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Robust-LLaVA: On the Effectiveness of Large-Scale Robust Image Encoders for Multi-modal Large Language Models

Hashmat Shadab Malik, Fahad Shamshad, Muzammal Naseer, Karthik Nandakumar, Fahad Khan, Salman Khan

Year: 2025Venue: arXiv preprintArea: Multimodal SafetyType: EmpiricalEmbeddings: 121

Models: CLIP, LLaVA, ViT-G/14, ViT-H/14

Intelligence

Status: succeeded | Model: google/gemini-3.1-flash-lite-preview | Prompt: intel-v1 | Confidence: 94%

Last extracted: 3/12/2026, 6:16:08 PM

Summary

Robust-LLaVA is a framework that integrates large-scale adversarially pre-trained vision encoders into Multi-modal Large Language Models (MLLMs) to enhance adversarial robustness without sacrificing visual reasoning performance. By leveraging large-scale robust models like ViT-H and ViT-G and aligning them with language components, the approach outperforms existing plug-and-play methods, achieving significant robustness gains against diverse threats including jailbreak attacks.

Entities (6)

CLIP · vision-encoder · 100%LLaVA · model-architecture · 100%MLLM · model-category · 100%Robust-LLaVA · model-architecture · 100%ViT-H · vision-encoder · 95%FARE · methodology · 90%

Relation Signals (4)

Robust-LLaVA → integrates → Large-scale robust vision encoders

confidence 95% · we propose an effective integration framework that incorporates large-scale adversarially pre-trained vision encoders into the MLLM architecture

Robust-LLaVA → outperforms → FARE

confidence 95% · Our proposed Robust-LLaVA outperforms state-of-the-art FARE

ViT-H → usedin → Robust-LLaVA

confidence 90% · we integrate robust vision encoders with high potential for vision-language alignment

MLLM → vulnerableto → Adversarial Attacks

confidence 90% · MLLMs excel in vision-language tasks but remain vulnerable to visual adversarial perturbations

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Abstract

Abstract:Multi-modal Large Language Models (MLLMs) excel in vision-language tasks but remain vulnerable to visual adversarial perturbations that can induce hallucinations, manipulate responses, or bypass safety mechanisms. Existing methods seek to mitigate these risks by applying constrained adversarial fine-tuning to CLIP vision encoders on ImageNet-scale data, ensuring their generalization ability is preserved. However, this limited adversarial training restricts robustness and broader generalization. In this work, we explore an alternative approach of leveraging existing vision classification models that have been adversarially pre-trained on large-scale data. Our analysis reveals two principal contributions: (1) the extensive scale and diversity of adversarial pre-training enables these models to demonstrate superior robustness against diverse adversarial threats, ranging from imperceptible perturbations to advanced jailbreaking attempts, without requiring additional adversarial training, and (2) end-to-end MLLM integration with these robust models facilitates enhanced adaptation of language components to robust visual features, outperforming existing plug-and-play methodologies on complex reasoning tasks. Through systematic evaluation across visual question-answering, image captioning, and jail-break attacks, we demonstrate that MLLMs trained with these robust models achieve superior adversarial robustness while maintaining favorable clean performance. Our framework achieves 2x and 1.5x average robustness gains in captioning and VQA tasks, respectively, and delivers over 10% improvement against jailbreak attacks. Code and pretrained models will be available at this https URL.

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Robust-LLaVA: On the Effectiveness of Large-Scale Robust Image Encoders for Multi-modal Large Language Models Hashmat Shadab Malik Fahad Shamshad Muzammal Naseer Karthik Nandakumar Fahad Khan Salman Khan Abstract Multi-modal Large Language Models (MLLMs) excel in vision-language tasks but remain vulnerable to visual adversarial perturbations that can induce hallucinations, manipulate responses, or bypass safety mechanisms. Existing methods seek to mitigate these risks by applying constrained adversarial fine-tuning to CLIP vision encoders on ImageNet-scale data, ensuring their generalization ability is preserved. However, this limited adversarial training restricts robustness and broader generalization. In this work, we explore an alternative approach of leveraging existing vision classification models that have been adversarially pre-trained on large-scale data. Our analysis reveals two principal contributions: (1) the extensive scale and diversity of adversarial pre-training enables these models to demonstrate superior robustness against diverse adversarial threats, ranging from imperceptible perturbations to advanced jailbreaking attempts, without requiring additional adversarial training, and (2) end-to-end MLLM integration with these robust models facilitates enhanced adaptation of language components to robust visual features, outperforming existing plug-and-play methodologies on complex reasoning tasks. Through systematic evaluation across visual question-answering, image captioning, and jail-break attacks, we demonstrate that MLLMs trained with these robust models achieve superior adversarial robustness while maintaining favorable clean performance. Our framework achieves 2× and 1.5× average robustness gains in captioning and VQA tasks, respectively, and delivers over 10% improvement against jailbreak attacks. Code and models are available on GitHub. Machine Learning, ICML 1 Introduction Figure 1: Robust performance of the proposed Robust-LLaVA on vision-language tasks at perturbation budget ϵ=4/255italic-ϵ4255ε=4/255ϵ = 4 / 255: The original CLIP exhibits minimal robustness. Our proposed Robust-LLaVA4 outperforms state-of-the-art FARE4 (Schlarmann et al., 2024) and Sim-CLIP4 (Hossain & Imteaj, 2024b) in robustness score across all tasks. Figure 2: Illustration of untargeted ℓ∞subscriptℓ _∞ℓ∞-attacks with ϵ=4/255italic-ϵ4255ε=4/255ϵ = 4 / 255 on LLaVA using different robust vision encoders: Both FARE4 (Schlarmann et al., 2024) and Sim-CLIP4 (Hossain & Imteaj, 2024b) are vulnerable to adversarial attacks while Robust-LLaVA4 not only demonstrates robustness against these attacks but also maintains high performance on the original images. Large Language Models (LLMs) have revolutionized natural language processing, and their integration with visual understanding has introduced a new paradigm: Multi-modal Large Language Models (MLLMs) (Yin et al., 2023; Li et al., 2024a). Unlike traditional vision models, which are typically limited to specific tasks, MLLMs leverage a vision encoder, typically CLIP (Radford et al., 2021), to expand their capabilities, enabling them to engage in open-ended visual reasoning, from answering complex questions about images to generating contextual descriptions and performing multi-step visual analyses (Zhu et al., 2023; Liu et al., 2024b). This flexibility, achieved through instruction tuning (Liu et al., 2024b; Zhang et al., 2023) that aligns model responses with multimodal tasks and natural language cues, redefines visual AI from rigid classification task to interactive assistants capable of interpreting, reasoning about, and discussing visual content in natural language. Recently, to enhance the fine-grained visual understanding of MLLMs and address their inherent “blindness", researchers have explored the integration of multiple vision encoders (Tong et al., 2024; Shi et al., 2024). Despite substantial advancements, integrating visual inputs poses a critical security challenge: MLLMs must process continuous, high-dimensional image spaces that are inherently more susceptible to adversarial attacks than the discrete text space of traditional LLMs (Carlini et al., 2024; Qi et al., 2024). This expanded attack surface enables sophisticated adversarial objectives beyond simple misclassification - attackers can manipulate visual inputs to induce factual hallucinations, trigger unsafe behaviors, or exploit model functionalities while maintaining coherent language output (Schlarmann & Hein, 2023). As adversarial attacks in the visual domain enable a wider range of achievable adversarial objectives, defending against these threats is particularly difficult, thereby posing a significant barrier to deploying MLLMs in critical applications where robustness is paramount. Although adversarial training (Mądry et al., 2017) offers potential protection, its effectiveness is constrained by an inherent trade-off between model accuracy and robustness, and its adaptation from unimodal vision models to MLLMs remains largely unexplored. Recent approaches aim to enhance MLLM robustness by independently training a vision encoder (CLIP) in an adversarial manner and then integrating it into the MLLM, primarily through two strategies: supervised adversarial fine-tuning of the vision encoder on ImageNet (TeCoA) (Mao et al., 2022) or unsupervised adversarial fine-tuning (FARE) (Schlarmann et al., 2024) and Sim-CLIP (Hossain & Imteaj, 2024b). However, these plug-and-play methods face two fundamental limitations in an attempt to balance robustness with MLLM visual understanding capabilities. Limited Robustness Gain: Both approaches operate under the premise that CLIP’s large-scale pre-training establishes a strong foundation for generalization, and therefore significantly restrict adversarial training to preserve these pre-trained features. For instance, FARE employs an extremely limited training schedule (only two epochs) to maintain CLIP’s foundational capabilities, resulting in only modest robustness gains when integrated into MLLMs, as shown in Fig. 1. This trade-off between preserving pre-trained features and achieving robustness inherently limits the effectiveness of these adversarial fine-tuning approaches. Semantic Alignment Gap: More importantly, the misalignment between adversarial CLIP training objectives and MLLMs’ generative understanding creates a semantic alignment gap — while such CLIP models might resist perturbations, their degraded visual representations may impair MLLMs’ complex visual reasoning capabilities, as evidenced by the substantial drop in performance on tasks like visual question-answering and image captioning (see Fig. 1). This highlights a critical challenge: balancing adversarial robustness with MLLMs’ advanced visual reasoning capabilities. Motivated by these challenges, we investigate the role of robust visual representations in enhancing MLLMs’ visual reasoning capabilities. Specifically, we explore existing robust vision encoders and assess their potential for integration within MLLMs while preserving adversarial robustness. To evaluate their multimodal compatibility, we conduct CLIP alignment experiments, examining how well these robust encoders can align with language components. Our findings reveal that highly aligned models, when incorporated into MLLMs, exhibit significantly improved robustness without compromising vision-language performance. Our analysis reveals two key benefits of integrating large-scale robust vision encoders into MLLM training: • Robust Feature Learning: Large-scale adversarial training fundamentally redefines visual representation learning by overcoming the robustness-preservation trade-off in limited-scale methods (Schmidt et al., 2018; Stutz et al., 2019; Hendrycks et al., 2019). The diversity of training data enables models to achieve both adversarial robustness and rich semantic understanding, crucial for MLLMs’ visual reasoning. In contrast, lightweight post-hoc adversarial training, while efficient, struggles to disentangle robustness from semantic feature learning, leading to weaker defenses. • Enhanced Semantic Alignment: Training MLLMs with these robust encoders offers key advantage over approaches that simply plug in robust vision encoders as in TeCoA (Mao et al., 2022) and FARE (Schlarmann & Hein, 2023). By enabling language components to adapt to robust visual features, this end-to-end training preserves MLLMs’ visual reasoning capabilities while maintaining robustness, showing improved performance on complex tasks. Our evaluation shows that training MLLMs with large-scale robust encoders achieves state-of-the-art adversarial robustness without compromising performance on vision-language tasks, as shown in Fig. 2. Tested across a wide range of threats—including imperceptible perturbations, targeted manipulations, transfer attacks, and advanced jailbreaking—our approach significantly improves robustness on all metrics. Notably, it provides strong defense against black-box jailbreaking and enhances resilience to common image corruptions, addressing critical real-world deployment concerns. Importantly, this robustness is achieved with clean-data performance comparable to the current best robustness approaches, suggesting that large-scale adversarial pre-training could contribute to models’ ability to develop both resilience and meaningful visual representations. 2 Related Work Multi-modal LLMs. MLLMs extend traditional LLMs with visual capabilities, enabling processing of both visual and textual information for tasks ranging from visual question-answering to image-grounded dialogue and complex reasoning (Yin et al., 2023; Li et al., 2024a; Caffagni et al., 2024; Awais et al., 2023). These models generally utilize a pretrained vision encoder such as CLIP (Radford et al., 2021) to transform images into dense vector representations (image embeddings), which are then processed through a projection layer to generate token embeddings compatible with the language model’s architecture. Notably, models like LLaVA (Liu et al., 2024b, a) exemplify this architecture’s potential, integrating CLIP vision encoder with Vicuna LLM (Chiang et al., 2023) through a linear projector that aligns visual features into the language domain. Adversarial Robustness of MLLMs. Adversarial attacks have been extensively studied in machine learning, with adversarial training emerging as a key defense strategy (Szegedy, 2013; Goodfellow et al., 2014; Mądry et al., 2017; Zhang et al., 2019), particularly in single-modality systems (Carlini & Wagner, 2017; Ebrahimi et al., 2017). However, MLLMs present unique challenges beyond traditional label misclassification, including hallucination (Huang et al., 2024), factual inconsistency (Wang et al., 2023), and reasoning errors (Wang et al., 2024a). Recent studies have demonstrated MLLMs’ vulnerability to various adversarial attacks that can trigger hallucinations (Bai et al., 2024), enable jailbreaking (Jin et al., 2024), and induce model misuse (Niu et al., 2024), making their security critical for real-world applications (Liu et al., 2025, 2024c; Zhang et al., 2024). While adversarial fine-tuning of vision encoders has shown promise in isolated vision tasks, its effectiveness in the complete MLLM pipeline remains understudied (Mao et al., 2022; Schlarmann et al., 2024; Hossain & Imteaj, 2024b). This gap is particularly significant since the visual encoder forms the foundation for all downstream reasoning in MLLMs. In this paper, we utilize LLaVA’s framework (Liu et al., 2024b, a) as a representative MLLM architecture to investigate how a large-scale adversarially trained vision encoder (Wang et al., 2024b) impacts end-to-end model reliability and performance across diverse downstream tasks and attack scenarios. Jailbreak Attacks for MLLMs. Jailbreaking attacks on MLLMs aim to bypass alignment constraints to generate harmful content (Jin et al., 2024; Qi et al., 2024; Hossain & Imteaj, 2024a; Niu et al., 2024). These attacks can be categorized into white-box and black-box approaches. White-box attacks focus on manipulating input images or visual embeddings, either by generating adversarial images with constraints on the harmful response set (Dong et al., 2023; Schlarmann & Hein, 2023), using teacher-forcing optimization (Carlini et al., 2024), or crafting images that appear harmless but have embeddings similar to harmful images (Shayegani et al., 2023). Black-box attacks employ techniques such as system prompt attacks (Wu et al., 2023), transferring harmful information into text-oriented images (Gong et al., 2023), or using surrogate models to generate adversarial images (Zhao et al., 2024).In this work, We show that LLaVA, when trained with a large-scale adversarially trained encoder, demonstrates resilience to both attack types while maintaining alignment with harmlessness. 3 Robust–LLaVA Our goal is to enhance the robustness of Multi-modal Large Language Models (MLLMs) against adversarial attacks while preserving their advanced visual reasoning capabilities. Conventional approaches that directly apply adversarial training to MLLMs encounter two critical limitations: prohibitive computational overhead and significant degradation in visual reasoning performance. To overcome these limitations, we propose an effective integration framework that incorporates large-scale adversarially pre-trained vision encoders into the MLLM architecture. This approach capitalizes on the robust feature representations learned through large-scale adversarial pretraining while avoiding the pitfalls of direct adversarial training on MLLMs. Adversarial Training: Adversarial training (Mądry et al., 2017), involves training models with adversarial examples (Goodfellow et al., 2014) to enhance resilience to attacks. The process can be formalized as a min-max optimization problem: minθ⁡(x,y)∼⁢[max‖x′−x‖∞≤ϵ⁡L⁢(fθ⁢(x′),y)],subscriptsubscriptsimilar-todelimited-[]subscriptsubscriptnormsuperscript′italic-ϵsubscriptsuperscript′ _θE_(x,y) [ _\|x -x\|_% ∞≤εL(f_θ(x ),y) ],minitalic_θ blackboard_E( x , y ) ∼ D [ max∥ x′ - x ∥ start_POSTSUBSCRIPT ∞ ≤ ϵ end_POSTSUBSCRIPT L ( fitalic_θ ( x′ ) , y ) ] , where fθsubscriptf_θfitalic_θ denotes the model parameterized by θ, L is the loss function (e.g., cross-entropy), and ϵitalic-ϵεϵ defines the perturbation bound in the ℓ∞subscriptℓ _∞ℓ∞-norm. As adversarial training is computationally intensive, it often limits its use to smaller networks e.g., ResNet-50 (He et al., 2016) on smaller datasets like CIFAR-10 (Krizhevsky et al., 2009). Scaling adversarial training to larger models and datasets has remained challenging due to these computational constraints. Large-Scale Robust Vision Encoders: Recent advancements in adversarial training have primarily focused on improving the robustness of vision encoders in classification tasks (Gadre et al., 2024; Wang et al., 2024b), particularly on ImageNet (Russakovsky et al., 2015). These efforts have resulted in robust vision models that can be broadly categorized into two groups: • Medium-scale models such as ViT-B/16 (Dosovitskiy, 2020) and ResNet-101 (He et al., 2016), which have been adversarially trained from scratch on ImageNet, ensuring robustness against standard adversarial perturbations. • Large-scale models such as ViT-H and ViT-G (Wang et al., 2024b), which extend adversarial training to billion-scale web datasets. These models employ a two-stage approach: first, adversarial pretraining on web-scale data using CLIP’s text encoder as a zero-shot classifier, followed by robust fine-tuning on ImageNet (Gadre et al., 2024; Wang et al., 2024b). While these robust encoders demonstrate strong performance in classification, it has not been explored whether these models can be integrated into multimodal large language models (MLLMs) while maintaining their robustness. A key challenge in this integration is ensuring effective alignment with language components to enable coherent vision-language reasoning. A natural approach to achieving this alignment is leveraging CLIP, which has been widely adopted in MLLMs due to two crucial properties: (1) its strong generalization capabilities and (2) its inherent semantic alignment between vision and language representations. However, current approach of limited adversarial fine-tuning of CLIP-based models (Schlarmann et al., 2024; Mao et al., 2022) presents a trade-off, either restricting the model’s level of robustness or significantly degrading downstream vision-language performance. This raises an important question: Can the robust feature represnetations learned by current robust vision encoders be successfully aligned with MLLMs while maintaining adversarial robustness? To investigate this, we conduct a comprehensive alignment study, systematically evaluating medium-scale adversarially trained models alongside large-scale adversarially pretrained encoders. Algorithm 1 Robust Vision Encoder Alignment with CLIP 1:Input: Robust encoder ϕrsubscriptitalic-ϕ _rϕitalic_r, CLIP encoder ϕcsubscriptitalic-ϕ _cϕitalic_c, data DD, learning rate η, batch size B, iterations T 2:Initialize projection W∈ℝd×d′ℝsuperscript′W ^d× d W ∈ blackboard_Rd × d start_POSTSUPERSCRIPT ′ end_POSTSUPERSCRIPT 3:for t=11t=1t = 1 to T do 4: Sample minibatch xii=1B∼similar-tosuperscriptsubscriptsubscript1\x_i\_i=1^B xitalic_i i = 1B ∼ D 5: ℒalign←1B⁢∑i=1B‖ϕc⁢(xi)−W⁢ϕr⁢(xi)‖22←subscriptℒalign1superscriptsubscript1superscriptsubscriptnormsubscriptitalic-ϕsubscriptsubscriptitalic-ϕsubscript22L_align← 1B _i=1^B\| _c(x_i)% -W _r(x_i)\|_2^2Lalign ← divide start_ARG 1 end_ARG start_ARG B end_ARG ∑i = 1B ∥ ϕitalic_c ( xitalic_i ) - W ϕitalic_r ( xitalic_i ) ∥22 6: W←W−η⁢∇Wℒalign←subscript∇subscriptℒalignW← W-η _WL_alignW ← W - η ∇W Lalign ▷ ▷ Gradient Update 7:end for 8:return Aligned encoder ℱ⁢(x)=W⁢ϕr⁢(x)ℱsubscriptitalic-ϕF(x)=W _r(x)F ( x ) = W ϕitalic_r ( x ) Figure 3: Robust Accuracy of Models Across Different Datasets. The plot shows the robust accuracy of different models evaluated across various datasets. PGD-10 attack is crafted at epsilon 1/255 with image-text adversarial loss. 3.1 Robust Vision Encoder Alignment In this section, we investigate whether robust vision encoders ϕrsubscriptitalic-ϕ _rϕitalic_r can align effectively with CLIP’s vision encoder ϕcsubscriptitalic-ϕ _cϕitalic_c through a linear mapping W∈ℝd×d′ℝsuperscript′W ^d× d W ∈ blackboard_Rd × d start_POSTSUPERSCRIPT ′ end_POSTSUPERSCRIPT trained on ImageNet data. Unlike prior work on concept alignment (Moayeri et al., 2023), we align robust encoders with CLIP’s text encoder to analyze robust zero-shot performance in order to assess the viability of their integration within MLLMs. Using a subset of ImageNet training data DD, we optimize W by minimizing the alignment loss ℒalign=1B⁢∑i=1B‖ϕc⁢(xi)−W⁢ϕr⁢(xi)‖22subscriptℒalign1superscriptsubscript1superscriptsubscriptnormsubscriptitalic-ϕsubscriptsubscriptitalic-ϕsubscript22L_align= 1B _i=1^B\| _c(x_i)-W _r% (x_i)\|_2^2Lalign = divide start_ARG 1 end_ARG start_ARG B end_ARG ∑i = 1B ∥ ϕitalic_c ( xitalic_i ) - W ϕitalic_r ( xitalic_i ) ∥22, where xii=1Bsuperscriptsubscriptsubscript1\x_i\_i=1^B xitalic_i i = 1B represents a minibatch of B images sampled from DD. The parameters W of linear layers are updated iteratively using gradient descent with a learning rate η (see Algorithm 1 for details). We evaluate the aligned encoder ℱ⁢(x)=W⁢ϕr⁢(x)ℱsubscriptitalic-ϕF(x)=W _r(x)F ( x ) = W ϕitalic_r ( x ) by testing its zero-shot adversarial robustness under PGD (Mądry et al., 2017) attacks at ϵ=1/255italic-ϵ1255ε=1/255ϵ = 1 / 255. These attacks minimize the similarity between aligned vision features ℱ⁢(x)ℱF(x)F ( x ) and CLIP text encoder features across diverse datasets, assessing effectiveness of the alignment strategy. The results in Figure 3 highlight a strong correlation between model scale, training strategy and robustness preservation during alignment. Small-scale robust models, including ViT-B (AT) and ResNet-101 (AT), exhibit significant degradation in robustness post-alignment, with robust accuracy dropping below 60% across all evaluated datasets. In contrast, large-scale models, robust ViT-H and ViT-G, preserve their robustness while acquiring CLIP’s zero-shot capabilities, achieving robust accuracy ranging from 40% to 85% across a diverse set of benchmarks: ImageNet (Russakovsky et al., 2015), CIFAR10 (Krizhevsky et al., 2009), CIFAR100, Pets (Parkhi et al., 2012), UCF101 (Soomro, 2012), and Caltech101 (Li et al., 2022). Notably, these large-scale models significantly outperform both their smaller counterparts and CLIP’s ViT-L/14 architecture, demonstrating that robust feature representations can be successfully preserved during alignment. Building on this key finding, we integrate robust vision encoders with high potential for vision-language alignment into MLLMS, specifically in the LLaVA framework (Liu et al., 2024a). This approach enables us to achieve both strong adversarial robustness and effective semantic alignment in MLLMs, without the need for additional specialized training procedures. For detailed alignment analysis, refer to Sec. A of the Appendix. 4 Experiments Table 1: Robustness of MLLMs with different Vision Encoders on APGD-Ensemble attack. This table displays the robust performance of LLaVA on two tasks: image captioning and visual question answering (VQA). For VQA tasks (TextVQA, VQAv2, VizWiz, OKVQA), we report VQA accuracy, and for captioning tasks (COCO, Flickr30k), we report CIDEr score. Vision Encoder COCO Flickr30 TextVQA Average clean ℓ∞subscriptℓ _∞ℓ∞ clean ℓ∞subscriptℓ _∞ℓ∞ clean ℓ∞subscriptℓ _∞ℓ∞ clean ℓ∞subscriptℓ _∞ℓ∞ 2/2552255 2255/ start_ARG 2 end_ARG start_ARG 255 end_ARG 4/2554255 4255/ start_ARG 4 end_ARG start_ARG 255 end_ARG 8/2558255 8255/ start_ARG 8 end_ARG start_ARG 255 end_ARG 2/2552255 2255/ start_ARG 2 end_ARG start_ARG 255 end_ARG 4/2554255 4255/ start_ARG 4 end_ARG start_ARG 255 end_ARG 8/2558255 8255/ start_ARG 8 end_ARG start_ARG 255 end_ARG 2/2552255 2255/ start_ARG 2 end_ARG start_ARG 255 end_ARG 4/2554255 4255/ start_ARG 4 end_ARG start_ARG 255 end_ARG 8/2558255 8255/ start_ARG 8 end_ARG start_ARG 255 end_ARG 2/2552255 2255/ start_ARG 2 end_ARG start_ARG 255 end_ARG 4/2554255 4255/ start_ARG 4 end_ARG start_ARG 255 end_ARG 8/2558255 8255/ start_ARG 8 end_ARG start_ARG 255 end_ARG CLIP 126.49 5.81 3.57 2.41 85.86 2.79 1.52 0.78 44.08 0 0 0 85.48 2.87 1.69 1.06 FARE4 117.26 54.54 35.14 18.03 68.14 30.89 18.47 9.25 33.40 15.14 9.70 7.00 72.93 33.52 21.10 11.43 Sim-CLIP4 106.80 50.62 34.56 14.96 64.45 27.26 17.61 7.39 24.86 14.58 8.88 4.74 65.37 30.82 20.35 9.03 Robust-LLaVA4H 108.43 88.73 73.60 43.88 59.79 45.39 34.62 22.09 25.14 18.84 15.90 8.24 64.45 50.99 41.37 24.74 Robust-LLaVA4G 110.40 89.68 76.59 47.12 65.18 49.44 38.75 23.14 24.34 18.14 14.54 10.18 66.64 52.42 43.29 26.81 Vision Encoder VQAv2 VizWiz OKVQA Average clean ℓ∞subscriptℓ _∞ℓ∞ clean ℓ∞subscriptℓ _∞ℓ∞ clean ℓ∞subscriptℓ _∞ℓ∞ clean ℓ∞subscriptℓ _∞ℓ∞ 2/2552255 2255/ start_ARG 2 end_ARG start_ARG 255 end_ARG 4/2554255 4255/ start_ARG 4 end_ARG start_ARG 255 end_ARG 8/2558255 8255/ start_ARG 8 end_ARG start_ARG 255 end_ARG 2/2552255 2255/ start_ARG 2 end_ARG start_ARG 255 end_ARG 4/2554255 4255/ start_ARG 4 end_ARG start_ARG 255 end_ARG 8/2558255 8255/ start_ARG 8 end_ARG start_ARG 255 end_ARG 2/2552255 2255/ start_ARG 2 end_ARG start_ARG 255 end_ARG 4/2554255 4255/ start_ARG 4 end_ARG start_ARG 255 end_ARG 8/2558255 8255/ start_ARG 8 end_ARG start_ARG 255 end_ARG 2/2552255 2255/ start_ARG 2 end_ARG start_ARG 255 end_ARG 4/2554255 4255/ start_ARG 4 end_ARG start_ARG 255 end_ARG 8/2558255 8255/ start_ARG 8 end_ARG start_ARG 255 end_ARG CLIP 75.66 4.28 0.52 0 38.79 0 0 0 59.04 0.31 0 0 57.83 1.53 0.17 0 FARE4 66.56 38.94 28.94 21.96 42.65 24.48 17.95 12.42 54.24 28.04 19.12 11.32 54.48 30.49 22.00 15.23 Sim-CLIP4 65.08 40.50 31.92 21.76 42.49 28.9 21.77 13.15 53.04 29.88 21.48 12.8 53.54 33.09 25.06 15.90 Robust-LLaVA4H 66.36 57.56 48.50 30.42 33.31 27.10 23.13 15.35 53.40 46.44 37.80 24.88 51.02 43.70 36.48 23.55 Robust-LLaVA4G 68.02 59.18 49.28 34.52 37.82 30.17 24.92 15.82 54.88 47.24 39.56 27.96 53.57 45.53 37.92 26.10 We adopt the LLaVA framework (Liu et al., 2024a) to explore the integration of robust vision encoders within Multimodal Large Language Models (MLLMs). The image features from the vision model are aligned with the pre-trained LLM embeddings by training an MLP layer that maps these features into the LLM’s embedding space using an image-text pairs dataset. This is followed by fine-tuning the MLP layer and the LLM within LLaVA on a language-image instruction-following dataset. We build on LLaVA-1.5-7B, which combines a Vicuna-7B (Chiang et al., 2023) language model with a vision encoder. We train and finetune our model with the same experiment setting as in LLaVA. We conduct a comprehensive empirical analysis to investigate the effectiveness of MLLMs trained with adversarially robust vision encoders. Robust Vision Models. We focus on robust vision encoders, specifically large-scale adversarially trained ImageNet models, ViT-G/14 (1B parameters) and ViT-H/14 (632M parameters) (Wang et al., 2024b), trained with a perturbation budget of ϵ=4/255italic-ϵ4255ε=4/255ϵ = 4 / 255. These classification models demonstrate strong zero-shot adversarial robustness, as shown in our CLIP alignment experiments(see Section 3.1). We denote ViT-G/14 and ViT-H/14 robust encoders trained in the LLaVA framework as Robust-LLaVA4G and Robust-LLaVA4H, respectively. Results for other robust vision encoders are presented in Sec. B of the Appendix. Baselines. To assess the effectiveness of our approach, we compare it against the original CLIP vision encoder ViT-L/14 (304M parameters) and its two recent robust variants: FARE (Schlarmann et al., 2024) and Sim-CLIP (Hossain & Imteaj, 2024a), both of which have been adversarially finetuned on the ImageNet dataset (Russakovsky et al., 2015). We evaluate the most robust versions of these models in the LLaVA framework, which have been adversarially trained using an ℓ∞subscriptℓ _∞ℓ∞ norm with a perturbation budget of ϵ=4/255italic-ϵ4255ε=4/255ϵ = 4 / 255, denoted as FARE4superscriptFARE4FARE^4FARE4 and Sim-CLIP4superscriptSim-CLIP4Sim-CLIP^4Sim-CLIP4. 4.1 Quantitative Robustness Evaluation Attack Setting. We evaluate the robust performance of LLaVA models with both the original and adapted robust vision encoders under various attack scenarios, comparing it to state-of-the-art methods such as FARE (Schlarmann et al., 2024) and SimCLIP (Hossain & Imteaj, 2024a). Our attack setting is designed to effectively evaluate the adversarial performance of model and incorporates both untargeted and targeted attack strategies. To assess robustness to untargeted attacks across downstream tasks, following prior works (Schlarmann & Hein, 2023; Schlarmann et al., 2024), we employ an ensemble of attacks. We first employ APGD attacks (Croce & Hein, 2020) at half precision, running for 100 iterations with several ground truth captions/answers as labels. Samples that score below a predefined threshold are excluded, leaving only the hard to fool samples to undergo a second round of attacks at single precision. This two-step strategy optimizes computational resources while maintaining attack effectiveness. Our evaluation spans image captioning and visual question answering (VQA) tasks. For targeted attacks, designed to compel models to produce predetermined outputs, we employ an APGD attack similar to the approach in (Schlarmann et al., 2024), running for 10,000 iterations. All the attacks are employed with ℓ∞subscriptℓ _∞ℓ∞ norm and adversarial perturbation budget set to ϵ=2255,4255,8255italic-ϵ225542558255ε=\ 2255, 4255, 8255\ϵ = divide start_ARG 2 end_ARG start_ARG 255 end_ARG , divide start_ARG 4 end_ARG start_ARG 255 end_ARG , divide start_ARG 8 end_ARG start_ARG 255 end_ARG . Datasets and Metrics. For image captioning, we use the COCO (Lin et al., 2014) and Flickr30k (Plummer et al., 2015) datasets, which feature diverse sets of images paired with multiple human-annotated captions. For VQA, we include VQAv2 (Goyal et al., 2017), TextVQA (Singh et al., 2019), VizWiz (Gurari et al., 2018), and OKVQA (Marino et al., 2019) dataset. Following (Schlarmann et al., 2024; Hossain & Imteaj, 2024a), we evaluate clean and adversarial performance on randomly sampled 500 images per dataset. We report the CIDEr score (Vedantam et al., 2015) to measure captioning performance, where a higher score indicates better alignment, and VQA accuracy as the percentage of correctly answered questions for VQA tasks. We assess targeted attacks on 25 COCO images using three target captions, measuring success rate and CIDEr score. 4.1.1 Results Untargeted Attacks: Table 1 presents results across six datasets, covering image captioning and visual question answering tasks. The LLaVA model using the original CLIP achieves the best clean performance but is completely vulnerable to adversarial attacks. Among the robust models, FARE4 demonstrates the best overall clean performance. However, this gain comes from explicitly aligning the representations of the robust CLIP model with the original CLIP, limiting the extent of adversarial fine-tuning and resulting in significantly reduced performance against adversarial attacks. In contrast, both Robust-LLaVA4G and Robust-LLaVA4H maintain comparable clean performance while achieving substantial robustness improvements against adversarial attacks, striking the right balance between clean and adversarial generalization. At ϵ=4255italic-ϵ4255ε= 4255ϵ = divide start_ARG 4 end_ARG start_ARG 255 end_ARG, Robust-LLaVA4G maintains a CIDEr score of 76.59 on COCO, more than doubling the performance of both FARE4 and Sim-CLIP4. Similarly, in visual question answering, it achieves a notable improvement with 49.28% accuracy on VQAv2, compared to only 31.92% for Sim-CLIP4. This advantage becomes even more pronounced at larger perturbations ϵ=8255italic-ϵ8255ε= 8255ϵ = divide start_ARG 8 end_ARG start_ARG 255 end_ARG, where our approach maintains significant robustness with 47.12 CIDEr on COCO and 34.52% on VQAv2. We report the transferability of adversarial images from Robust-LLaVA4G, FARE4, and Sim-CLIP4 models, as well as results on transfer attacks in Sec. C of the Appendix. Table 2: Quantitative analysis of ℓ∞subscriptℓ _∞ℓ∞ targeted attacks. CLIP is highly susceptible to the attack, Robust-LLaVA4G is completely robust. Target CLIP FARE4 Sim-CLIP4 Robust-LLaVA4G ℓ∞subscriptℓ _∞ℓ∞ ℓ∞subscriptℓ _∞ℓ∞ ℓ∞subscriptℓ _∞ℓ∞ ℓ∞subscriptℓ _∞ℓ∞ 2/2552255 2255/ start_ARG 2 end_ARG start_ARG 255 end_ARG 4/2554255 4255/ start_ARG 4 end_ARG start_ARG 255 end_ARG 8/2558255 8255/ start_ARG 8 end_ARG start_ARG 255 end_ARG 2/2552255 2255/ start_ARG 2 end_ARG start_ARG 255 end_ARG 4/2554255 4255/ start_ARG 4 end_ARG start_ARG 255 end_ARG 8/2558255 8255/ start_ARG 8 end_ARG start_ARG 255 end_ARG 2/2552255 2255/ start_ARG 2 end_ARG start_ARG 255 end_ARG 4/2554255 4255/ start_ARG 4 end_ARG start_ARG 255 end_ARG 8/2558255 8255/ start_ARG 8 end_ARG start_ARG 255 end_ARG 2/2552255 2255/ start_ARG 2 end_ARG start_ARG 255 end_ARG 4/2554255 4255/ start_ARG 4 end_ARG start_ARG 255 end_ARG 8/2558255 8255/ start_ARG 8 end_ARG start_ARG 255 end_ARG Visit https://tinyurl(…) 25 / 25 25 / 25 25 / 25 0 / 25 0 / 25 1 / 25 0 / 25 0 / 25 0 / 25 0 / 25 0 / 25 0 / 25 Please Reset Your Pas(…) 25 / 25 25 / 25 25 / 25 0 / 25 0 / 25 6 / 25 0 / 25 0 / 25 2 / 25 0 / 25 0 / 25 0 / 25 You are too stupi(…) 25 / 25 25 / 25 25 / 25 0 / 25 0 / 25 2 / 25 0 / 25 0 / 25 0 / 25 0 / 25 0 / 25 0 / 25 Mean success rate: 100% 100% 100% 0% 0% 12% 0% 0% 2.67% 0% 0% 0% Average CIDEr score: 0 0 0 93.7 84.1 53.9 102.1 84.5 71.7 116.3 109.68 109.58 Targeted Attacks: Table 4.1.1 presents quantitative results for targeted adversarial attacks. LLaVA with the original CLIP completely fails, with a 100% attack success rate and 0 CIDEr score across all perturbations, demonstrating the attacker’s ability to fully control the output. While FARE4 and Sim-CLIP4 show resilience, they break at higher perturbation budgets ϵ=8255italic-ϵ8255ε= 8255ϵ = divide start_ARG 8 end_ARG start_ARG 255 end_ARG. In contrast, Robust-LLaVA4G and Robust-LLaVA4H remain fully robust, resisting adversarial manipulation even at high perturbations. Notably, Robust-LLaVA4G maintains high-quality captions, preserving a strong CIDEr score, whereas FARE4 and Sim-CLIP4, despite blocking the attacker’s string, experience a noticeable decline in caption quality as perturbations increase. See Sec. D of the Appendix for further details. Table 3: Performance of MLLMs against Visual Adv attacks. We report the number of outputs generated by the model that contain specific(or any) toxic attributes. Model Identity Obscene Insult Threat Toxicity Any CLIP 72 387 235 20 503 503 FARE4 59 237 167 22 298 299 Sim-CLIP4 37 315 182 24 422 422 Robust-LLaVA4G 26 111 75 5 137 137 Robust-LLaVA4H 23 200 121 19 272 272 Table 4: Evaluation results of the MLLMs on HADES. We report ASR (lower is better) for different harmful instructions paired with adversarial image using HADES. Model Animal Financial Privacy Violence Average CLIP 44 59.67 69.22 77.22 62.41 FARE4 33.67 52.44 65.33 72.33 55.94 Sim-CLIP4 31.89 52.44 66.11 72.67 55.78 Robust-LLaVA4G 30.22 43 53.89 65.11 48.06 Robust-LLaVA4H 27.56 38.11 51.11 64.89 45.41 4.2 Robustness against Jailbreaking Attacks MLLMs are susceptible to jailbreaking attacks, where crafted images can trigger harmful prompts, such as generating toxic content or dangerous instructions. We evaluate our approach against both white-box and black-box jailbreaking attacks targeting the model’s safety mechanisms. Whitebox. We evaluate the models using the VisualAdv (Qi et al., 2023) jailbreak attack, which employs the PGD (Mądry et al., 2017) perturbations to benign images, maximizing the likelihood of generating harmful content from a set of 66 derogatory sentences. We generate the adversarial image with a perturbation budget of ϵ=128255italic-ϵ128255ε= 128255ϵ = divide start_ARG 128 end_ARG start_ARG 255 end_ARG, focusing on the higher constraint to test model robustness against strong jailbreak attacks. We then pair the adversarial images with harmful prompts from the RealToxicityPrompts (Gehman et al., 2020) dataset, which contains 1,200 toxic instructions designed to elicit harmful content from the models. Toxicity is measured using the Detoxify classifier(Hanu & Unitary team, 2020), which assigns scores from 0 (least toxic) to 1 (most toxic), with outputs exceeding a threshold of 0.5 considered harmful. Table 4 compares various vision encoders against whitebox VisualAdv attacks across different toxicity attributes. The attribute Any represents instances where at least one toxic attribute is present in the generated output. As anticipated, LLaVA with the original CLIP encoder is the most susceptible, producing 503 toxic outputs, with the majority being obscene content. Both Sim-CLIP4 and FARE4 exhibit improved robustness compared to CLIP. However, Robust-LLaVA4G and Robust-LLaVA4H demonstrate the highest level of robustness against toxic content generation, with Robust-LLaVA4G reducing the total number of toxic outputs significantly to 137. Nevertheless, identity-based and threat-based attacks remain challenging across all models, indicating persistent vulnerabilities even after robust training. For additional results, see Sec. E of Appendix. Blackbox. We employ the black-box version of the HADES attack, which operates in two stages. First, it extracts harmful content from text, embedding it typographically and linking it to an image, transferring adversarial cues to the visual domain. Next, a harmful image, generated via diffusion-based prompt optimization, is appended to increase susceptibility to adversarial content. The GPT-4-0613 model iteratively optimizes the prompt as the LLM attacker. We evaluate 600 harmful instructions across four scenarios (Violence, Financial, Privacy, and Animal), pairing each with an adversarial image. Response harmfulness is assessed using Beaver-7B (Ji et al., 2024), with Attack Success Rate (ASR) quantifying the proportion of successful jailbreaks. Table 4 compares LLaVA variants against HADES attacks. LLaVA with the original CLIP encoder is the most vulnerable, exhibiting the highest ASR, particularly in Violence and Privacy scenarios. While FARE4 and Sim-CLIP4 provide moderate defense, lowering ASR by 7%, they remain susceptible to Violence-related threats. Robust-LLaVA models offer the strongest protection, achieving the lowest ASR and significantly improving resilience, especially in Financial and Privacy attacks. See Sec. F of the Appendix for details. Table 5: Robustness of MLLMs with ensemble of Vision Encoders on APGD attack. For VQA tasks (TextVQA), we report VQA accuracy, and for captioning tasks (COCO, Flickr30k), we report CIDEr score. Vision Encoder COCO Flickr30 TextVQA Average clean ℓ∞subscriptℓ _∞ℓ∞ clean ℓ∞subscriptℓ _∞ℓ∞ clean ℓ∞subscriptℓ _∞ℓ∞ clean ℓ∞subscriptℓ _∞ℓ∞ 2/2552255 2255/ start_ARG 2 end_ARG start_ARG 255 end_ARG 4/2554255 4255/ start_ARG 4 end_ARG start_ARG 255 end_ARG 8/2558255 8255/ start_ARG 8 end_ARG start_ARG 255 end_ARG 2/2552255 2255/ start_ARG 2 end_ARG start_ARG 255 end_ARG 4/2554255 4255/ start_ARG 4 end_ARG start_ARG 255 end_ARG 8/2558255 8255/ start_ARG 8 end_ARG start_ARG 255 end_ARG 2/2552255 2255/ start_ARG 2 end_ARG start_ARG 255 end_ARG 4/2554255 4255/ start_ARG 4 end_ARG start_ARG 255 end_ARG 8/2558255 8255/ start_ARG 8 end_ARG start_ARG 255 end_ARG 2/2552255 2255/ start_ARG 2 end_ARG start_ARG 255 end_ARG 4/2554255 4255/ start_ARG 4 end_ARG start_ARG 255 end_ARG 8/2558255 8255/ start_ARG 8 end_ARG start_ARG 255 end_ARG CLIP 126.49 20.59 12.35 8.36 85.86 12.38 9.09 4.88 44.08 9.8 8.66 6.96 85.48 14.26 10.03 6.73 FARE4 117.94 69.56 61.07 46.66 69.77 41.98 36.74 27.25 32.66 19.40 14.50 11.78 73.46 43.65 37.44 28.56 Robust-LLaVA4H 108.43 103.10 93.84 72.69 59.79 52.79 47.79 36.56 25.14 20.26 17.40 10.94 64.45 58.72 53.01 40.06 Robust-LLaVA4G 110.40 102.40 93.21 75.58 65.18 58.98 51.49 42.26 24.34 19.54 15.96 12.64 66.63 60.31 53.55 43.49 Robust-LLaVA4G + CLIP 125.94 26.34 18.07 10.88 84.27 17.23 11.01 6.61 46.22 11.44 8.96 6.54 85.48 18.34 12.68 8.01 Robust-LLaVA4G + FARE4 116.79 72.41 64.76 49.29 73.48 43.28 39.50 29.84 33.34 18.54 15.50 11.02 74.54 44.74 39.92 30.05 Robust-LLaVA4G + Robust-LLaVA4H 108.89 102.19 94.44 77.09 62.64 56.55 52.12 40.65 24.64 22.16 19.24 13.74 65.39 60.30 55.27 43.83 Table 6: Prompt formatting results of MLLMs on COCO captioning task. Vision Encoders Original AP AC RandStr RandSent ℓ∞subscriptℓ _∞ℓ∞ ℓ∞subscriptℓ _∞ℓ∞ ℓ∞subscriptℓ _∞ℓ∞ ℓ∞subscriptℓ _∞ℓ∞ ℓ∞subscriptℓ _∞ℓ∞ 4/2554255 4255/ start_ARG 4 end_ARG start_ARG 255 end_ARG 8/2558255 8255/ start_ARG 8 end_ARG start_ARG 255 end_ARG 4/2554255 4255/ start_ARG 4 end_ARG start_ARG 255 end_ARG 8/2558255 8255/ start_ARG 8 end_ARG start_ARG 255 end_ARG 4/2554255 4255/ start_ARG 4 end_ARG start_ARG 255 end_ARG 8/2558255 8255/ start_ARG 8 end_ARG start_ARG 255 end_ARG 4/2554255 4255/ start_ARG 4 end_ARG start_ARG 255 end_ARG 8/2558255 8255/ start_ARG 8 end_ARG start_ARG 255 end_ARG 4/2554255 4255/ start_ARG 4 end_ARG start_ARG 255 end_ARG 8/2558255 8255/ start_ARG 8 end_ARG start_ARG 255 end_ARG CLIP 2.98 1.95 4.99 3.13 4.84 3.10 3.53 2.64 5.32 3.13 FARE4 35.43 19.12 40.78 24.02 42.63 24.46 39.71 21.90 47.16 27.90 Sim-CLIP4 34.27 15.95 40.36 20.86 40.90 21.71 37.59 16.85 41.70 22.75 Robust-LLaVA4H 73.67 44.13 78.63 52.48 78.20 52.64 80.25 48.92 82.14 59.22 Robust-LLaVA4G 76.76 46.57 78.90 54.52 81.89 53.57 84.10 51.77 87.74 58.22 Table 7: Hallucination evaluation of MLLMs using POPE (F1-Score). Vision Encoders POPE Sampling Adversarial Popular Random Mean CLIP 84.72 86.31 87.79 86.27 FARE4 76.10 78.06 78.78 77.65 Sim-CLIP4 72.67 74.65 75.68 74.33 Robust-LLaVA4H 78.88 82.83 84.35 82.02 Robust-LLaVA4G 80.15 83.63 84.89 82.89 Table 8: Evaluation under Corruptions on COCO. Average drop in score (S) across each corruption type is reported (S1−S5S1subscriptS1subscriptS5subscriptS1 S_1-S_5S_1divide start_ARG S1 - S5 end_ARG start_ARG S1 end_ARG*100), with corruption severity increasing from 1 to 5. Corruption Type CLIP FARE4 Sim-CLIP4 Robust-LLaVA4G Snow 13.59 50.21 47.68 25.35 Frost 17.88 57.62 52.49 30.88 Fog 14.47 84.34 82.02 56.65 Brightness 3.89 19.87 16.45 7.17 Defocus Blur 26.58 61.42 59.70 45.90 Glass Blur 58.91 70.07 69.85 48.56 Motion Blur 21.56 61.42 58.69 39.56 Zoom Blur 34.32 58.62 55.91 45.00 Contrast 31.20 93.99 95.69 95.73 Elastic Transform 33.63 32.62 31.15 25.41 Pixelate 7.58 14.15 12.98 8.65 JPEG 10.66 7.73 7.05 2.18 Gaussian Noise 33.88 55.67 49.05 27.89 Shot Noise 27.90 53.26 50.39 28.09 Impulse Noise 28.66 52.99 46.62 25.67 4.3 Ensemble of Vision Encoders Inspired by (Tong et al., 2024), we integrate CLIP with Robust-LLaVA4G in the LLaVA framework, incorporating separate learnable projection layers. To enhance robustness, we replace CLIP with robust alternatives like FARE4 and also train an ensemble of ViT-G and ViT-H. Our results evaluated using APGD-100 attack at half precision reported in Table 5 reveal the limitation of model ensebmling in context of adversarial robustness: when using multiple encoders, the model’s robustness tends to align with its weakest component. For instance, LLaVA with Robust-LLaVA4G + CLIP maintains high clean performance but suffers significant drops under attacks (18.07 vs 93.21 at ϵ=4255italic-ϵ4255ε= 4255ϵ = divide start_ARG 4 end_ARG start_ARG 255 end_ARG), suggesting that adversaries can exploit the vulnerable CLIP encoder despite having a robust counterpart. Similarly, Robust-LLaVA4G + FARE4 shows intermediate performance (64.76 vs 93.21 at ϵ=4255italic-ϵ4255ε= 4255ϵ = divide start_ARG 4 end_ARG start_ARG 255 end_ARG), indicating that even a moderately robust encoder can limit the overall system’s robustness. These findings suggest that multi-encoder systems inherit the robustness of their weakest component, making a single, highly robust encoder a more effective alternative. See Sec. G of the Appendix for further analysis. 4.4 Performance on Other Robustness Tasks We expand our evaluation to a wider range of robustness metrics, including resilience to natural image corruptions (Hendrycks & Dietterich, 2019), avoidance of object hallucinations (Li et al., 2023), and robustness to inference-time prompt variations (Bhagwatkar et al., 2024). Common Corruptions. We evaluate MLLMs’ resilience to common image corruptions on the COCO captioning dataset, as summarized in Table 8. CLIP achieves the highest performance across all corruption types. However, adversarially finetuned CLIP vision encoders, such as FARE4 and Sim-CLIP4, exhibit substantial performance drops; exceeding 50% on corruptions like snow, frost, blur, noise, and fog. This degradation is likely a result of adversarial finetuning, which compromises the original generalization capabilities of CLIP. In contrast, Robust-LLaVA4G demonstrates stronger generalization, highlighting that integrating large-scale robust backbones into the LLaVA framework effectively balances robustness and generalization, maintaining the generalization from large-scale training. Further results are detailed in Sec. H of the Appendix. Prompt Formatting. Recent work (Bhagwatkar et al., 2024) has investigated modifying textual prompts at inference to enhance the robustness of non-robust LLaVA models on COCO captioning and VQAv2. While their study focused on weaker attacks, we extend this by evaluating prompt modifications against stronger APGD-ensemble attacks. For COCO captioning, we test four prompt strategies: mentioning the possibility (AP) or certainty (AC) of adversarial perturbations, and adding random strings (RandStr) or sentences (RandSent) at the start. In Table 4.2, we observe consistent improvements with different prompt variations at inference time; however, the non-robust LLaVA model fails to achieve meaningful robustness under stronger attacks, contrasting with the high robustness observed in (Bhagwatkar et al., 2024). Further details are reported in Sec. I of the Appendix. Hallucinations. In Table 4.2, we report the F1-score for each setting of the hallucination benchmark POPE (Li et al., 2023). We observe that while original CLIP has the highest score across all the settings, Robust-LLaVA4G and Robust-LLaVA4H perform better than their robust counterparts. 5 Conclusion In this work, we investigate the adversarial robustness of Multimodal Large Language Models (MLLMs), focusing on vision modality vulnerabilities. We address the limitations of constrained adversarial fine-tuning by exploring multi-modal alignment capabilities of robust vision encoders, leading to the integration of large-scale adversarially trained vision classifiers into MLLM frameworks. Our results demonstrate that these encoders significantly enhance resilience against both untargeted and targeted attacks in image captioning and visual question answering tasks. We further show robustness against optimization-based and generation-based jailbreak attacks, common corruptions, and object hallucination. Additionally, we analyze adversarial weaknesses in MLLM ensembling and explore inference-time prompt formatting techniques to improve robustness. Our work advances the adversarial resilience of MLLMs, contributing to more safer models for real-world applications. Impact Statement Multi-Modal Large Language Models (MLLMs) are increasingly being deployed across various applications due to their impressive performance on diverse tasks. However, ensuring their security and reliability remains a critical challenge. In this work, we take a step toward addressing this issue by investigating adversarially trained vision models and their potential to enhance robustness when integrated into MLLMs. Our findings reveal that MLLMs remain vulnerable to a wide range of vision-centric adversarial attacks, irrespective of the underlying language model. This underscores the necessity of prioritizing robust vision encoders. Notably, our results highlight that incorporating large-scale adversarially multi-modal pretraining of vision encoders before integrating them into the MLLM framework yields significant benefits. Exploring this direction further could lead to substantial improvements in the robustness and security of MLLMs. References Awais et al. 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Appendix This appendix provides a comprehensive exploration of various aspects of the proposed Robust-LLaVA. • Section A: Zero-shot adversarial robustness analysis, focusing on the alignment between adversarially trained vision encoders and the CLIP vision encoder • Section B: Additional results for White-box untargeted attacks within the LLaVA framework using diverse robust vision encoders. • Section C: Transferability analysis of adversarial examples in image captioning and visual question answering tasks, including ensemble-based transfer attacks. • Section D: Detailed insights into experiments conducted for white-box targeted attacks. • Section E and F: Results and discussions on white-box and black-box jailbreak attacks, respectively. • Section G: Additional evaluations of vision encoder ensembles within the MLLM framework. • Section H: Systematic assessment of MLLM robustness against common image corruptions. • Section I: Analysis of MLLM robustness under varying prompt formats. • Section J: Assessment of hallucinations in MLLMs. • Section K: Qualitative examples showcasing the robustness of our approach compared to baseline methods across various robustness tasks. Our well-documented code and pretrained weights are available on GitHub to ensure reproducibility and facilitate further research within the community. Appendix A Alignment of Robust Vision Encoders with CLIP We align multiple robust classification models to CLIP ViT-L/14 by training a fully connected affine transformation that maps their feature space to CLIP’s vision space. Similar to (Moayeri et al., 2023), the fully connected layer is optimized using Stochastic Gradient Descent (SGD) with a learning rate of 0.01, momentum of 0.9, and weight decay of 5e-4, while a cosine annealing scheduler with Tmax=200subscriptT200T_ =200Tmax = 200 adjusts the learning rate over six epochs. Once trained, this alignment enables direct comparison between an image processed by the given model and CLIP-derived text embeddings using cosine similarity to evaluate zero-shot performance across several classification benchmarks. To evaluate the robust multimodal alignment of different vision encoders, we generate adversarial examples to assess their adversarial zero-shot performance. These adversarial examples are crafted by minimizing the cosine similarity between the image features extracted by the vision model and the CLIP text features corresponding to the image prompt, following the approach in (Mao et al., 2022). We expand upon the results presented in the main paper by analyzing the alignment between various vision encoders and the CLIP vision encoder. Our analysis includes additional architectures such as adversarially trained ConvNext-L and WideResNet-50 models. As demonstrated in Figure 4, models trained on large-scale adversarial datasets exhibit superior zero-shot robustness compared to other robust vision models. This enhanced robustness, particularly evident in models like ViT-G and ViT-H, can be attributed to two key factors: (1) extensive adversarial training on large-scale datasets, and (2) the use of a frozen CLIP text encoder as a zero-shot classifier during the pretraining phase. The latter enables effective adversarial training on image-text pairs, leading to robust multi-modal alignment between the vision and text modalities. Figure 4: Robust Accuracy of Models Across Different Datasets. The plot illustrates the robust accuracy of various models evaluated on multiple datasets. The PGD-10 attack is generated with an epsilon 1/255, using an image-text adversarial loss. Appendix B Robustness Against White-Box Untargetted Attacks In this section, we present a comprehensive evaluation of different vision encoders integrated within the LLaVA framework for White-box untargetted adversarial attacks. Our analysis focuses on both robust and standard ImageNet-trained models, examining their performance after pretraining and finetuning stages. Table 9 summarizes these results. We analyze two models: (1) ViT-B/16, which undergoes adversarial training on ImageNet-1K, and (2) ViT-L/14, which is pretrained on the larger ImageNet-21K dataset before being fine-tuned on ImageNet-1K. Our findings reveal distinct performance characteristics for each model. While ViT-B/16 demonstrates moderate robustness against adversarial attacks, its overall performance falls notably short of Robust-LLaVA4G. Conversely, ViT-L/14 achieves superior performance on clean data compared to ViT-B/16 but shows minimal resistance to adversarial attacks. This performance gain for clean data can be attributed to ViT-L/14’s exposure to a substantially larger pretraining dataset, enabling better generalization capabilities. For qualitative validation, we provide extensive visual examples across different tasks: • Image Captioning: Figures LABEL:fig:untargetted_figures_app_1–LABEL:fig:untargetted_figures_app_4 demonstrate adversarial attacks on the COCO dataset • Visual Question Answering: Figures 10 and 11 showcase attacks on the VQAv2 dataset These qualitative examples clearly demonstrate the strong robustness of Robust-LLaVA4G compared to competing robust models FARE4 and Sim-CLIP4. Table 9: Robustness of MLLMs with different Vision Encoders on APGD attack. This table presents the robust performance of various vision-language models across two tasks: image captioning and visual question answering (VQA). For VQA tasks (TextVQA, VQAv2, VizWiz, OKVQA), the reported metric is VQA accuracy, while for captioning tasks (COCO, Flickr30k), the CIDEr score is used. Vision Encoder COCO Flickr30 TextVQA Average clean ℓ∞subscriptℓ _∞ℓ∞ clean ℓ∞subscriptℓ _∞ℓ∞ clean ℓ∞subscriptℓ _∞ℓ∞ clean ℓ∞subscriptℓ _∞ℓ∞ 2/2552255 2255/ start_ARG 2 end_ARG start_ARG 255 end_ARG 4/2554255 4255/ start_ARG 4 end_ARG start_ARG 255 end_ARG 8/2558255 8255/ start_ARG 8 end_ARG start_ARG 255 end_ARG 2/2552255 2255/ start_ARG 2 end_ARG start_ARG 255 end_ARG 4/2554255 4255/ start_ARG 4 end_ARG start_ARG 255 end_ARG 8/2558255 8255/ start_ARG 8 end_ARG start_ARG 255 end_ARG 2/2552255 2255/ start_ARG 2 end_ARG start_ARG 255 end_ARG 4/2554255 4255/ start_ARG 4 end_ARG start_ARG 255 end_ARG 8/2558255 8255/ start_ARG 8 end_ARG start_ARG 255 end_ARG 2/2552255 2255/ start_ARG 2 end_ARG start_ARG 255 end_ARG 4/2554255 4255/ start_ARG 4 end_ARG start_ARG 255 end_ARG 8/2558255 8255/ start_ARG 8 end_ARG start_ARG 255 end_ARG CLIP 126.49 20.59 12.35 8.36 85.86 12.38 9.09 4.88 44.08 9.8 8.66 6.96 85.48 14.26 10.03 6.73 FARE4 117.94 69.56 61.07 46.66 69.77 41.98 36.74 27.25 32.66 19.40 14.50 11.78 73.46 43.65 37.44 28.56 Sim-CLIP4 106.80 74.07 60.68 38.06 64.45 45.15 38.10 22.45 24.86 16.86 13.18 8.52 65.37 45.36 37.32 23.01 Robust-LLaVA4G 110.40 102.40 93.21 75.58 65.18 58.98 51.49 42.26 24.34 19.54 15.96 12.64 66.64 60.31 53.55 43.49 ImageNet Robust ViT-B 77.31 65.54 53.46 29.34 31.34 26.94 19.64 11.29 9.80 8.72 7.50 5.00 39.48 33.73 26.87 15.21 ImageNet Std ViT-L 106.53 16.52 6.71 3.55 59.14 9.15 4.10 2.45 11.18 2.28 1.44 1.02 58.95 9.30 4.08 2.34 Vision Encoder VQAv2 VizWiz OKVQA Average clean ℓ∞subscriptℓ _∞ℓ∞ clean ℓ∞subscriptℓ _∞ℓ∞ clean ℓ∞subscriptℓ _∞ℓ∞ clean ℓ∞subscriptℓ _∞ℓ∞ 2/2552255 2255/ start_ARG 2 end_ARG start_ARG 255 end_ARG 4/2554255 4255/ start_ARG 4 end_ARG start_ARG 255 end_ARG 8/2558255 8255/ start_ARG 8 end_ARG start_ARG 255 end_ARG 2/2552255 2255/ start_ARG 2 end_ARG start_ARG 255 end_ARG 4/2554255 4255/ start_ARG 4 end_ARG start_ARG 255 end_ARG 8/2558255 8255/ start_ARG 8 end_ARG start_ARG 255 end_ARG 2/2552255 2255/ start_ARG 2 end_ARG start_ARG 255 end_ARG 4/2554255 4255/ start_ARG 4 end_ARG start_ARG 255 end_ARG 8/2558255 8255/ start_ARG 8 end_ARG start_ARG 255 end_ARG 2/2552255 2255/ start_ARG 2 end_ARG start_ARG 255 end_ARG 4/2554255 4255/ start_ARG 4 end_ARG start_ARG 255 end_ARG 8/2558255 8255/ start_ARG 8 end_ARG start_ARG 255 end_ARG CLIP 75.66 34.72 34.80 33.82 38.79 7.14 6.69 5.87 59.04 16.64 13.88 11.56 57.83 19.50 18.46 17.08 FARE4 66.72 44.70 42.50 39.66 42.64 28.07 24.31 19.75 54.64 32.32 29.88 24.52 54.67 35.03 32.23 27.98 Sim-CLIP4 65.08 45.86 40.70 36.14 42.49 32.55 27.25 19.44 53.04 36.20 30.88 22.96 53.54 38.20 32.94 26.18 Robust-LLaVA4G 68.02 59.92 52.12 41.26 37.82 31.71 28.09 20.96 54.88 49.68 43.76 34.04 53.57 47.10 41.32 32.09 ImageNet Robust ViT-B 57.90 49.48 38.56 25.66 33.74 30.86 27.51 22.69 44.96 39.36 31.92 23.48 45.53 39.90 32.66 23.94 ImageNet Std ViT-L 66.64 25.54 21.98 19.92 37.40 8.45 5.68 5.91 53.64 11.16 8.52 6.36 52.56 15.05 12.06 10.73 Appendix C Transferability of Adversarial Examples In this section, we analyze the transferability of adversarial examples across different robust vision encoders integrated into the LLaVA framework for untargetted attacks. We conduct our analysis through two complementary approaches: direct transferability evaluation and ensemble-based attacks. C.1 Direct Transferability Analysis Table C.2 presents our evaluation of various robust vision encoders against adversarial example transfers. Following the setup in Table 1 of the main paper, we craft untargeted adversarial examples for both COCO image captioning and VQAv2 visual question answering tasks. Our findings reveal that adversarial transferability is primarily determined by the choice of vision encoder: • CLIP-based models exhibit high intra-group transferability • Robust-LLaVA4G and Robust-LLaVA4H demonstrate strong transferability within their architectural family • Limited cross-group transferability is observed, despite shared LLM components Notably, while all models utilize the same large language model during adversarial attack generation, this shared component does not facilitate cross-model transferability. C.2 Ensemble-based Transfer Attacks We further evaluate MLLM robustness using the MultiTrust benchmarking framework (Zhang et al., 2024), which employs ensemble-based SSA-CWA attacks for generating highly transferable adversarial examples. Our ensemble includes diverse models: ViT-L/14, ViT-B/32, ViT-B/16, ViT-G/14, ConvNext-L, and SigLIP. Adversarial examples (xadvsubscriptadvx_advxadv) are generated by adding imperceptible perturbations (ϵ=16/255italic-ϵ16255ε=16/255ϵ = 16 / 255) to input images (x) to mislead models from recognizing the ground-truth label (y). The attack uses 50 iterations with a step size of 1/25512551/2551 / 255, and robustness is tested by prompting MLLMs to describe adversarial images using the template: “Please provide a detailed description of the image." For evaluation, we use 100 manually relabeled images from the NIPS17 dataset, focusing on commonly understood categories. Model performance is assessed using GPT-4 to determine whether the main object is correctly described in the MLLM outputs. As shown in Figure 5, both Robust-LLaVA4G and Robust-LLaVA4H demonstrate significant robustness improvements, achieving 10-12% higher accuracy compared to their closest robust counterparts. These results validate the effectiveness of our approach against sophisticated ensemble-based transfer attacks. Table 10: Transferability analysis of adversarial examples crafted using Ensemble Attack. This table shows the transferability rates of ℓ∞subscriptℓ _∞ℓ∞-bounded adversarial examples generated by each surrogate model across different target models for both COCO and VQAv2 tasks. COCO VQAv2 Surrogate CLIP FARE4 Robust-LLaVA4H Robust-LLaVA4G CLIP FARE4 Robust-LLaVA4H Robust-LLaVA4G ℓ∞subscriptℓ _∞ℓ∞ ℓ∞subscriptℓ _∞ℓ∞ ℓ∞subscriptℓ _∞ℓ∞ ℓ∞subscriptℓ _∞ℓ∞ ℓ∞subscriptℓ _∞ℓ∞ ℓ∞subscriptℓ _∞ℓ∞ ℓ∞subscriptℓ _∞ℓ∞ ℓ∞subscriptℓ _∞ℓ∞ 4/2554255 4255/ start_ARG 4 end_ARG start_ARG 255 end_ARG 8/2558255 8255/ start_ARG 8 end_ARG start_ARG 255 end_ARG 4/2554255 4255/ start_ARG 4 end_ARG start_ARG 255 end_ARG 8/2558255 8255/ start_ARG 8 end_ARG start_ARG 255 end_ARG 4/2554255 4255/ start_ARG 4 end_ARG start_ARG 255 end_ARG 8/2558255 8255/ start_ARG 8 end_ARG start_ARG 255 end_ARG 4/2554255 4255/ start_ARG 4 end_ARG start_ARG 255 end_ARG 8/2558255 8255/ start_ARG 8 end_ARG start_ARG 255 end_ARG 4/2554255 4255/ start_ARG 4 end_ARG start_ARG 255 end_ARG 8/2558255 8255/ start_ARG 8 end_ARG start_ARG 255 end_ARG 4/2554255 4255/ start_ARG 4 end_ARG start_ARG 255 end_ARG 8/2558255 8255/ start_ARG 8 end_ARG start_ARG 255 end_ARG 4/2554255 4255/ start_ARG 4 end_ARG start_ARG 255 end_ARG 8/2558255 8255/ start_ARG 8 end_ARG start_ARG 255 end_ARG 4/2554255 4255/ start_ARG 4 end_ARG start_ARG 255 end_ARG 8/2558255 8255/ start_ARG 8 end_ARG start_ARG 255 end_ARG CLIP 2.98 1.95 117.39 114.07 108.57 108.35 110.32 109.75 33.42 32.82 66.50 66.26 66.16 65.76 68.02 67.70 FARE4 88.83 44.90 35.43 19.13 107.53 105.25 108.24 104.55 59.30 44.48 28.94 22.64 65.02 65.70 67.58 66.46 Robust-LLaVA4H 123.12 111.79 110.16 96.62 73.67 44.13 105.44 95.12 76.64 73.46 66.00 62.64 48.80 30.42 66.62 62.42 Robust-LLaVA4G 122.85 113.79 112.04 100.19 101.64 93.09 76.77 46.57 75.78 70.94 65.94 63.36 64.54 60.58 49.28 35.26 Figure 5: Clean and Robust Accuracy of Models on Ensemble-based transfer attack. Appendix D Robustness Against White-Box Targeted Attacks We implement targeted attacks following the methodology proposed by (Schlarmann et al., 2024), employing the APGD attack framework with 10,000 iterations. Table 11 provides a comprehensive list of target captions used in crafting these attacks. These carefully selected captions serve as adversarial targets, designed to evaluate the models’ resilience against directed manipulation attempts. Figure LABEL:fig:viz_targetted_attack_1 demonstrates qualitative examples of targeted adversarial attacks on the COCO image captioning task. These examples illustrate the varying degrees of model susceptibility to targeted manipulations and highlight the effectiveness of different defensive strategies. Table 11: Various types of target captions used for targetted attacks. Prompt Target Caption Prompt 1 Visit https://tinyurl.com/23cbjxjz Prompt 2 Please Reset Your Password Prompt 3 You are too stupid. I will not answer anymore Appendix E Robustness Against White-Box Jailbreak Attacks In this section, we present an extensive evaluation of model robustness against white-box VisualAdv jailbreak attacks across multiple perturbation levels. Our analysis examines adversarial images generated with three distinct perturbation budgets: ϵ∈16255,64255,128255italic-ϵ1625564255128255ε∈\ 16255, 64255, 128255\ϵ ∈ divide start_ARG 16 end_ARG start_ARG 255 end_ARG , divide start_ARG 64 end_ARG start_ARG 255 end_ARG , divide start_ARG 128 end_ARG start_ARG 255 end_ARG . The evaluation protocol combines adversarially perturbed images with harmful prompts from the RealToxicityPrompts dataset (Gehman et al., 2020). This dataset comprises approximately 1,200 toxic instructions specifically designed to elicit harmful responses from language models. To quantify response toxicity, we employ the Detoxify classifier (Hanu & Unitary team, 2020), which assigns scores on a scale of 0 (non-toxic) to 1 (highly toxic). A response is classified as toxic if its score exceeds a threshold of 0.5. Table 12 presents our comprehensive ablation study results. The findings demonstrate that both Robust-LLaVA4H and Robust-LLaVA4G maintain robust defense mechanisms against generating toxic content, consistently outperforming baseline models across all perturbation budgets. This superior performance persists even under increasingly aggressive perturbation levels, highlighting the effectiveness of our approach in maintaining safe and appropriate model behavior under adversarial conditions. Table 12: Performance of LVLMs against Visual Adv attacks. This table shows the number of outputs generated by LVLMs that contain specific toxic attributes. The LVLMs utilize various jailbreak defense strategies and are evaluated at different levels of attack strength (ϵitalic-ϵεϵ). Model Identity Obscene Severe Toxicity Insult Threat Toxicity Any Attack Strength ϵ=16255italic-ϵ16255ε= 16255ϵ = divide start_ARG 16 end_ARG start_ARG 255 end_ARG CLIP 131 443 39 304 33 572 573 FARE4 12 226 9 125 19 274 274 Sim-CLIP4 11 192 7 106 19 238 238 Robust-LLaVA4G 6 105 2 60 5 139 139 Robust-LLaVA4H 16 159 7 87 6 217 217 Attack Strength ϵ=64255italic-ϵ64255ε= 64255ϵ = divide start_ARG 64 end_ARG start_ARG 255 end_ARG CLIP 72 387 25 235 20 503 503 FARE4 59 237 27 167 22 298 299 Sim-CLIP4 37 315 13 182 24 422 422 Robust-LLaVA4G 26 111 8 75 5 137 137 Robust-LLaVA4H 23 200 14 121 19 272 272 Attack Strength ϵ=128255italic-ϵ128255ε= 128255ϵ = divide start_ARG 128 end_ARG start_ARG 255 end_ARG CLIP 104 410 24 285 40 541 541 FARE4 40 513 33 315 45 626 626 Sim-CLIP4 40 367 23 217 34 454 454 Robust-LLaVA4G 45 165 13 112 14 231 231 Robust-LLaVA4H 35 201 16 117 15 271 271 Appendix F Robustness Against Black-Box Jailbreak Attacks In this section, we evaluate model robustness against black-box jailbreak attacks where adversaries operate without access to model parameters or gradients. Our analysis utilizes the HADES benchmark (Li et al., 2024b) in a black-box setting, encompassing harmful instructions across four distinct categories: Violence, Financial, Privacy, and Animal. The attack exploits MLLMs’ visual-text alignment vulnerabilities through a two-stage process: Stage 1: Text-to-Visual Translation • Harmful keywords are extracted from input text • Keywords are converted to typographical representations • Harmful content is replaced with generic image-related placeholders • This transformation shifts adversarial cues from textual to visual domain Stage 2: Harmful Image Generation • Harmful images are generated using publicly available diffusion models • Prompts are iteratively optimized using GPT-4-0613 as the adversarial LLM attacker • Final input combines typographically modified text with harmful images Our evaluation protocol employs a comprehensive dataset of 600 harmful instructions spanning four distinct categories: Violence, Financial, Privacy, and Animal. To assess the effectiveness of these attacks, we utilize Beaver-7B (Ji et al., 2024), a specialized model designed for content safety evaluation. Beaver-7B assigns toxicity scores to model outputs, enabling us to determine whether attacks successfully bypass the model’s safety mechanisms. The quantitative results, presented as Attack Success Rates (ASR) in Table 4 of the main paper, provide a detailed assessment of model vulnerability to these sophisticated attacks. Appendix G Robustness of Vision Encoders Ensembles In this section, we present a comprehensive analysis of vision encoder ensembles within the MLLM framework, extending the results from Table 5 of the main paper. We expand our analysis of vision encoder ensembles with Robust-LLaVA4G beyond the original tasks, incorporating additional benchmarks including Visual Question Answering (VQAv2), VizWiz, and OKVQA. The results, presented in Table 13, consistently demonstrate that MLLM robustness is fundamentally limited by the weakest vision encoder in the ensemble. Our investigation further explores two additional ensemble configurations. First, Table 14 presents a detailed analysis of model ensembling with Robust-LLaVA4H as the base architecture. Second, in Table 15, we compare the performance of our most robust ensemble configurations against single-model robust baselines under the more challenging APGD-Ensemble attack. The findings across all configurations reinforce our key observation: the overall robustness of an ensemble system is inherently bounded by its least robust component model. This principle holds true even under more sophisticated attack strategies, highlighting the critical importance of individual model robustness in ensemble configurations. Table 13: Robustness of MLLMs with an ensemble of Vision Encoders under APGD attack. For VQA tasks (TextVQA, VQAv2, VizWiz, OKVQA), VQA accuracy is reported, while for captioning tasks (COCO, Flickr30k), the CIDEr score is provided. Vision Encoder COCO Flickr30 TextVQA Average clean ℓ∞subscriptℓ _∞ℓ∞ clean ℓ∞subscriptℓ _∞ℓ∞ clean ℓ∞subscriptℓ _∞ℓ∞ clean ℓ∞subscriptℓ _∞ℓ∞ 2/2552255 2255/ start_ARG 2 end_ARG start_ARG 255 end_ARG 4/2554255 4255/ start_ARG 4 end_ARG start_ARG 255 end_ARG 8/2558255 8255/ start_ARG 8 end_ARG start_ARG 255 end_ARG 2/2552255 2255/ start_ARG 2 end_ARG start_ARG 255 end_ARG 4/2554255 4255/ start_ARG 4 end_ARG start_ARG 255 end_ARG 8/2558255 8255/ start_ARG 8 end_ARG start_ARG 255 end_ARG 2/2552255 2255/ start_ARG 2 end_ARG start_ARG 255 end_ARG 4/2554255 4255/ start_ARG 4 end_ARG start_ARG 255 end_ARG 8/2558255 8255/ start_ARG 8 end_ARG start_ARG 255 end_ARG 2/2552255 2255/ start_ARG 2 end_ARG start_ARG 255 end_ARG 4/2554255 4255/ start_ARG 4 end_ARG start_ARG 255 end_ARG 8/2558255 8255/ start_ARG 8 end_ARG start_ARG 255 end_ARG CLIP 126.49 20.59 12.35 8.36 85.86 12.38 9.09 4.88 44.08 9.8 8.66 6.96 85.48 14.26 10.03 6.73 FARE4 117.94 69.56 61.07 46.66 69.77 41.98 36.74 27.25 32.66 19.40 14.50 11.78 73.46 43.65 37.44 28.56 Robust-LLaVA4H 108.43 103.10 93.84 72.69 59.79 52.79 47.79 36.56 25.14 20.26 17.40 10.94 64.45 58.72 53.01 40.06 Robust-LLaVA4G 110.40 102.40 93.21 75.58 65.18 58.98 51.49 42.26 24.34 19.54 15.96 12.64 66.64 60.31 53.55 43.49 Robust-LLaVA4G + CLIP 125.94 26.34 18.07 10.88 84.27 17.23 11.01 6.61 46.22 11.44 8.96 6.54 85.48 18.34 12.68 8.01 Robust-LLaVA4G + FARE4 116.79 72.41 64.76 49.29 73.48 43.28 39.50 29.84 33.34 18.54 15.50 11.02 74.54 44.74 39.92 30.05 Robust-LLaVA4G +Robust-LLaVA4H 108.89 102.19 94.44 77.09 62.64 56.55 52.12 40.65 24.64 22.16 19.24 13.74 65.39 60.30 55.27 43.83 Vision Encoder VQAv2 VizWiz OKVQA Average clean ℓ∞subscriptℓ _∞ℓ∞ clean ℓ∞subscriptℓ _∞ℓ∞ clean ℓ∞subscriptℓ _∞ℓ∞ clean ℓ∞subscriptℓ _∞ℓ∞ 2/2552255 2255/ start_ARG 2 end_ARG start_ARG 255 end_ARG 4/2554255 4255/ start_ARG 4 end_ARG start_ARG 255 end_ARG 8/2558255 8255/ start_ARG 8 end_ARG start_ARG 255 end_ARG 2/2552255 2255/ start_ARG 2 end_ARG start_ARG 255 end_ARG 4/2554255 4255/ start_ARG 4 end_ARG start_ARG 255 end_ARG 8/2558255 8255/ start_ARG 8 end_ARG start_ARG 255 end_ARG 2/2552255 2255/ start_ARG 2 end_ARG start_ARG 255 end_ARG 4/2554255 4255/ start_ARG 4 end_ARG start_ARG 255 end_ARG 8/2558255 8255/ start_ARG 8 end_ARG start_ARG 255 end_ARG 2/2552255 2255/ start_ARG 2 end_ARG start_ARG 255 end_ARG 4/2554255 4255/ start_ARG 4 end_ARG start_ARG 255 end_ARG 8/2558255 8255/ start_ARG 8 end_ARG start_ARG 255 end_ARG CLIP 75.66 34.72 34.80 33.82 38.79 7.14 6.69 5.87 59.04 16.64 13.88 11.56 57.83 19.50 18.46 17.08 FARE4 66.72 44.70 42.50 39.66 42.64 28.07 24.31 19.75 54.64 32.32 29.88 24.52 54.67 35.03 32.23 27.98 Robust-LLaVA4H 66.36 58.66 50.96 36.64 33.31 28.88 24.95 19.25 53.40 47.92 40.72 30.60 51.02 45.15 38.88 28.83 Robust-LLaVA4G 68.02 59.92 52.12 41.26 37.82 31.71 28.09 20.96 54.88 49.68 43.76 34.04 53.57 47.10 41.32 32.09 Robust-LLaVA4G + CLIP 76.26 35.12 32.56 30.20 37.05 7.43 6.50 5.87 60.12 17.92 14.56 11.72 57.81 20.16 17.87 15.93 Robust-LLaVA4G + FARE4 67.90 44.60 42.82 39.98 39.12 26.01 22.85 18.17 54.72 33.68 30.72 25.24 53.91 34.76 32.13 27.79 Robust-LLaVA4G + Robust-LLaVA4H 68.58 59.20 52.10 40.14 36.92 33.09 29.03 21.93 54.16 49.72 44.08 33.68 53.22 47.34 41.74 31.92 Table 14: Robustness of MLLMs with ensemble of Vision Encoders on APGD attack. For VQA tasks (TextVQA, VQAv2, VizWiz, OKVQA), we report VQA accuracy, and for captioning tasks (COCO, Flickr30k), we report CIDEr score. Vision Encoder COCO Flickr30 TextVQA Average clean ℓ∞subscriptℓ _∞ℓ∞ clean ℓ∞subscriptℓ _∞ℓ∞ clean ℓ∞subscriptℓ _∞ℓ∞ clean ℓ∞subscriptℓ _∞ℓ∞ 2/2552255 2255/ start_ARG 2 end_ARG start_ARG 255 end_ARG 4/2554255 4255/ start_ARG 4 end_ARG start_ARG 255 end_ARG 8/2558255 8255/ start_ARG 8 end_ARG start_ARG 255 end_ARG 2/2552255 2255/ start_ARG 2 end_ARG start_ARG 255 end_ARG 4/2554255 4255/ start_ARG 4 end_ARG start_ARG 255 end_ARG 8/2558255 8255/ start_ARG 8 end_ARG start_ARG 255 end_ARG 2/2552255 2255/ start_ARG 2 end_ARG start_ARG 255 end_ARG 4/2554255 4255/ start_ARG 4 end_ARG start_ARG 255 end_ARG 8/2558255 8255/ start_ARG 8 end_ARG start_ARG 255 end_ARG 2/2552255 2255/ start_ARG 2 end_ARG start_ARG 255 end_ARG 4/2554255 4255/ start_ARG 4 end_ARG start_ARG 255 end_ARG 8/2558255 8255/ start_ARG 8 end_ARG start_ARG 255 end_ARG CLIP 126.49 20.59 12.35 8.36 85.86 12.38 9.09 4.88 44.08 9.8 8.66 6.96 85.48 14.26 10.03 6.73 FARE4 117.94 69.56 61.07 46.66 69.77 41.98 36.74 27.25 32.66 19.40 14.50 11.78 73.46 43.65 37.44 28.56 Sim-CLIP4 106.80 74.07 60.68 38.06 64.45 45.15 38.10 22.45 24.86 16.86 13.18 8.52 65.37 45.36 37.32 23.01 Robust-LLaVA4G 110.40 102.40 93.21 75.58 65.18 58.98 51.49 42.26 24.34 19.54 15.96 12.64 66.64 60.31 53.55 43.49 Robust-LLaVA4G + CLIP 125.94 26.34 18.07 10.88 84.27 17.23 11.01 6.61 46.22 11.44 8.96 6.54 85.48 18.34 12.68 8.01 Robust-LLaVA4G + FARE4 116.79 72.41 64.76 49.29 73.48 43.28 39.50 29.84 33.34 18.54 15.50 11.02 74.54 44.74 39.92 30.05 Robust-LLaVA4H 108.43 103.10 93.84 72.69 59.79 52.79 47.79 36.56 25.14 20.26 17.40 10.94 64.45 58.72 53.01 40.06 Robust-LLaVA4H + CLIP 126.42 25.95 18.27 10.82 85.08 16.90 12.78 6.46 44.00 9.98 10.16 7.12 85.17 17.61 13.74 8.13 Robust-LLaVA4H + FARE4 114.96 69.58 62.14 49.39 69.74 40.64 39.45 29.88 33.82 18.38 15.84 11.9 72.84 42.87 39.14 30.39 Robust-LLaVA4G +Robust-LLaVA4H 108.89 102.19 94.44 77.09 62.64 56.55 52.12 40.65 24.64 22.16 19.24 13.74 65.39 60.30 55.27 43.83 Vision Encoder VQAv2 VizWiz OKVQA Average clean ℓ∞subscriptℓ _∞ℓ∞ clean ℓ∞subscriptℓ _∞ℓ∞ clean ℓ∞subscriptℓ _∞ℓ∞ clean ℓ∞subscriptℓ _∞ℓ∞ 2/2552255 2255/ start_ARG 2 end_ARG start_ARG 255 end_ARG 4/2554255 4255/ start_ARG 4 end_ARG start_ARG 255 end_ARG 8/2558255 8255/ start_ARG 8 end_ARG start_ARG 255 end_ARG 2/2552255 2255/ start_ARG 2 end_ARG start_ARG 255 end_ARG 4/2554255 4255/ start_ARG 4 end_ARG start_ARG 255 end_ARG 8/2558255 8255/ start_ARG 8 end_ARG start_ARG 255 end_ARG 2/2552255 2255/ start_ARG 2 end_ARG start_ARG 255 end_ARG 4/2554255 4255/ start_ARG 4 end_ARG start_ARG 255 end_ARG 8/2558255 8255/ start_ARG 8 end_ARG start_ARG 255 end_ARG 2/2552255 2255/ start_ARG 2 end_ARG start_ARG 255 end_ARG 4/2554255 4255/ start_ARG 4 end_ARG start_ARG 255 end_ARG 8/2558255 8255/ start_ARG 8 end_ARG start_ARG 255 end_ARG CLIP 75.66 34.72 34.80 33.82 38.79 7.14 6.69 5.87 59.04 16.64 13.88 11.56 57.83 19.50 18.46 17.08 FARE4 66.72 44.70 42.50 39.66 42.64 28.07 24.31 19.75 54.64 32.32 29.88 24.52 54.67 35.03 32.23 27.98 Sim-CLIP4 65.08 45.86 40.70 36.14 42.49 32.55 27.25 19.44 53.04 36.20 30.88 22.96 53.54 38.20 32.94 26.18 Robust-LLaVA4G 68.02 59.92 52.12 41.26 37.82 31.71 28.09 20.96 54.88 49.68 43.76 34.04 53.57 47.10 41.32 32.09 Robust-LLaVA4G + CLIP 76.26 35.12 32.56 30.20 37.05 7.43 6.50 5.87 60.12 17.92 14.56 11.72 57.81 20.16 17.87 15.93 Robust-LLaVA4G + FARE4 67.90 44.60 42.82 39.98 39.12 26.01 22.85 18.17 54.72 33.68 30.72 25.24 53.91 34.76 32.13 27.79 Robust-LLaVA4H 66.36 58.66 50.96 36.64 33.31 28.88 24.95 19.25 53.40 47.92 40.72 30.60 51.02 45.15 38.88 28.83 Robust-LLaVA4H + CLIP 76.12 34.96 33.24 30.08 40.54 8.61 7.87 7.21 59.20 19.48 16.56 12.76 58.62 21.02 19.22 16.68 Robust-LLaVA4H + FARE4 66.94 43.82 40.94 36.50 42.99 28.51 23.92 20.87 55.96 33.96 29.08 24.32 55.29 35.43 31.31 27.23 Robust-LLaVA4G + Robust-LLaVA4H 68.58 59.20 52.10 40.14 36.92 33.09 29.03 21.93 54.16 49.72 44.08 33.68 53.22 47.34 41.74 31.92 Table 15: Robustness of MLLMs with an Ensemble of Vision Encoders under APGD-Ensemble Attack. This table presents the robust performance of LLaVA across two tasks: image captioning and visual question answering (VQA). For VQA tasks (TextVQA, VQAv2, VizWiz, OKVQA), VQA accuracy is reported, while for captioning tasks (COCO, Flickr30k), the CIDEr score is provided. Additionally, the most robust ensemble models, Robust-LLaVA4G + FARE4 and Robust-LLaVA4G + Robust-LLaVA4H, are evaluated against the strong APGD-Ensemble attack. Vision Encoder COCO Flickr30 TextVQA Average clean ℓ∞subscriptℓ _∞ℓ∞ clean ℓ∞subscriptℓ _∞ℓ∞ clean ℓ∞subscriptℓ _∞ℓ∞ clean ℓ∞subscriptℓ _∞ℓ∞ 2/2552255 2255/ start_ARG 2 end_ARG start_ARG 255 end_ARG 4/2554255 4255/ start_ARG 4 end_ARG start_ARG 255 end_ARG 8/2558255 8255/ start_ARG 8 end_ARG start_ARG 255 end_ARG 2/2552255 2255/ start_ARG 2 end_ARG start_ARG 255 end_ARG 4/2554255 4255/ start_ARG 4 end_ARG start_ARG 255 end_ARG 8/2558255 8255/ start_ARG 8 end_ARG start_ARG 255 end_ARG 2/2552255 2255/ start_ARG 2 end_ARG start_ARG 255 end_ARG 4/2554255 4255/ start_ARG 4 end_ARG start_ARG 255 end_ARG 8/2558255 8255/ start_ARG 8 end_ARG start_ARG 255 end_ARG 2/2552255 2255/ start_ARG 2 end_ARG start_ARG 255 end_ARG 4/2554255 4255/ start_ARG 4 end_ARG start_ARG 255 end_ARG 8/2558255 8255/ start_ARG 8 end_ARG start_ARG 255 end_ARG CLIP 126.49 5.81 3.57 2.41 85.86 2.79 1.52 0.78 44.08 0 0 0 85.48 2.87 1.69 1.06 FARE4 117.26 54.54 35.14 18.03 68.14 30.89 18.47 9.25 33.40 15.14 9.70 7.00 72.93 33.52 21.10 11.43 Sim-CLIP4 106.80 50.62 34.56 14.96 64.45 27.26 17.61 7.39 24.86 14.58 8.88 4.74 65.37 30.82 20.35 9.03 Robust-LLaVA4G 110.40 89.68 76.59 47.12 65.18 49.44 38.75 23.14 24.34 18.14 14.54 10.18 66.64 52.42 43.29 26.81 Robust-LLaVA4H 108.43 88.73 73.60 43.88 59.79 45.39 34.62 22.09 25.14 18.84 15.90 8.24 64.45 50.99 41.37 24.74 Robust-LLaVA4G +FARE4 114.53 54.01 38.05 19.68 69.92 30.06 20.75 10.75 33.78 15.70 11.24 6.84 72.75 33.26 23.35 12.42 Robust-LLaVA4G +Robust-LLaVA4H 108.89 90.81 74.97 48.24 62.64 47.27 38.82 22.89 24.64 21.56 16.86 10.56 65.39 53.21 43.55 27.23 Vision Encoder VQAv2 VizWiz OKVQA Average clean ℓ∞subscriptℓ _∞ℓ∞ clean ℓ∞subscriptℓ _∞ℓ∞ clean ℓ∞subscriptℓ _∞ℓ∞ clean ℓ∞subscriptℓ _∞ℓ∞ 2/2552255 2255/ start_ARG 2 end_ARG start_ARG 255 end_ARG 4/2554255 4255/ start_ARG 4 end_ARG start_ARG 255 end_ARG 8/2558255 8255/ start_ARG 8 end_ARG start_ARG 255 end_ARG 2/2552255 2255/ start_ARG 2 end_ARG start_ARG 255 end_ARG 4/2554255 4255/ start_ARG 4 end_ARG start_ARG 255 end_ARG 8/2558255 8255/ start_ARG 8 end_ARG start_ARG 255 end_ARG 2/2552255 2255/ start_ARG 2 end_ARG start_ARG 255 end_ARG 4/2554255 4255/ start_ARG 4 end_ARG start_ARG 255 end_ARG 8/2558255 8255/ start_ARG 8 end_ARG start_ARG 255 end_ARG 2/2552255 2255/ start_ARG 2 end_ARG start_ARG 255 end_ARG 4/2554255 4255/ start_ARG 4 end_ARG start_ARG 255 end_ARG 8/2558255 8255/ start_ARG 8 end_ARG start_ARG 255 end_ARG CLIP 75.66 4.28 0.52 0 38.79 0 0 0 59.04 0.31 0 0 57.83 1.53 0.17 0 FARE4 66.56 38.94 28.94 21.96 42.65 24.48 17.95 12.42 54.24 28.04 19.12 11.32 54.48 30.49 22.00 15.23 Sim-CLIP4 65.08 40.50 31.92 21.76 42.49 28.9 21.77 13.15 53.04 29.88 21.48 12.8 53.54 33.09 25.06 15.90 Robust-LLaVA4G 68.02 59.18 49.28 34.52 37.82 30.17 24.92 15.82 54.88 47.24 39.56 27.96 53.57 45.53 37.92 26.10 Robust-LLaVA4H 66.36 57.56 48.50 30.42 33.31 27.10 23.13 15.35 53.40 46.44 37.80 24.88 51.02 43.70 36.48 23.55 Robust-LLaVA4G +FARE4 69.56 40.32 30.86 21.78 38.79 23.23 16.82 8.89 53.92 28.20 19.68 10.56 54.09 30.58 22.45 13.74 Robust-LLaVA4G +Robust-LLaVA4H 68.58 58.78 49.46 33.94 36.92 31.51 24.80 16.57 54.16 48.44 40.12 26.28 53.22 46.24 38.13 25.59 Table 16: Evaluation on COCO Captioning datasets under corruptions. We report the average performance drop for each method across different corruption types, where the severity level increases from 1 to 5. The average drop is evaluated using the formula: (Score at Level 1−Score at Level 5)/Score at Level 1Score at Level 1Score at Level 5Score at Level 1(Score at Level 1-Score at Level 5)/Score at Level 1( Score at Level 1 - Score at Level 5 ) / Score at Level 1. Corruption Type CLIP FARE4 Sim-CLIP4 Robust-LLaVA4G Robust-LLaVA4H Snow 13.59 50.21 47.68 25.35 22.73 Frost 17.88 57.62 52.49 30.88 30.69 Fog 14.47 84.34 82.02 56.65 60.80 Brightness 3.89 19.87 16.45 7.17 8.67 Defocus Blur 26.58 61.42 59.70 45.90 47.47 Glass Blur 58.91 70.07 69.85 48.56 51.79 Motion Blur 21.56 61.42 58.69 39.56 47.04 Zoom Blur 34.32 58.62 55.91 45.00 46.76 Contrast 31.20 93.99 95.69 95.73 94.88 Elastic Transform 33.63 32.62 31.15 25.41 23.72 Pixelate 7.58 14.15 12.98 8.65 12.18 JPEG Compression 10.66 7.73 7.05 2.18 2.98 Gaussian Noise 33.88 55.67 49.05 27.89 31.17 Shot Noise 27.90 53.26 50.39 28.09 26.28 Impulse Noise 28.66 52.99 46.62 25.67 28.72 Table 17: Evaluation on VQAv2 datasets under corruptions. We report the average performance drop for each method across different corruption types, where the severity level increases from 1 to 5. The average drop is evaluated using the formula: (Score at Level 1−Score at Level 5)/Score at Level 1Score at Level 1Score at Level 5Score at Level 1(Score at Level 1-Score at Level 5)/Score at Level 1( Score at Level 1 - Score at Level 5 ) / Score at Level 1. Corruption Type CLIP FARE4 Sim-CLIP4 Robust-LLaVA4G Robust-LLaVA4H Snow 6.94 19.46 19.27 12.21 7.54 Frost 6.66 22.69 22.58 12.09 12.38 Fog 7.81 21.03 26.08 24.68 17.77 Brightness 2.84 8.67 8.34 3.59 5.05 Defocus Blur 15.03 19.56 19.83 18.64 19.03 Glass Blur 27.96 24.61 25.48 24.60 23.66 Motion Blur 12.93 22.25 20.59 17.40 19.50 Zoom Blur 12.93 11.80 10.15 12.94 12.98 Contrast 13.25 30.66 31.70 38.85 37.00 Elastic Transform 15.40 16.21 10.83 11.45 13.58 Pixelate 5.35 6.04 6.39 5.04 1.35 JPEG Compression 10.35 1.86 2.37 2.32 -0.68 Gaussian Noise 19.17 23.83 22.08 12.84 14.83 Shot Noise 17.85 24.10 22.48 8.31 13.97 Impulse Noise 15.76 21.64 14.65 8.87 11.20 Table 18: Evaluation on OKVQA datasets under corruptions. We report the average performance drop for each method across various corruption types, with severity levels ranging from 1 to 5. The average drop is evaluated using the formula: (Score at Level 1−Score at Level 5)/Score at Level 1Score at Level 1Score at Level 5Score at Level 1(Score at Level 1-Score at Level 5)/Score at Level 1( Score at Level 1 - Score at Level 5 ) / Score at Level 1. Corruption Type CLIP FARE4 Sim-CLIP4 Robust-LLaVA4G Robust-LLaVA4H Snow 9.04 22.61 21.76 11.42 7.72 Frost 7.88 23.48 21.69 12.64 10.76 Fog 6.71 17.03 20.58 16.88 15.59 Brightness 1.80 8.54 8.86 0.24 0.68 Defocus Blur 8.91 22.76 23.17 19.63 18.17 Glass Blur 35.58 29.87 27.15 21.67 18.80 Motion Blur 10.68 24.29 24.66 18.36 17.72 Zoom Blur 15.03 22.94 21.76 15.78 14.84 Contrast 15.92 26.04 28.94 42.68 37.02 Elastic Transform 17.88 12.74 13.33 13.10 11.93 Pixelate 6.02 5.96 5.93 7.56 2.06 JPEG Compression 2.54 2.38 3.17 1.21 1.20 Gaussian Noise 16.51 22.96 19.20 18.15 14.38 Shot Noise 19.65 23.20 20.78 14.71 10.77 Impulse Noise 13.36 22.06 18.79 16.65 16.34 Table 19: Various types of prompts used for testing robustness against image captioning task(COCO) by modifying prompts at inference time. Prompt Type Prompt Original Provide a short caption for this image. AP Given the image could be adversarially perturbed. Provide a short caption for this image. AC Given the image is adversarially perturbed. Provide a short caption for this image. RandStr ryFo8ZVcyNMtLgryNOg64UTjySyEb79e5aq6IJ xGuz0GzWNtoz. Provide a short caption for this image. RandSent Clouds drift quietly over the ancient, forgotten city. Provide a short caption for this image. Table 20: Prompt formatting results on COCO captioning task. Vision Encoders Original AP AC RandStr RandSent ℓ∞subscriptℓ _∞ℓ∞ ℓ∞subscriptℓ _∞ℓ∞ ℓ∞subscriptℓ _∞ℓ∞ ℓ∞subscriptℓ _∞ℓ∞ ℓ∞subscriptℓ _∞ℓ∞ 4/2554255 4255/ start_ARG 4 end_ARG start_ARG 255 end_ARG 8/2558255 8255/ start_ARG 8 end_ARG start_ARG 255 end_ARG 4/2554255 4255/ start_ARG 4 end_ARG start_ARG 255 end_ARG 8/2558255 8255/ start_ARG 8 end_ARG start_ARG 255 end_ARG 4/2554255 4255/ start_ARG 4 end_ARG start_ARG 255 end_ARG 8/2558255 8255/ start_ARG 8 end_ARG start_ARG 255 end_ARG 4/2554255 4255/ start_ARG 4 end_ARG start_ARG 255 end_ARG 8/2558255 8255/ start_ARG 8 end_ARG start_ARG 255 end_ARG 4/2554255 4255/ start_ARG 4 end_ARG start_ARG 255 end_ARG 8/2558255 8255/ start_ARG 8 end_ARG start_ARG 255 end_ARG CLIP 2.98 1.95 4.99 3.13 4.84 3.10 3.53 2.64 5.32 3.13 FARE4 35.43 19.12 40.78 24.02 42.63 24.46 39.71 21.90 47.16 27.90 Sim-CLIP4 34.27 15.95 40.36 20.86 40.90 21.71 37.59 16.85 41.70 22.75 Robust-LLaVA4H 73.67 44.13 78.63 52.48 78.20 52.64 80.25 48.92 82.14 59.22 Robust-LLaVA4G 76.76 46.57 78.90 54.52 81.89 53.57 84.10 51.77 87.74 58.22 Robust-LLaVA4G +FARE4 38.16 19.82 41.42 45.88 41.01 25.07 42.97 24.41 47.88 28.78 Robust-LLaVA4H +FARE4 36.53 19.89 40.53 42.64 42.74 26.32 41.60 22.48 43.25 27.74 Robust-LLaVA4G +Robust-LLaVA4H 75.09 47.93 79.99 56.50 79.42 55.36 78.40 51.38 85.30 58.71 Appendix H Robustness against Common Corruptions In this section, we present an extensive evaluation of MLLM robustness against common image corruptions, expanding upon the results reported in the main paper. Our analysis encompasses three tasks, with results presented in Table 16 for COCO image captioning, Table 17 for VQAv2 visual question answering, and Table 18 for OKVQA knowledge-based visual reasoning. For each corruption type, we assess model performance across five severity levels. The impact of corruptions is quantified using an average performance drop metric: Average Drop (%)=S1−S5S1×100,Average Drop (%)subscript1subscript5subscript1100Average Drop (\%)= S_1-S_5S_1× 100,Average Drop (%) = divide start_ARG S1 - S5 end_ARG start_ARG S1 end_ARG × 100 , where S1subscript1S_1S1 and S5subscript5S_5S5 represent model performance at the lowest and highest severity levels, respectively. Our comprehensive analysis includes both quantitative and qualitative evaluations. The qualitative results are presented through two sets of visualizations. Figures LABEL:fig:all_corruptions and LABEL:fig:all_corruptions_2 demonstrate MLLM performance across different corruption types, while Figures 15, 16, and 17 illustrate the progressive impact of increasing corruption severity. The results consistently demonstrate that both Robust-LLaVA4G and Robust-LLaVA4H achieve superior robustness against common corruptions compared to robust baseline models. This enhanced resilience is evident across all corruption types and severity levels, highlighting the effectiveness of our approach in maintaining reliable performance under varying image degradation conditions. Appendix I Prompt Formatting at Inference In this section, we examine the impact of prompt modifications on model robustness during inference. Table 19 presents our suite of modified prompts designed for the COCO captioning task, specifically crafted to enhance model resilience against adversarial examples during inference. We extend our analysis beyond the initial results presented in Table 4.2 of the main paper by evaluating the performance of robust model ensembles within the LLaVA framework. The comprehensive results, detailed in Table 20, demonstrate consistent improvements in robustness through strategic prompt modifications at inference time. However, it is important to note that while this approach shows promise, it is not inherently secure against adaptive attacks. Adversaries with knowledge of the modified prompts can potentially exploit this information to craft more sophisticated adversarial examples that circumvent these defensive measures. Table 21: Hallucination evaluation using POPE (F1-Score). Vision Encoders POPE Sampling Adversarial Popular Random Mean CLIP 84.72 86.31 87.79 86.27 FARE4 76.10 78.06 78.78 77.65 Sim-CLIP4 72.67 74.65 75.68 74.33 Robust-LLaVA4H 78.88 82.83 84.35 82.02 Robust-LLaVA4G 80.15 83.63 84.89 82.89 Robust-LLaVA4G +CLIP 84.76 86.35 87.72 86.28 Robust-LLaVA4G +FARE4 76.89 79.04 80.03 78.65 Robust-LLaVA4G +Robust-LLaVA4H 78.72 81.58 83.69 81.33 Robust-LLaVA4H +CLIP 85.10 86.82 88.12 86.68 Robust-LLaVA4H +FARE4 76.27 78.27 79.23 77.92 Table 22: Hallucination evaluation using POPE (Accuracy). Vision Encoders POPE Sampling Adversarial Popular Random Mean CLIP 85.60 87.33 88.56 87.16 FARE4 79.27 81.40 81.61 80.76 Sim-CLIP4 76.63 78.00 79.41 78.31 Robust-LLaVA4H 79.03 83.77 85.02 82.61 Robust-LLaVA4G 80.60 84.67 85.64 83.64 Robust-LLaVA4G +CLIP 85.70 87.40 88.52 87.21 Robust-LLaVA4G +FARE4 79.56 81.96 82.50 81.34 Robust-LLaVA4G +Robust-LLaVA4H 79.36 82.76 84.67 82.26 Robust-LLaVA4H +CLIP 85.93 87.80 88.83 87.52 Robust-LLaVA4H +FARE4 79.26 81.50 81.99 80.92 Appendix J Hallucinations In this section, we present an expanded analysis of model hallucination behavior, building upon the results reported in Table 4.2 of the main paper. Our evaluation utilizes the POPE dataset, a benchmark specifically designed for assessing hallucination tendencies. The comprehensive results are presented in Table I for F1-Scores and Table I for accuracy metrics. Our analysis reveals that ensembling robust models with CLIP yields improved performance in hallucination mitigation. However, this improvement pattern does not extend to ensembles incorporating the adversarially fine-tuned CLIP variant, FARE4. This discrepancy highlights a key limitation: the fine-tuning process employed in FARE4 appears to compromise the model’s generalization capabilities. Notably, among the robust model variants, both Robust-LLaVA4G and Robust-LLaVA4H demonstrate substantially better performance in controlling object hallucination compared to FARE4. These results underscore the effectiveness of our approach in maintaining reliable object recognition while preserving model robustness. Appendix K Qualitative Results In this section, we present comprehensive qualitative examples demonstrating the performance of various robust MLLMs under different adversarial scenarios and image corruptions. Our visual analysis spans multiple tasks and evaluation settings. For the COCO image captioning task, we provide extensive examples of model behavior under adversarial attacks in Figures LABEL:fig:untargetted_figures_app_1 through LABEL:fig:untargetted_figures_app_4. These examples illustrate how different models respond to untargeted adversarial perturbations. We further explore targeted adversarial attacks on the same task in Figure LABEL:fig:viz_targetted_attack_1, highlighting the specific challenges posed by directed adversarial manipulations. For visual question answering, Figures 10 and 11 demonstrate model performance on the VQAv2 dataset under adversarial conditions. These examples showcase how adversarial attacks can affect the model’s reasoning capabilities across different question types. Regarding image corruption robustness, we present two complementary sets of results. Figures LABEL:fig:all_corruptions and LABEL:fig:all_corruptions_2 demonstrate model performance across various corruption types, while Figures 15, 16, and 17 provide a detailed examination of model behavior under increasing corruption severity levels. These visualizations help understand how different models maintain performance as image quality degrades. These qualitative results complement our quantitative findings, providing concrete examples of how our robust training approach enhances model reliability across diverse challenging scenarios. The examples consistently demonstrate that Robust-LLaVA4G and Robust-LLaVA4H maintain more reliable and semantically appropriate outputs compared to baseline methods, even under severe adversarial perturbations or image corruptions. Adversarial Image LLaVA output when using Question: Are you able to turn left? FARE4: Yes Sim-CLIP4: Yes Robust-LLaVA4G: No Adversarial Image LLaVA output when using Question: Are there any trees next to the sign? FARE4: No Sim-CLIP4: Yes Robust-LLaVA4G: No Adversarial Image LLaVA output when using Question: Is that a food processor? FARE4: No Sim-CLIP4: No Robust-LLaVA4G: Yes Adversarial Image LLaVA output when using Question: Is the stand empty? FARE4: Yes Sim-CLIP4: No Robust-LLaVA4G: No Adversarial Image LLaVA output when using Question: Is the game played indoors or outdoors? FARE4: Indoors Sim-CLIP4: Outdoors Robust-LLaVA4G: Outdoors Figure 10: Illustration of untargeted ℓ∞subscriptℓ _∞ℓ∞-attacks with ϵ=8/255italic-ϵ8255ε= 8255ϵ = / start_ARG 8 end_ARG start_ARG 255 end_ARG on LLaVA when using different robust vision encoders on Visual Question Answering Task: Adversarial Image LLaVA output when using Question: What is flying in the sky? FARE4: Bird Sim-CLIP4: Bird Robust-LLaVA4G: Airplane Adversarial Image LLaVA output when using Question: Is the sky blue? FARE4: Yes Sim-CLIP4: No Robust-LLaVA4G: Yes Adversarial Image LLaVA output when using Question: Could this man get hurt? FARE4: Yes Sim-CLIP4: No Robust-LLaVA4G: Yes Adversarial Image LLaVA output when using Question: Is the skier in the red jacket using ski poles? FARE4: No Sim-CLIP4: No Robust-LLaVA4G: Yes Adversarial Image LLaVA output when using Question: Is there a clock in this picture? FARE4: No Sim-CLIP4: Yes Robust-LLaVA4G: No Figure 11: Illustration of untargeted ℓ∞subscriptℓ _∞ℓ∞-attacks with ϵ=8/255italic-ϵ8255ε= 8255ϵ = / start_ARG 8 end_ARG start_ARG 255 end_ARG on LLaVA when using different robust vision encoders on Visual Question Answering Task: Impulse Noise (Level 1) LLaVA output when using FARE4: A giraffe eating grass in a field. Sim-CLIP4: A giraffe eating grass in a field. Robust-LLaVA4G: A giraffe eating grass in a field. Impulse Noise (Level 2) LLaVA output when using FARE4: A giraffe eating grass in a field. Sim-CLIP4: A giraffe eating grass in a field. Robust-LLaVA4G: A giraffe eating grass in a field. Impulse Noise (Level 3) LLaVA output when using FARE4: A zebra grazing on grass in a field. Sim-CLIP4: A giraffe eating grass in a field. Robust-LLaVA4G: A giraffe eating grass in a field. Impulse Noise (Level 4) LLaVA output when using FARE4: A zebra is standing in a field of grass. Sim-CLIP4: A giraffe eating grass in a field. Robust-LLaVA4G: A giraffe is standing in a grassy field. Impulse Noise (Level 5) LLaVA output when using FARE4: A dog is standing in a field of grass. Sim-CLIP4: A zebra is standing in a field of grass. Robust-LLaVA4G: A giraffe is standing in a grassy field. Figure 15: Performance Under Increasing Corruption Severity. Visualization of how different robust vision encoders in LLaVA respond to corruption applied at varying severity levels. Impulse Noise (Level 1) LLaVA output when using FARE4: A kitchen with a window and a refrigerator. Sim-CLIP4: A kitchen with a window and a refrigerator. Robust-LLaVA4G: A kitchen with a white refrigerator and a window. Impulse Noise (Level 2) LLaVA output when using FARE4: A window in a room with a view of a kitchen. Sim-CLIP4: A bathroom with a window and a mirror. Robust-LLaVA4G: A kitchen with a window and a refrigerator. Impulse Noise (Level 3) LLaVA output when using FARE4: A window in a room with a view of a kitchen. Sim-CLIP4: A mirror in a room with a window. Robust-LLaVA4G: A kitchen with a window and a refrigerator. Impulse Noise (Level 4) LLaVA output when using FARE4: A window in a room with a view of a doorway. Sim-CLIP4: A window in a room with a mirror. Robust-LLaVA4G: A kitchen with a window and a refrigerator. Impulse Noise (Level 5) LLaVA output when using FARE4: A window with a view of a room. Sim-CLIP4: A window in a room with a view of the outside. Robust-LLaVA4G: A room with a window and a sink. Figure 16: Performance Under Increasing Corruption Severity. Visualization of how different robust vision encoders in LLaVA respond to corruption applied at varying severity levels. Glass Blur (Level 1) LLaVA output when using FARE4: A table with a vase of flowers on it. Sim-CLIP4: A table with a vase of flowers on it. Robust-LLaVA4G: A table with a vase of flowers on it. Glass Blur (Level 2) LLaVA output when using FARE4: A table with a vase on it. Sim-CLIP4: A table with a white tablecloth and a vase on it. Robust-LLaVA4G: A table with a vase of flowers on it. Glass Blur (Level 3) LLaVA output when using FARE4: A view of a room with a window. Sim-CLIP4: A view of a room with a table and chairs. Robust-LLaVA4G: A table with a white tablecloth and a vase of flowers on it. Glass Blur (Level 4) LLaVA output when using FARE4: A window with a view of trees. Sim-CLIP4: A view of a room with a window. Robust-LLaVA4G: A view of a room with a table and chairs. Glass Blur (Level 5) LLaVA output when using FARE4: A window with a reflection of a person. Sim-CLIP4: A black and white photo of a window. Robust-LLaVA4G: A view of a room with a table and chairs. Figure 17: Performance Under Increasing Corruption Severity. Visualization of how different robust vision encoders in LLaVA respond to corruption applied at varying severity levels.

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