forums). The clinical panel review is limited to harm assessment (38.2%); absence of ecological-validity checks or comparison to observed real-world examples is load-bearing for the claim that the measured 51.5% attack success generalizes to realistic medical decision-making scenarios.
  2. [Experimental results] Experimental results: aggregate accuracy figures (71.1% → 38.0%) and attack rates are presented without reported statistical tests, confidence intervals, per-model variance, or controls for question difficulty between original and misleading conditions. This makes it impossible to assess whether the central drop is robust or driven by a subset of items.
minor comments (1)
  1. [Abstract] The abstract states results across '11 model configurations' but does not list the models or prompting setups; adding this detail would improve reproducibility.

Simulated Author's Rebuttal

2 responses · 1 unresolved

We thank the referee for the constructive feedback. We address each major comment below, indicating where revisions will be made.

read point-by-point responses

  1. Referee: [Benchmark construction] Benchmark construction section: the paper provides no validation that the 48,889 author-generated misleading contexts are representative of naturalistic medical misinformation (social media, patient reports, forums). The clinical panel review is limited to harm assessment (38.2%); absence of ecological-validity checks or comparison to observed real-world examples is load-bearing for the claim that the measured 51.5% attack success generalizes to realistic medical decision-making scenarios.

    Authors: We agree that the benchmark relies on author-generated contexts without direct empirical matching to real-world medical misinformation distributions. Contexts were constructed to instantiate documented misinformation patterns (authority framing, exception poisoning) drawn from prior literature, but no systematic comparison to scraped social media or forum examples was performed. The clinical panel assessed harm potential only. We will revise the manuscript to add an explicit limitations section clarifying the scope of generalizability claims and noting the absence of ecological validity checks. revision: partial

  2. Referee: [Experimental results] Experimental results: aggregate accuracy figures (71.1% → 38.0%) and attack rates are presented without reported statistical tests, confidence intervals, per-model variance, or controls for question difficulty between original and misleading conditions. This makes it impossible to assess whether the central drop is robust or driven by a subset of items.

    Authors: The accuracy drop is measured on the exact same items that each model answered correctly in the original condition, providing an item-level paired control for question difficulty. We acknowledge the value of additional statistical reporting. In the revision we will add 95% bootstrap confidence intervals for aggregate and per-model metrics, report model-level variance, and include paired statistical tests (e.g., McNemar’s test) for the significance of accuracy changes. revision: yes

standing simulated objections not resolved
  • Empirical validation of the generated misleading contexts against observed real-world medical misinformation distributions from social media, patient reports, or forums.

Circularity Check

0 steps flagged

No significant circularity; results are direct empirical measurements on a new benchmark

full rationale

The paper introduces MedMisBench as a new collection of 10,932 questions and 48,889 author-generated misleading context-option pairs, then directly measures LLM accuracy drops (71.1% to 38.0%) and attack success (51.5%) when the contexts are injected. No equations, fitted parameters, or self-citations appear in the derivation chain; the reported figures are observed outcomes of running the models on the constructed items. The central claim is therefore an empirical measurement rather than a reduction to prior definitions or self-referential inputs. The representativeness of the generated contexts is a separate validity question outside the scope of circularity analysis.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Review based on abstract only; no free parameters, axioms, or invented entities are extractable beyond the implicit assumption that the benchmark measures the intended phenomenon.

axioms (1)
  • domain assumption Constructed misleading contexts validly test epistemic resilience without introducing unrelated biases.
    The benchmark and reported attack success rates rest on this premise.

pith-pipeline@v0.9.1-grok · 5839 in / 1059 out tokens · 23011 ms · 2026-06-27T09:28:48.771176+00:00 · methodology

discussion (0)

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