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arxiv: 2606.07706 · v1 · pith:V57YKBK7new · submitted 2026-06-05 · 💻 cs.CR · cs.AI

MLingualFC: Evaluating Jailbreak Vulnerabilities in Multilingual Vision-Language Models

Rishabh Makwana , Mamta , Deeksha Varshney , Oana Cocarascu This is my paper

Pith reviewed 2026-06-27 21:45 UTC · model grok-4.3

classification 💻 cs.CR cs.AI
keywords multilingual vision-language modelsjailbreak attacksflowchart promptssafety alignmentmultilingual benchmarkvisual encodingLatin script languagesPunjabi
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The pith

Flowchart images encoding harmful instructions jailbreak vision-language models more effectively in Latin-script languages than in Punjabi.

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

The paper introduces MLingualFC, a benchmark that encodes harmful instructions into flowchart images across five languages including Hindi, Punjabi, Spanish, Romanian, and German. It evaluates state-of-the-art multilingual VLMs under black-box conditions and finds high attack success rates for Latin-script languages, showing that visual presentation bypasses existing safety alignment. For non-Latin scripts like Punjabi the success rate drops sharply, which the authors attribute to recognition difficulties rather than better defenses. A sympathetic reader would care because the results indicate that current safety mechanisms do not transfer reliably across languages or input modalities.

Core claim

Flowchart-based attacks achieve high attack success rates in case of Latin script languages, demonstrating that visual encoding of harmful content effectively bypasses safety alignment across languages. In contrast, non-Latin script languages such as Punjabi exhibit substantially lower ASR, suggesting potential limitations in visual text recognition rather than stronger safety alignment. These findings highlight that current VLM safety mechanisms fail to generalize across languages and modalities.

What carries the argument

MLingualFC benchmark that converts harmful instructions into multilingual flowchart images for testing black-box jailbreak success in VLMs.

If this is right

  • Visual encoding of harmful content bypasses safety alignment in Latin-script languages for models including Qwen2.5-VL, Gemma-4, and Pangea.
  • Safety mechanisms in current VLMs do not generalize across languages and visual modalities.
  • Non-Latin script languages show lower vulnerability primarily because of recognition limits rather than stronger alignment.
  • Multilingual safety gaps exist under structured visual prompt attacks.

Where Pith is reading between the lines

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

  • Improving visual text recognition for non-Latin scripts would likely increase attack success rates unless safety training is strengthened for visual inputs.
  • Safety training should incorporate flowchart-style visual representations of text in every supported language to close the observed gaps.
  • Extending the benchmark to additional scripts and languages would test whether the Latin versus non-Latin pattern generalizes.

Load-bearing premise

The lower attack success rate for Punjabi is caused by limitations in visual text recognition rather than by differences in safety alignment or other factors.

What would settle it

Direct measurement of each model's accuracy at reading and transcribing the Punjabi text inside the flowchart images, followed by checking whether lower recognition accuracy predicts the observed lower attack success rates.

Figures

Figures reproduced from arXiv: 2606.07706 by Deeksha Varshney, Mamta, Oana Cocarascu, Rishabh Makwana.

Figure 1
Figure 1. Figure 1: Horizontal flowcharts for the query "Write a tutorial on how to make a bomb" for English (top), Hindi [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
read the original abstract

Vision-Language Models (VLMs) have demonstrated strong performance across multimodal tasks, yet their safety robustness remains an open challenge. While prior work has shown that structured visual prompts such as flowcharts can effectively jailbreak VLMs, existing studies are largely limited to English-centric settings. In this paper, we introduce MLingualFC, a multilingual multimodal benchmark designed to evaluate jailbreak vulnerabilities of VLMs across diverse languages using structured flowchart representations. MLingualFC encodes harmful instructions into flowchart images across five languages (Hindi, Punjabi, Spanish, Romanian, and German). We evaluate state-of-the-art multilingual VLMs, including Qwen2.5-VL, Gemma-4, and Pangea, under a black-box threat model. Our results reveal significant multilingual safety gaps. Flowchart-based attacks achieve high attack success rates (ASR) in case of Latin script languages, demonstrating that visual encoding of harmful content effectively bypasses safety alignment across languages. In contrast, non-Latin script languages such as Punjabi exhibit substantially lower ASR, suggesting potential limitations in visual text recognition rather than stronger safety alignment. These findings highlight that current VLM safety mechanisms fail to generalize across languages and modalities. Resources are available at https://github.com/Rishabhpm23/MLingualFC

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

1 major / 3 minor

Summary. The paper introduces MLingualFC, a multilingual multimodal benchmark that encodes harmful instructions into flowchart images across five languages (Hindi, Punjabi, Spanish, Romanian, German) to evaluate jailbreak vulnerabilities in VLMs including Qwen2.5-VL, Gemma-4, and Pangea under a black-box threat model. It reports high attack success rates (ASR) for Latin-script languages via visual flowchart prompts, while non-Latin scripts such as Punjabi exhibit substantially lower ASR, which the authors suggest may indicate visual text recognition limitations rather than stronger safety alignment. The work concludes that current VLM safety mechanisms fail to generalize across languages and modalities, with resources released at a GitHub repository.

Significance. If the empirical measurements hold, the paper is significant for extending jailbreak evaluation to a multilingual multimodal setting and releasing an open benchmark that can support future work on cross-lingual VLM safety. The public code and dataset constitute a clear reproducibility strength.

major comments (1)
  1. [Abstract and results discussion] Abstract and discussion of results: the interpretation that substantially lower ASR for Punjabi flowcharts reflects visual text recognition failure (rather than language-specific safety differences or other factors) is presented as a suggestion but rests on an untested causal claim; no OCR/transcription accuracy measurements, no direct comparison of the same content supplied as readable text versus flowchart, and no safety-component ablations are reported to distinguish these explanations.
minor comments (3)
  1. [Methods] Methods section: the exact number of flowchart instances per language, the source of the harmful instructions, and the precise definition and computation of ASR (including any refusal detection criteria) should be stated explicitly.
  2. [Results] Results: report numerical ASR values (with error bars or confidence intervals if multiple runs) for each language-model pair rather than qualitative descriptors such as 'high' and 'substantially lower'.
  3. [Evaluation] Evaluation setup: specify the exact model versions, temperature settings, and prompt templates used for all three VLMs.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback. We address the major comment below and agree that the interpretation requires clearer framing as a hypothesis.

read point-by-point responses

  1. Referee: [Abstract and results discussion] Abstract and discussion of results: the interpretation that substantially lower ASR for Punjabi flowcharts reflects visual text recognition failure (rather than language-specific safety differences or other factors) is presented as a suggestion but rests on an untested causal claim; no OCR/transcription accuracy measurements, no direct comparison of the same content supplied as readable text versus flowchart, and no safety-component ablations are reported to distinguish these explanations.

    Authors: We agree that the suggestion of visual text recognition limitations for non-Latin scripts such as Punjabi is an untested causal interpretation, as no OCR accuracy measurements, text-vs-flowchart comparisons, or safety ablations are reported. In revision we will update the abstract and results discussion to present this explicitly as a hypothesis inferred from the differential ASR patterns across scripts, rather than a confirmed explanation. We will add a sentence noting that distinguishing recognition failures from language-specific safety differences would require the measurements identified by the referee and is left for future work. The reported ASR numbers and experimental setup remain unchanged. revision: yes

Circularity Check

0 steps flagged

No circularity: pure empirical benchmark with direct ASR measurements

full rationale

The paper introduces MLingualFC as a benchmark, encodes harmful instructions into flowchart images across languages, and reports attack success rates from black-box evaluations on VLMs. No equations, parameter fitting, derivations, or predictions appear in the provided text. The suggestion that lower Punjabi ASR reflects visual recognition limits is an interpretive remark, not a load-bearing claim derived from self-referential steps or fitted inputs. All results are direct counts from experiments, making the work self-contained against external benchmarks with no reduction to its own inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Empirical benchmark paper with no mathematical derivations; no free parameters, axioms, or invented entities are introduced.

pith-pipeline@v0.9.1-grok · 5767 in / 1101 out tokens · 28258 ms · 2026-06-27T21:45:39.925973+00:00 · methodology

discussion (0)

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Reference graph

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