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arxiv: 2606.03793 · v1 · pith:QVCJOPEYnew · submitted 2026-06-02 · 💻 cs.CL · cs.CV

Exploring Adversarial Robustness and Safety Alignment in Multilingual Multi-Modal Large Language Models

Hashmat Shadab Malik , Muzammal Naseer , Salman Khan This is my paper

Pith reviewed 2026-06-28 10:08 UTC · model grok-4.3

classification 💻 cs.CL cs.CV
keywords adversarial robustnessmultilingual MLLMssafety alignmentcross-lingual transferabilitysafety-by-failuremultimodal modelsvision encoder
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The pith

Adversarial images optimized in one language continue to induce failures in others across multilingual multimodal models, while safety in low-resource languages often results from comprehension failures rather than alignment.

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

The paper systematically studies adversarial robustness and multimodal safety in open-source MLLMs across 12 languages. Gradient-based attacks demonstrate strong cross-lingual transferability of adversarial images. Safety varies with how well models retrieve or interpret harmful instructions in different languages. When harmful content is in images as text, non-English scripts are rarely recognized by the vision encoder. This creates safety-by-failure in lower-resource languages, unlike models with deeper multilingual integration that show genuine safety.

Core claim

Gradient-based attacks reveal a transferable multilingual vulnerability where adversarial images optimized in one language continue to induce failure in others, demonstrating strong cross-lingual transferability. Multilingual safety further varies with how effectively a model retrieves or interprets harmful instructions, leading to safety-by-failure in lower-resource languages as an artefact of comprehension and visual-grounding failures rather than genuine alignment.

What carries the argument

safety-by-failure, where lower-resource languages appear safer due to models failing to parse non-English scripts or understand instructions instead of actively refusing harmful content

If this is right

  • Adversarial attacks need only be developed in high-resource languages to affect others.
  • Stronger linguistic grounding leads to higher rates of misuse-enabling responses when harmful intent is in text.
  • English scripts in images are reliably followed while non-English are not parsed.
  • Shallow multilingual adaptation via instruction tuning produces illusory safety.
  • Deeper integration of multilingual capability across training stages produces genuine safety alignment.

Where Pith is reading between the lines

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

  • Safety evaluations must separately measure comprehension success and refusal behavior.
  • Enhancing vision encoder script recognition could eliminate the safety-by-failure effect and reveal true alignment levels.
  • The pattern may apply to other tasks where models are adapted superficially to new languages or modalities.

Load-bearing premise

The premise that safety differences arise specifically from linguistic grounding and visual parsing of scripts rather than other factors such as training data distribution or model scale.

What would settle it

Finding that safety refusal rates equalize across languages once the model is forced to correctly interpret the instructions and scripts, or observing no difference in attack success when controlling for language-specific comprehension.

Figures

Figures reproduced from arXiv: 2606.03793 by Hashmat Shadab Malik, Muzammal Naseer, Salman Khan.

Figure 1
Figure 1. Figure 1: Performance degradation of PALO on COCO (left) and Flickr30k (right) when adversarial perturbations are optimized in a source (attack) language and evaluated across all target (evaluation) languages. Across both datasets, perturbations generated in one language transfer broadly to other languages, yielding consistently high degradation irrespective of the evaluation language. English-centric pretraining wi… view at source ↗
Figure 2
Figure 2. Figure 2: Performance degradation of PARROT on COCO (left) and Flickr30k (right) when adversarial perturbations are optimized in a source (attack) language and evaluated across all target (evaluation) languages. Across both datasets, perturbations crafted in one language transfer strongly across languages, leading to consistently high degradation irrespective of the evaluation language [PITH_FULL_IMAGE:figures/full… view at source ↗
Figure 3
Figure 3. Figure 3: CIDEr scores on COCO and Flickr30k for PA [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Cross-lingual vulnerability on LLaVA-Bench. Performance degradation when [PITH_FULL_IMAGE:figures/full_fig_p010_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Safety outcomes under the Visual Adversarial Jailbreak attack for PALO (top) and [PITH_FULL_IMAGE:figures/full_fig_p011_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Multi-Lingual Safety Evaluation (Text). Average unsafe response rate for text-only [PITH_FULL_IMAGE:figures/full_fig_p011_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Multi-Lingual Safety Evaluation (TYPO). Average unsafe response rate for typo [PITH_FULL_IMAGE:figures/full_fig_p012_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Multi-Lingual Safety Evaluation (TYPO). Average refusal rate for typographical [PITH_FULL_IMAGE:figures/full_fig_p012_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Multi-Lingual Safety Evaluation (TYPO) for Q [PITH_FULL_IMAGE:figures/full_fig_p013_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Qualitative examples illustrating the three multilingual safety outcomes observed [PITH_FULL_IMAGE:figures/full_fig_p013_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Global speaker population of the 12 evaluated languages (native + second [PITH_FULL_IMAGE:figures/full_fig_p020_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Prompt templates used in the multilingual translation pipeline for adapting [PITH_FULL_IMAGE:figures/full_fig_p027_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Prompt template used in the multilingual translation pipeline for automatic ver [PITH_FULL_IMAGE:figures/full_fig_p028_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Prompt used in the LLM-as-a-judge evaluation framework for scoring COCO [PITH_FULL_IMAGE:figures/full_fig_p029_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: Prompt template used in the multilingual translation pipeline for translating [PITH_FULL_IMAGE:figures/full_fig_p029_15.png] view at source ↗
Figure 16
Figure 16. Figure 16: Qualitative examples from our multilingual adaptation of MM-SafetyBench. [PITH_FULL_IMAGE:figures/full_fig_p030_16.png] view at source ↗
Figure 17
Figure 17. Figure 17: Qualitative examples from our multilingual adaptation of MM-SafetyBench. [PITH_FULL_IMAGE:figures/full_fig_p031_17.png] view at source ↗
Figure 18
Figure 18. Figure 18: Multi-Lingual Safety Evaluation. Average unsafe response rate across languages [PITH_FULL_IMAGE:figures/full_fig_p032_18.png] view at source ↗
Figure 19
Figure 19. Figure 19: Multilingual Safety Evaluation via Refusal Rates across MM-SafetyBench set [PITH_FULL_IMAGE:figures/full_fig_p033_19.png] view at source ↗
Figure 20
Figure 20. Figure 20: Qualitative examples from the COCO multilingual captioning task under [PITH_FULL_IMAGE:figures/full_fig_p034_20.png] view at source ↗
Figure 21
Figure 21. Figure 21: Qualitative examples from the COCO multilingual captioning task under [PITH_FULL_IMAGE:figures/full_fig_p035_21.png] view at source ↗
Figure 22
Figure 22. Figure 22: Qualitative examples from the FLICKR multilingual captioning task under [PITH_FULL_IMAGE:figures/full_fig_p036_22.png] view at source ↗
Figure 23
Figure 23. Figure 23: Qualitative examples from the FLICKR multilingual captioning task under [PITH_FULL_IMAGE:figures/full_fig_p037_23.png] view at source ↗
Figure 24
Figure 24. Figure 24: Qualitative examples from the COCO multilingual captioning benchmark illus [PITH_FULL_IMAGE:figures/full_fig_p038_24.png] view at source ↗
Figure 25
Figure 25. Figure 25: Qualitative examples from the COCO multilingual captioning benchmark illus [PITH_FULL_IMAGE:figures/full_fig_p039_25.png] view at source ↗
Figure 26
Figure 26. Figure 26: Qualitative examples from the COCO multilingual captioning benchmark illus [PITH_FULL_IMAGE:figures/full_fig_p040_26.png] view at source ↗
Figure 27
Figure 27. Figure 27: Qualitative examples from the COCO multilingual captioning benchmark illus [PITH_FULL_IMAGE:figures/full_fig_p041_27.png] view at source ↗
Figure 28
Figure 28. Figure 28: Qualitative example from the LLaVA-Bench multilingual reasoning benchmark [PITH_FULL_IMAGE:figures/full_fig_p042_28.png] view at source ↗
Figure 29
Figure 29. Figure 29: Qualitative example from the LLaVA-Bench multilingual reasoning benchmark [PITH_FULL_IMAGE:figures/full_fig_p042_29.png] view at source ↗
Figure 30
Figure 30. Figure 30: Qualitative example from the LLaVA-Bench multilingual reasoning benchmark [PITH_FULL_IMAGE:figures/full_fig_p043_30.png] view at source ↗
Figure 31
Figure 31. Figure 31: Qualitative example from the LLaVA-Bench multilingual reasoning benchmark [PITH_FULL_IMAGE:figures/full_fig_p044_31.png] view at source ↗
Figure 32
Figure 32. Figure 32: Qualitative examples from the multilingual MM-Safety evaluation using text [PITH_FULL_IMAGE:figures/full_fig_p045_32.png] view at source ↗
Figure 33
Figure 33. Figure 33: Qualitative examples from the multilingual MM-Safety evaluation using text [PITH_FULL_IMAGE:figures/full_fig_p046_33.png] view at source ↗
Figure 34
Figure 34. Figure 34: Qualitative examples from the multilingual MM-Safety evaluation with text-only [PITH_FULL_IMAGE:figures/full_fig_p047_34.png] view at source ↗
Figure 35
Figure 35. Figure 35: Qualitative examples from the multilingual MM-Safety evaluation with text-only [PITH_FULL_IMAGE:figures/full_fig_p048_35.png] view at source ↗
Figure 36
Figure 36. Figure 36: Qualitative examples from the multilingual MM-Safety evaluation with text-only [PITH_FULL_IMAGE:figures/full_fig_p049_36.png] view at source ↗
Figure 37
Figure 37. Figure 37: Qualitative examples from the multilingual MM-Safety evaluation using text [PITH_FULL_IMAGE:figures/full_fig_p050_37.png] view at source ↗
Figure 38
Figure 38. Figure 38: Qualitative examples from the multilingual MM-Safety evaluation using text [PITH_FULL_IMAGE:figures/full_fig_p051_38.png] view at source ↗
Figure 39
Figure 39. Figure 39: Qualitative examples from the multilingual MM-Safety evaluation using text [PITH_FULL_IMAGE:figures/full_fig_p052_39.png] view at source ↗
Figure 40
Figure 40. Figure 40: Qualitative examples from the multilingual MM-Safety evaluation with text-only [PITH_FULL_IMAGE:figures/full_fig_p053_40.png] view at source ↗
Figure 41
Figure 41. Figure 41: Qualitative examples from the multilingual MM-Safety evaluation with text-only [PITH_FULL_IMAGE:figures/full_fig_p054_41.png] view at source ↗
Figure 42
Figure 42. Figure 42: Qualitative examples from the multilingual MM-Safety evaluation under the [PITH_FULL_IMAGE:figures/full_fig_p055_42.png] view at source ↗
Figure 43
Figure 43. Figure 43: Qualitative examples from the multilingual MM-Safety evaluation under the [PITH_FULL_IMAGE:figures/full_fig_p056_43.png] view at source ↗
read the original abstract

Multimodal Large Language Models integrate visual perception into language reasoning, introducing a continuous attack surface susceptible to adversarial attacks. Prior work on MLLM robustness has focused largely on English-centric tasks, leaving multilingual behaviour unexplored. We address this gap through a systematic study of adversarial robustness and multimodal safety across 12 diverse languages, evaluating open-source MLLMs that acquire multilingual capability through instruction tuning. Gradient-based attacks reveal a transferable multilingual vulnerability: adversarial images optimized in one language continue to induce failure in others, demonstrating strong cross-lingual transferability. Multilingual safety further varies with how effectively a model retrieves or interprets harmful instructions. When harmful intent is issued through text, languages with stronger linguistic grounding more often elicit misuse-enabling responses, while weaker languages produce fewer unsafe outputs. When embedded in the image as typographic content, English scripts are reliably recognised and followed, whereas non-English scripts are rarely parsed by the vision encoder. Lower-resource languages may therefore appear safer, but this is an artefact of comprehension and visual-grounding failures rather than genuine alignment, a phenomenon we term safety-by-failure. In contrast, MLLMs that build multilingual capability throughout their training stages rather than only at instruction tuning, such as Qwen3-VL, exhibit genuine cross-lingual safety, maintaining active refusal across languages rather than masking comprehension failure. Shallow multilingual adaptation, such as fine-tuning on translated instruction data, may produce surface-level understanding that creates illusory safety in low-resource languages; deeper integration across training stages leads to genuine multilingual safety alignment.

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 / 1 minor

Summary. The paper claims that gradient-based adversarial attacks on MLLMs exhibit strong cross-lingual transferability, with images optimized in one language inducing failures in others. It further claims that multilingual safety varies by linguistic grounding: text-based harmful instructions in stronger-grounded languages more often elicit unsafe responses, while non-English scripts embedded in images are rarely parsed by the vision encoder. Lower-resource languages thus exhibit apparent safety that is an artefact of comprehension and visual-grounding failures (termed 'safety-by-failure') rather than genuine alignment; models with deeper multilingual integration throughout training (e.g., Qwen3-VL) maintain consistent refusal across languages, unlike those relying on shallow instruction tuning.

Significance. If the empirical patterns hold after controlling for confounds, the work would usefully demonstrate that shallow multilingual adaptation can produce illusory safety and that deeper pretraining-stage integration is required for genuine cross-lingual alignment. This would inform practical choices in MLLM development beyond English-centric robustness studies.

major comments (1)
  1. [Abstract] Abstract: the claim that safety differences arise specifically from linguistic grounding, visual script recognition, and instruction retrieval (rather than unmeasured factors such as pretraining corpus composition, language frequency, or model scale) is load-bearing for the 'safety-by-failure' interpretation and the contrast with Qwen3-VL, yet the manuscript provides no explicit ablations, matched controls, or corpus statistics to rule out these alternatives.
minor comments (1)
  1. The abstract states clear observational claims but supplies no quantitative results, error bars, dataset details, attack success rates, or language-specific metrics, which should be added to allow assessment of effect sizes.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for highlighting a key interpretive challenge in our work. The concern about unmeasured confounds is well-taken, and we address it directly below while proposing a targeted revision.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the claim that safety differences arise specifically from linguistic grounding, visual script recognition, and instruction retrieval (rather than unmeasured factors such as pretraining corpus composition, language frequency, or model scale) is load-bearing for the 'safety-by-failure' interpretation and the contrast with Qwen3-VL, yet the manuscript provides no explicit ablations, matched controls, or corpus statistics to rule out these alternatives.

    Authors: We agree that the manuscript does not contain explicit ablations, matched controls, or corpus statistics that would isolate linguistic grounding from pretraining corpus composition, language frequency, or model scale. Our 'safety-by-failure' framing is therefore correlational, resting on (i) the systematic language-wise patterns we observe across instruction-tuned models and (ii) the documented architectural difference that Qwen3-VL integrates multilingual data from pretraining rather than instruction tuning alone. Because we lack the data to rule out the listed alternatives, we will revise the abstract and add a new Limitations subsection that explicitly flags these confounds, qualifies the causal language, and notes that deeper pretraining integration remains a plausible but unisolated explanatory factor. This change will be reflected in the next version. revision: yes

Circularity Check

0 steps flagged

No circularity: purely empirical observations without derivations or self-referential reductions

full rationale

The paper consists of empirical evaluations of adversarial attacks and safety behaviors across 12 languages in MLLMs, reporting observations such as cross-lingual transferability of attacks and differences in instruction retrieval or script parsing. No equations, fitted parameters, predictions derived from subsets of data, or self-citation load-bearing steps appear in the provided abstract or description. Claims about 'safety-by-failure' and comparisons to models like Qwen3-VL rest on direct experimental contrasts rather than any reduction to inputs by construction or imported uniqueness theorems. This is self-contained empirical work with no load-bearing steps that collapse to prior fits or definitions.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 1 invented entities

The abstract relies on standard assumptions about model instruction tuning and vision encoder behavior but introduces the coined term safety-by-failure without external validation beyond the described observations.

invented entities (1)
  • safety-by-failure no independent evidence
    purpose: To label the appearance of safety in low-resource languages caused by comprehension or visual-grounding failures rather than active alignment.
    The abstract explicitly coins and defines this term to distinguish illusory from genuine safety.

pith-pipeline@v0.9.1-grok · 5811 in / 1122 out tokens · 23456 ms · 2026-06-28T10:08:20.560449+00:00 · methodology

discussion (0)

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