Leave My Images Alone: Preventing Multi-Modal Large Language Models from Analyzing Images via Visual Prompt Injection Zedian Shao1∗ , Hongbin Liu2∗ , Yuepeng Hu2 , Neil Zhenqiang Gong2 Georgia Institute of Technology1 , Duke University2 [email protected], {hongbin.liu, yuepeng.hu, neil.gong}@duke.edu
arXiv:2604.09024v1 [cs.CV] 10 Apr 2026
Abstract Multi-modal large language models (MLLMs) have emerged as powerful tools for analyzing Internet-scale image data, offering significant benefits but also raising critical safety and societal concerns. In particular, open-weight MLLMs may be misused to extract sensitive information from personal images at scale, such as identities, locations, or other private details. In this work, we propose ImageProtector, a user-side method that proactively protects images before sharing by embedding a carefully crafted, nearly imperceptible perturbation that acts as a visual prompt injection attack on MLLMs. As a result, when an adversary analyzes a protected image with an MLLM, the MLLM is consistently induced to generate a refusal response such as “I’m sorry, I can’t help with that request.” We empirically demonstrate the effectiveness of ImageProtector across six MLLMs and four datasets. Additionally, we evaluate three potential countermeasures, Gaussian noise, DiffPure, and adversarial training, and show that while they partially mitigate the impact of ImageProtector, they simultaneously degrade model accuracy and/or efficiency. Our study focuses on the practically important setting of open-weight MLLMs and large-scale automated image analysis, and highlights both the promise and the limitations of perturbationbased privacy protection.
1
Probing Question
Image
+
Image with perturbation from ImageProtector
+
Where is the court that this person plays at?
Probing Question Where is the court that this person plays at?
Normal response MLLM
The František Pitoňák Stadium in Brno in Czech Republic.
Refusal MLLM
Unfortunately, I can’t assist with that request.
Figure 1: Illustration of a user leveraging ImageProtector to safeguard their image from being analyzed by an MLLM, preventing the extraction of sensitive information.
model (LLM). The vision encoder generates an image embedding, which the vision-language projector maps to tokens compatible with the LLM, producing a text response. Like any advanced technology, MLLMs are double-edged swords. While they offer numerous benefits as highlighted above, they also pose significant safety and societal risks. In particular, MLLMs can be misused to analyze personal images on the Internet at scale, extracting sensitive information such as individuals’ names and locations (Luo et al., 2025). This threat is further amplified by the availability of many openweight MLLMs, which can be easily accessed and exploited by malicious actors.
Introduction
In this work, we introduce and formalize a novel problem: preventing MLLMs from analyzing images. To address this challenge, we present ImageProtector, a method that subtly perturbs an image to induce refusal responses from MLLMs, regardless of the questions asked about the image. Our primary focus is large-scale, low-cost analysis of online images by adversaries using open-weight MLLMs. As illustrated in Figure 1, a user can apply ImageProtector to perturb their image before posting it online. If a malicious actor downloads
Multimodal large language models (MLLMs) (OpenAI, 2024; Reid et al., 2024; Liu et al., 2024a; Zhu et al., 2024; Dai et al., 2024; Bai et al., 2023b) have become foundational in generative AI applications, including visual question answering (Liu et al., 2024a), image captioning (Karpathy and FeiFei, 2015), and embodied AI (Driess et al., 2023). An MLLM generally consists of a vision encoder, a vision-language projector, and a large language * Equal contributions.
1
the perturbed image and attempts to analyze it with an MLLM, the model will return a refusal response, thereby protecting the user’s sensitive information. We therefore study a proactive user-side defense in which an image owner perturbs an image before sharing it online, with the goal of disrupting downstream automated analysis by such models. The perturbation introduced by ImageProtector acts as a visual prompt injection attack (Bagdasaryan et al., 2023), redirecting MLLMs to generate refusal responses regardless of the intended task. While prompt injection attacks are typically viewed as offensive techniques for subverting the safety and security of LLMs and MLLMs, our primary contribution is the novel formulation of this problem: using visual prompt injection as a defensive mechanism to protect user privacy by inducing universal refusal responses. We are the first to demonstrate this potential, shifting prompt injection from a purely offensive technique to a tool for safeguarding user images. Our goal is not to “break” MLLMs broadly, but to investigate whether users can opt in to privacy-preserving protection of images they own before publication. ImageProtector optimizes image perturbations with two key goals: effectiveness, ensuring that MLLMs consistently produce refusal responses, and utility, keeping the perturbations visually nearly imperceptible to preserve the image’s quality and usability. This is formulated as a constrained optimization problem, where the objective focuses on maximizing effectiveness, while the utility goal imposes a constraint on the magnitude of the perturbation. To solve this problem, ImageProtector employs gradient-based optimization methods to efficiently generate the desired perturbation. Our main contributions are as follows:
2
Related Work
2.1
MLLMs
MLLMs (Liu et al., 2024a; Zhu et al., 2024; Dai et al., 2024; Bai et al., 2023b) extend Large Language Models (LLMs) to process visual inputs, generating text responses to image-text prompts. They typically consist of three components: a vision encoder, a vision-language projector, and an LLM. The vision encoder converts images into embeddings, often using pre-trained models on large image datasets or image-text pairs (Oquab et al., 2023; Chen et al., 2020; Radford et al., 2021), with stateof-the-art implementations leveraging CNNs or Vision Transformers (ViTs) (Radford et al., 2021; Liu et al., 2024b). The vision-language projector aligns these embeddings to the LLM’s token space via cross-attention layers (Lin et al., 2022) or feed-forward networks (FNNs). Finally, the LLM integrates the projected embeddings with text tokens to generate responses, employing transformerbased architectures (Vaswani et al., 2017) to model long-range dependencies and context for improved language modeling and question answering. 2.2
Visual Prompt Injection
Adversarial examples are deliberately crafted inputs designed to cause machine learning models to produce incorrect predictions (Szegedy et al., 2014). For MLLMs, adversarial examples can target either images (Schlarmann and Hein, 2023; Bagdasaryan et al., 2023; Qi et al., 2024; Luo et al., 2024a; Bailey et al., 2023; Carlini et al., 2024; Huang et al., 2024; Zhao et al., 2024) or text-based prompts (Alzantot et al., 2018; Jones et al., 2023). In this work, we focus specifically on image-based adversarial examples, motivated by scenarios where users modify their images to prevent analysis by MLLMs while having no control over the accompanying questions or text prompts used during the analysis. Two prominent categories of image-based adversarial examples targeting MLLMs are jailbreaking, which bypasses safety mechanisms to elicit harmful responses (Qi et al., 2024; Carlini et al., 2024; Luo et al., 2024b; Gong et al., 2025), and visual prompt injection, which embeds a hidden prompt within an image to manipulate the model’s output (Bagdasaryan et al., 2023). Our work introduces a novel application of image-based adversarial examples in the form of visual prompt injection. Specifically, the perturbation introduced by Image-
• We formalize a novel problem: preventing MLLMs from analyzing images. • We propose ImageProtector, a constrained optimization-based method that generates protective perturbations to simultaneously achieve the goals of effectiveness and utility. • We demonstrate the success of ImageProtector across six MLLMs and four datasets. • We evaluate three countermeasures, Gaussian noise, DiffPure (Nie et al., 2022), and adversarial training (Goodfellow et al., 2015), and show that while they mitigate the impact of ImageProtector to some extent, they do so at the cost of reduced accuracy or efficiency. 2
Protector acts as a visual prompt injection attack, compelling MLLMs to produce refusal responses as the injected task regardless of the malicious actor’s original intended task of private information extraction through probing questions. While visual prompt injections have traditionally been considered attack techniques, our approach uniquely demonstrates their potential as a defensive mechanism to safeguard user’s images. Crucially, ImageProtector addresses a new threat model focused on inducing injected refusals for malicious probing questions, a previously underexplored dimension. ImageProtector is designed specifically to induce generic, safe refusal responses to a wide range of benign but privacyinvasive questions. Prior multimodal adversarial attacks are mainly designed either to elicit unsafe behaviors or to degrade model performance, whereas ImageProtector studies perturbations as an opt-in defensive mechanism for privacy-preserving refusal. This necessitates a different optimization objective (detailed in Section 4.3) designed to enforce effectiveness and utility simultaneously. This distinguishes our approach from prior methods primarily aimed at misclassification and jailbreaking for unsafe content. While prior methods (Qi et al., 2024; Bagdasaryan et al., 2023) could be adapted for this purpose, our experiments (Section 5.2) demonstrate that they are suboptimal due to their design for different objectives.
3
Problem Definition
3.1
Problem Setup
injection attack. The goal is that if a malicious actor subsequently downloads the protected image and queries an MLLM about it, the model will be misled into generating a generic refusal response, such as “Unfortunately, I cannot assist with that request,” regardless of the original probing question posed. This strategy effectively protects the user’s image from analysis by malicious actors using MLLMs, thus preserving user’s private information. This scenario is particularly relevant as users increasingly seek methods to protect digital content from AI-based analysis, paralleling efforts to prevent attribute inference (Jia and Gong, 2018), membership inference (Jia et al., 2019), facial recognition (Shan et al., 2020; Cherepanova et al., 2021), and location information extraction (Luo et al., 2025). 3.2
Threat Model
We define the threat model from the perspective of the image owner. The primary threat is the unauthorized, large-scale, privacy-invasive analysis of an owner’s images by a malicious actor using MLLMs. We focus on this setting because open-weight models substantially lower the cost of automated image analysis relative to commercial black-box APIs, which are typically rate-limited and more likely to expose suspicious bulk access patterns. To proactively defend against such analyses, the image owner applies protective perturbations that serve as visual prompt injection attacks. As illustrated in Figure 1, the image owner crafts these perturbations and shares the images online, e.g., on social media. When a malicious actor attempts to use an MLLM to analyze the perturbed image, embedded prompt injection will cause the model to generate a refusal response. In creating this perturbation, the image owner has two fundamental goals: effectiveness and utility.
The problem setup involves two main parties: a user, hereafter referred to as the image owner, who wishes to share personal images online, and a malicious actor seeking to exploit these images. The proliferation of powerful, open-weight, and easily accessible MLLMs has created a significant privacy threat. Malicious actors can leverage these models to perform large-scale analysis of personal images, extracting sensitive information such as individuals’ identities, locations, or private details without the owner’s consent. To counter this threat, we approach the problem from a defensive standpoint. We propose that an image owner can use a protective tool, which we refer to as ImageProtector, before sharing their images online. This tool protects the image by embedding a subtle, nearly human-imperceptible perturbation into the image, which functions as a visual prompt
• Effectiveness goal: The perturbation embedded in the image must reliably function as a visual prompt injection attack, causing MLLMs to generate refusal responses to a wide range of probing questions about the image. • Utility goal: The perturbation should remain nearly imperceptible to humans. This ensures the image’s visual quality is preserved, allowing it to be shared and viewed online without any noticeable degradation. Image owner’s background knowledge: We assume that the image owner has white-box access 3
Shadow questions ... When is the event taking place? What color is the object? ... Image
MLLM
Image + Perturbation
Logits
Refusal tokens
Visual Prompt Injection Attack
Cross-Entropy Loss Competing
BIM +
Perturbation
MLLM
I'm sorry, I can't assist with that request.
Probing question Where is the court that
Refusal
this person plays at?
Update
Figure 2: Overview of our ImageProtector.
to one or more target open-weight MLLMs of concern, including access to gradients. This assumption is realistic in our target setting because many strong MLLMs are openly released and can be run locally by both attackers and defenders. These open-weight models present a greater threat, as they significantly lower the economic barrier for malicious actors to conduct large-scale analysis to extract information from image owners who frequently post images online, compared to closedsource models. Our method also enables the image owner to optimize a universal protective perturbation that is effective across multiple MLLMs simultaneously. The image owner has access to a set of shadow questions for each image, categorized as exact, similar, or general probing questions to guide the optimization process. We elaborate further on shadow question construction in Section 4.2. Image owner’s capability: The image owner’s ability is limited to adding perturbations to their images before publishing them online. They cannot alter the target MLLMs’ parameters, affect their training, or modify the probing queries from malicious actors. Thus, the integrity of both MLLMs and the malicious actors’ probing queries remains intact.
4
Our ImageProtector
4.1
Overview
questions caused by visual prompt injection attack. We hypothesize that refusal on real malicious probing questions arises from transfer across question formulations that optimizing on diverse shadow questions encourages the perturbation to generalize to semantically related, and even partially out-ofdomain, probing questions posed by adversaries. Finally, to preserve utility, ImageProtector enforces an ℓ∞ -norm constraint during optimization. Formally, we frame the task of finding protective perturbation, functioning as visual prompt injection, as a constrained optimization problem and solve it using a gradient-based approach. 4.2
Constructing Shadow Questions
The image owner must anticipate the types of questions a malicious actor might ask and identify the information that requires protection from analysis. We categorize these potential probing questions into three types: Exact probing questions: If the image owner can precisely anticipate the probing questions a malicious actor might pose, e.g., “Where was this photo taken?”, shadow questions can be constructed to directly match them. Similar probing questions: When the image owner understands the general intent behind potential questions and can specify the thematic information requiring protection, they can create an example question and use an LLM to generate a set of similar probing questions. Our ImageProtector employs the prompt provided in Figure 12, with an example shown in Figure 17 in the Appendix. General probing questions: If specific probing questions are unknown, and the image owner aims to prevent the malicious actor from extracting any sensitive information, an LLM can generate general questions simulating typical probing queries about any image. Our ImageProtector employs the prompt illustrated in Figure 13, with an example provided in Figure 15 in the Appendix.
Figure 2 provides an overview of our ImageProtector. Given an image, ImageProtector optimizes a perturbation that satisfies both effectiveness and utility goals. First, we generate shadow questions using an LLM (e.g., GPT-4); these questions can be exact, similar, or general probing questions, depending on the image owner’s preference and anticipation of malicious actor’s probing questions. Next, ImageProtector optimizes the perturbation so that target MLLMs are likely to refuse prompts containing the perturbed image along with shadow 4
(a) Original (ϵ = 0)
(b) ϵ = 4/255
(c) ϵ = 8/255
(d) ϵ = 12/255
(e) ϵ = 16/255
Figure 3: Images without and with perturbations added by our ImageProtector under different ℓ∞ -norm constraint ϵ.
4.3
Formalizing the Image Owner’s Goals
tive:
Let M be a set of target MLLMs and QS the set of shadow questions. Given an image xI , the image owner seeks a perturbation δR which effectively conduct a visual prompt injection attack on each MLLM, causing it to output a refusal response R with high probability when queried with xI + δR and any q ∈ QS . Figure 16 in Appendix shows 10 refusal responses collected using GPT-4. For each xI , we sample a refusal response R uniformly from these 10 to enhance stealthiness and diversity. The probability that an MLLM M ∈ M refuses on input xI + δR and q ∈ QS is denoted as pM (R|[xI + δR , q]). The image owner solves: ∗ δR = argmax δR
∗ = argmin δR δR
where r is the number of tokens in R. For simplicity, we define LCE (M, R, xI + δR , q) = P − rk=1 log Tk (M, R, xI + δR , q) and the overall objective as L(M, R, xI + δR , QS ) = P P M ∈M q∈QS LCE (M, R, xI + δR , q). We use refusal responses as the target sequence because they are generic, semantically coherent across many probing questions, and aligned with existing safety behavior in current MLLMs. However, the formulation itself does not require refusals specifically and any target response sequence could be substituted into the same optimization framework.
M ∈M q∈QS
(1)
∗ is the optimized perturbation and ϵ enwhere δR forces utility via an ℓ∞ -norm constraint. Figure 3 shows images without and with perturbations under different small ϵ. For an MLLM, the probability of producing R = (t1 , t2 , . . . , tr ) as tokens given (xI + δR , q) factorizes as:
4.4
=
Tk (M, R, xI + δR , q),
Solving the Optimization Problem
ImageProtector solves the optimization problem in Equation 3 using the basic iterative method (BIM) (Kurakin et al., 2018). We initialize the perturbation as a zero tensor matching the dimensions of xI . In each iteration, we sample a mini-batch of shadow questions QB ⊆ QS and compute the gradient g = ∇δR L(M, R, xI + δR , QB ). ImageProtector then updates δR as δR = δR −α·sign(g), where α is the step size and sign(·) denotes the sign function. We project δR = proj(δR , ϵ) at each iteration to ensure ||δR ||∞ ≤ ϵ. This process repeats for max_iter iterations. Algorithm 1 summarizes ImageProtector. In Section 5.2, we show that using projected gradient descent (PGD) (Madry et al., 2018) achieves similar effectiveness but is less efficient.
pM (R | [xI + δR , q]) r Y = pM (tk | [xI + δR , q, t1 , . . . , tk−1 ]) k=1 r Y
M ∈M q∈QS k=1
− log Tk (M, R, xI + δR , q) |M| · |QS | s.t. ||δR ||∞ ≤ ϵ, (3)
X X pM (R|[xI + δR , q]) |M| · |QS | s.t. ||δR ||∞ ≤ ϵ,
r X X X
(2)
k=1
where Tk (M, R, xI + δR , q) denotes conditional probability pM (tk | [xI + δR , q, t1 , . . . , tk−1 ]). Since pM (R | [xI + δR , q]) is typically nonconvex in δR due to the non-linearity of neural networks, we reformulate Equation 1 as a crossentropy loss for a smoother, differentiable objec5
Algorithm 1 ImageProtector.
context, potentially to probe for unexpected model behaviors. For this, we use 100 randomly sampled questions from CommonsenseQA (Talmor et al., 2019), a dataset of general knowledge queries, and pair them with images from our datasets. For instance, “What is a likely consequence of ignorance of rules?” serves as an image-irrelevant question.
1: Input: Image xI , shadow questions set QS ,
step size α, maximum iterations max_iter, ℓ∞ -norm constraint ϵ, ℓ∞ -norm projection function proj, and sign function sign 2: Output: Protective perturbation δR 3: δR ← 0 4: for iteration = 1 to max_iter do 5: Randomly select a mini-batch QB from QS 6: g ← ∇δR L(M, R, xI + δR , QB ) ▷ compute gradient 7: δR ← proj(δR − α · sign(g), ϵ) ▷ BIM 8: end for 9: return δR
5
Evaluation
5.1
Experimental Setup
5.1.1
5.1.3 MLLMs We evaluate ImageProtector on six popular openweight MLLMs: LLaVA-1.5 (Liu et al., 2024a), MiniGPT-4 (Zhu et al., 2024), Qwen-VL-Chat (Bai et al., 2023b), InstructBLIP (Dai et al., 2024), Phi4-multimodal-instruct (Abouelenin et al., 2025), and Qwen2.5-VL-7B-Instruct (Bai et al., 2025). These models employ different vision encoders, LLMs, and vision-language projectors, summarized in Table 8 in Appendix. For consistency, all image inputs are resized to 224×224 pixels. Phi-4multimodal-instruct and Qwen2.5-VL-7B-Instruct require updated dependencies and different image sizes, leading to incompatible environments. Thus, they are excluded from multiple target MLLMs experiments. We adopt unified resizing as a controlled choice for multi-model optimization convenience. This design is not required when protecting against a single target model, and in principle perturbations could also be optimized at modelspecific resolutions.
Datasets
To assess ImageProtector, we use image-question pairs from VQAv2 (Antol et al., 2015), GQA (Hudson and Manning, 2019), and TextVQA (Singh et al., 2019). Additionally, we extend CelebA (Liu et al., 2015) into a visual question dataset to simulate queries about personal images, a primary use case for ImageProtector. The details of question generation are in Appendix A. Table 1 summarizes dataset statistics. For evaluation, we randomly sample 100 image-question pairs from each dataset’s test or validation split.
5.1.4 Evaluation Metrics We evaluate the effectiveness of our ImageProtector using the refusal rate. Given an MLLM M and a dataset of N image-question pairs with protective perturbations from ImageProtector, the refusal rate is NNR , where NR is the number of refusals by M on perturbed pairs. To account for MLLM response randomness due to sampling and decoding strategies, each MLLM is queried three times per image-question pair, and the refusal rates are averaged. To evaluate whether an MLLM’s response is a refusal, we use GPT-4 (Achiam et al., 2023) as a refusal judge. The MLLM’s response is assessed using the prompt in Figure 14 in Appendix.
Table 1: Dataset statistics. # Image-question
# Ground-truth
Pairs
Answers
VQAv2
1,105,904
11,059,040
GQA
22,669,678
22,669,678
TextVQA
45,336
453,360
CelebA
202,599
0
Dataset
5.1.2
Probing Questions
To simulate real-world analysis by a malicious actor, we consider both image-relevant and imageirrelevant probing questions. Image-relevant questionsare directly related to the image content and represent a malicious actor’s primary method for extracting specific information. These are sourced from the questions associated with each dataset. Image-irrelevant questions model a scenario where a malicious actor might ask general knowledge questions while an image remains in the MLLM’s
5.1.5 Compared Methods We extend two existing image-based adversarial attacks on MLLMs (Bagdasaryan et al., 2023; Qi et al., 2024) to induce refusals for safe prompts. Additionally, we evaluate a variant of our ImageProtector. Detailed descriptions for compared methods are in Appendix B. 6
Table 2: Refusal rates of compared methods and ImageProtector using three types of shadow questions, with LLaVA-1.5 as the target MLLM on VQAv2 dataset. Method No Perturbation Qi et al. (Qi et al., 2024) Bagdasaryan et al. (Bagdasaryan et al., 2023) ImageProtector+PGD ImageProtector
Table 3: GPU-minutes of ImageProtector and ImageProtector + PGD for optimizing perturbation per image, with LLaVA-1.5 as the target MLLM on VQAv2 dataset.
Exact Similar General Probing Probing Probing Questions Questions Questions 0.00 0.00 0.00 0.02
0.02
0.02
0.65
0.62
0.51
0.94 0.94
0.91 0.88
0.91 0.88
Exact Similar General Probing ProbingUser ProbingUser Questions Questions Questions ImageProtector+PGD 16.2 61.2 61.2 ImageProtector 10.2 45.6 45.6 Method
Table 4 reports LLaVA-1.5’s accuracy on VQAv2 for the compared methods and ImageProtector with three shadow question types. While LLaVA-1.5 performs well on original unperturbed images, accuracy drops sharply with protective perturbations. Qi et al. reduces accuracy by nearly half, while Bagdasaryan et al. and ImageProtector lower it to nearly zero. This underscores the effectiveness of perturbations in disrupting MLLM comprehension, leading to inaccurate responses.
5.1.6 Parameter Setting Unless stated otherwise, we consider one target MLLM and image-relevant questions. For shadow questions, we use one exact probing question, ten similar probing questions, and fifty general probing questions. Section 5.2 examines the impact of shadow question quantity in the cases of similar or general probing questions. To ensure utility, we constrain the refusal perturbation with an ℓ∞ norm bound of 8/255, aligning with prior work (Qi et al., 2024; Luo et al., 2024a; Bailey et al., 2023). We conduct a grid search for key hyperparameters in ImageProtector (Algorithm 1): step size α, maximum iterations, and mini-batch size of shadow questions, tailored to the image owner’s background knowledge and choices (exact, similar, or general probing questions). Section 5.2 further analyzes these hyperparameters. To prevent overfitting when using similar or general probing questions, we implement early stopping if the loss in Equation 3 stays below 0.001 for 30 consecutive iterations. 5.2
Table 4: Accuracy of compared methods and ImageProtector using three types of shadow questions, with LLaVA-1.5 as the target MLLM on the VQAv2 dataset. Method No Perturbation Qi et al. (Qi et al., 2024) Bagdasaryan et al. (Bagdasaryan et al., 2023) ImageProtector+PGD ImageProtector
Exact Similar General Probing ProbingUser Probing Questions Questions Questions 0.92 0.92 0.92 0.48
0.48
0.48
0.03
0.04
0.03
0.03 0.03
0.03 0.04
0.04 0.03
ImageProtector achieves the effectiveness goal: ImageProtector effectively meets the effectiveness goal, as shown in Tables 5, 6, and 7, which report refusal rates across the four datasets and six MLLMs using exact, similar, and general shadow questions. We make four key observations. First, ImageProtector generally exhibits slightly higher refusal rates for image-relevant questions than image-irrelevant ones, with average refusal rates of 0.95, 0.91, and 0.86 versus 0.94, 0.90, and 0.84 for exact, similar, and general shadow questions, respectively. Second, ImageProtector achieves higher refusal rates when shadow questions closely resemble actual malicious actor’s probing questions, reinforcing the effectiveness of protective perturbations when the distributions align. Third, ImageProtector yields the lowest refusal rates on InstructBLIP except for exact, image-relevant shadow questions, likely due to its Q-Former (Li et al., 2023)-based vision-language projector, which has the most parameters among
Experimental Results
ImageProtector outperforms compared methods: Table 2 shows the refusal rates of ImageProtector and baseline methods on three shadow question types, using LLaVA-1.5 as the target MLLM on VQAv2. First, ImageProtector consistently achieves the highest refusal rate at 0.88, outperforming Bagdasaryan et al. at 0.51 on general probing questions. Second, Qi et al. performs poorly, with near-zero refusals, as it optimizes refusals without shadow questions, so the optimized perturbation is tied to a narrow prompt pattern and fails to generalize to diverse probing formulations. Third, ImageProtector + PGD achieves similar refusal rates but requires more iterations, shown in Table 3, increasing computational costs. Thus, we use ImageProtector in subsequent experiments. 7
Table 5: Refusal rates of ImageProtector with exact probing questions as shadow questions when using (a) image-relevant and (b) image-irrelevant questions on four MLLMs and four datasets. ‘Avg.’ denotes average.
Table 7: Refusal rates of ImageProtector with general probing questions as shadow questions when using (a) image-relevant and (b) image-irrelevant questions on four MLLMs and datasets. ‘Avg.’ denotes average.
(a) Image-relevant questions
(a) Image-relevant questions
Target MLLM
Target MLLM
VQAv2 GQA CelebA TextVQA Avg.
LLaVA-1.5 MiniGPT-4 Qwen-VL-Chat InstructBLIP Phi-4-multimodal Qwen2.5-VL Avg.
0.94 0.86 0.94 0.91 1.00 0.96 0.94
0.94 0.93 0.95 0.94 1.00 1.00 0.96
1.00 0.97 0.99 0.93 1.00 1.00 0.98
0.91 0.81 0.88 0.92 0.98 0.97 0.91
0.95 0.89 0.94 0.93 1.00 0.98 0.95
LLaVA-1.5 MiniGPT-4 Qwen-VL-Chat InstructBLIP Phi-4-multimodal Qwen2.5-VL Avg.
(b) Image-irrelevant questions Target MLLM
0.91 0.90 0.93 0.89 0.99 0.95 0.93
0.94 0.93 0.96 0.87 1.00 0.98 0.95
0.98 0.96 0.94 0.90 1.00 1.00 0.96
Target MLLM
0.90 0.84 0.91 0.84 0.97 0.97 0.90
0.93 0.91 0.94 0.88 0.99 0.97 0.94
LLaVA-1.5 MiniGPT-4 Qwen-VL-Chat InstructBLIP Phi-4-multimodal Qwen2.5-VL Avg.
Table 6: Refusal rates of ImageProtector with similar probing questions as shadow questions when using (a) image-relevant and (b) image-irrelevant questions on four MLLMs and datasets. ‘Avg.’ denotes average.
VQAv2 GQA CelebA TextVQA Avg.
LLaVA-1.5 MiniGPT-4 Qwen-VL-Chat InstructBLIP Phi-4-multimodal Qwen2.5-VL Avg.
0.88 0.88 0.94 0.89 0.93 0.94 0.91
0.91 0.97 0.95 0.93 0.90 0.93 0.93
1.00 0.98 0.98 0.89 0.87 0.95 0.94
0.81 0.88 0.86 0.90 0.85 0.85 0.86
0.90 0.93 0.93 0.90 0.89 0.92 0.91
(b) Image-irrelevant questions Target MLLM LLaVA-1.5 MiniGPT-4 Qwen-VL-Chat InstructBLIP Phi-4-multimodal Qwen2.5-VL Avg.
VQAv2 GQA CelebA TextVQA Avg. 0.92 0.93 0.91 0.83 0.94 0.94 0.91
0.92 0.96 0.97 0.84 0.90 0.93 0.92
0.94 0.99 0.96 0.87 0.85 0.94 0.92
0.91 0.96 0.87 0.81 0.84 0.83 0.87
0.96 0.98 0.96 0.80 0.81 0.87 0.90
0.86 0.86 0.75 0.83 0.79 0.74 0.80
0.90 0.93 0.87 0.81 0.83 0.82 0.86
0.82 0.93 0.89 0.83 0.86 0.83 0.86
VQAv2 GQA CelebA TextVQA Avg. 0.90 0.94 0.87 0.77 0.85 0.80 0.86
0.92 0.97 0.87 0.77 0.81 0.82 0.86
0.97 0.95 0.86 0.87 0.81 0.87 0.89
0.84 0.87 0.73 0.70 0.77 0.72 0.77
0.91 0.93 0.83 0.78 0.81 0.80 0.84
get MLLMs. For each image, ImageProtector optimizes a universal refusal perturbation to satisfy the image owner’s two goals across all target MLLMs. To reduce GPU costs, we sample 10 image-question pairs from VQAv2. Starting with LLaVA-1.5, additional target MLLMs are randomly added from the remaining three. All hyperparameters are fixed except for the iteration limit in ImageProtector (Algorithm 1) to ensure loss convergence, setting to 2500, 4500, and 4500 for two, three, and four MLLMs, respectively. ImageProtector consistently meets effectiveness and utility goals. For example, with LLaVA-1.5, MiniGPT-4, and Qwen-VL-Chat as target MLLMs, it achieves refusal rates of 0.90, 0.80, and 0.80. Ablation study of ImageProtector: We conducted a comprehensive ablation study to analyze the impact of key hyperparameters in our method, with the full analysis and corresponding figures provided in Appendix C. Our findings show that the optimal hyperparameter settings often involve a balance. For instance, the optimal step size α depends on the type of shadow questions. Experiments shows higher optimal step size for exact probing questions (around 0.007) than for similar or general ones (around 0.005). The number of iterations also presents a trade-off. Performance on similar questions degrades after 1500 iterations, likely due to overfitting to shadow questions. The perturbation constraint ϵ is most effective at 8/255,
(a) Image-relevant questions Target MLLM
0.88 0.90 0.89 0.81 0.87 0.83 0.86
(b) Image-irrelevant questions
VQAv2 GQA CelebA TextVQA Avg.
LLaVA-1.5 MiniGPT-4 Qwen-VL-Chat InstructBLIP Phi-4-multimodal Qwen2.5-VL Avg.
VQAv2 GQA CelebA TextVQA Avg.
0.90 0.95 0.93 0.84 0.89 0.91 0.90
compared MLLMs shown in Table 8 in Appendix and enhances robustness against perturbations. Finally, ImageProtector consistently achieves the highest refusal rates on CelebA across all MLLMs, likely because facial images are often considered sensitive in MLLM alignment to enforce refusals. Multiple target MLLMs: Figure 4 shows ImageProtector’s refusal rates against multiple tar8
hat IP 1.5 GPT-4 n-VL-C ructBL A i V e LLa Min Qw Inst 1.0 LLaVA-1.5 0.90 0.00 0.03 0.00 0.8 LLaVA-1.5 0.90 0.90 0.03 0.00 0.6 +MiniGPT-4 LLaVA-1.5 +MiniGPT-4 0.90 0.80 0.80 0.00 +Qwen-VL-Chat LLaVA-1.5 +MiniGPT-4 0.80 0.80 0.70 0.60 +Qwen-VL-Chat +InstructBLIP Target MLLM(s)
Gaussian noise: Figure 10a in Appendix presents the target MLLM’s accuracy on VQAv2 and ImageProtector’s refusal rates when apply Gaussian noise. We observe that larger σ values better mitigate ImageProtector’s impact. Without noise (σ = 0), ImageProtector’s refusal rates exceed 0.90, while at σ = 0.02, they drop to nearly zero. However, noise degrades accuracy from 0.92 to around 0.80 at σ = 0.02, making it an insufficient defense.
0.4 0.2 0.0
DiffPure (Nie et al., 2022): Figure 10b in Appendix shows the accuracy of the target MLLM and ImageProtector’s refusal rates across different timesteps of Diffpure. Diffpure reduces ImageProtector’s effectiveness but significantly degrades the target MLLM’s accuracy. A single timestep lowers ImageProtector’s refusal rate from above 0.90 to below 0.20, while accuracy drops from 0.92 to 0.82. Two timesteps further reduce refusal rates to near zero, while accuracy declines to 0.78. Additionally, DiffPure increases inference time by 8.0% for one timestep and 13.1% for two, impacting computational cost.
Figure 4: Refusal rates of ImageProtector with multiple target MLLMs. The VQAv2 dataset is used, with general probing questions being used as shadow questions. Each row indicates the set of MLLMs jointly included in the optimization objective when generating one universal perturbation per image. Each column reports the refusal rate measured on a specific evaluation model. For example, the third row means that the perturbation was jointly optimized against LLaVA-1.5, MiniGPT-4, and Qwen-VL-Chat, and then evaluated on each of the four models shown as columns.
as smaller values underfit due to a restricted search space and larger ones overfit to shadow questions. Performance of generalization stabilizes with a mini-batch size of at least 3 and a set of at least 40 shadow questions. Notably, our approach is robust to different settings of MLLM’s temperature. A detailed breakdown of each hyperparameter’s impact is available in the appendix.
6
Adversarial training (Goodfellow et al., 2015): Figure 11 in Appendix illustrates the target MLLM’s accuracy and ImageProtector’s refusal rates when using adversarial training across training epochs. Using three shadow question types with LLaVA-1.5 on VQAv2, we find that ImageProtector’s refusal rates remain around 60% even after three epochs, while the target MLLM’s accuracy significantly decreases. Additionally, adversarial training also requires significant computational resources.
Countermeasures
Images with protective perturbation functioning as visual prompt injection from ImageProtector are adversarial examples. Existing defenses (Nie et al., 2022; Goodfellow et al., 2015; Cao and Gong, 2017; Liu et al., 2024c) fall into testingtime and training-time categories. We evaluate ImageProtector against two testing-time defenses, Gaussian noise and DiffPure (Nie et al., 2022), and one training-time defense, adversarial training (Goodfellow et al., 2015). Appendix D details these countermeasures. These countermeasures can also be interpreted as approximate attacker-side bypass strategies in our threat model. To assess ImageProtector’s impact, we measure refusal rate and accuracy, where accuracy quantifies correctly answered image-question pairs on clean images with countermeasures applied. Our results show that while these countermeasures mitigate ImageProtector’s effect on the target MLLM, they also degrade its accuracy and/or efficiency.
7
Conclusion
We presented ImageProtector, a proactive, privacypreserving method that embeds a nearly imperceptible perturbation into images to trigger visual prompt–based refusals from MLLMs. By optimizing a universal, sequence-level objective, ImageProtector consistently prevents sensitive content extraction and achieves high refusal rates across six MLLMs and four datasets. Robustness studies show that common countermeasures (e.g., Gaussian noise, DiffPure, adversarial training) do not fully negate the protection without incurring notable accuracy or efficiency costs. Overall, ImageProtector offers a practical, front-line defense for users before sharing images online. 9
Limitations
No. 2450935, 2125977, and 2112562.
Multi-round visual question answering: ImageProtector primarily addresses image-based multiround VQA, with performance varying based on question formulations and context length. Protective perturbations become less effective in extended interactions, especially for general probing queries. Figure 6 in Appendix demonstrates refusal rates across multiple rounds for various shadow question types, using the same probing question repeatedly for simplicity. For shadow questions closely resembling the actual probing question, refusal rates remain high, decreasing by less than 5% across rounds. However, with general probing questions, longer context lengths reduce perturbation effectiveness, dropping refusal rates from 88% to approximately 70%. Future work could enhance the robustness of ImageProtector by incorporating diverse shadow questions and integrating multi-round interactions during perturbation generation. Diverse modalities: While ImageProtector targets visual inputs, emerging MLLMs increasingly support additional modalities like audio and video, where perturbation-based privacy protection techniques remain unexplored. Extending perturbation strategies to multimodal inputs presents a valuable direction for future work for proactively protecting user’s privacy. Closed-source MLLMs: A key limitation of ImageProtector is its dependence on a white-box setting. This constraint limits direct applicability to proprietary, closed-source MLLMs offering only black-box API access. Our work deliberately focuses on the white-box threat model because the proliferation of powerful, open-weight MLLMs presents a significant and growing privacy threat, drastically lowering the barrier for malicious actors to conduct large-scale image analysis. Although our work demonstrates the feasibility of visual prompt injection to protect images from MLLM analysis, extending this method to blackbox settings exceeds the scope of our study. Such an extension would necessitate fundamentally different techniques, like surrogate model training or query-based optimization, entailing significant computational and economic challenges suitable for dedicated investigation.
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We appreciate the reviewers’ constructive feedback. This research was partially supported by NSF grant 10
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12
A
Generating Questions for CelebA
values rather than gradient signs. We set the learning rate as 0.3 with maximum of 1500 iterations when shadow questions are exact user questions and 0.4 with maximum of 2000 iterations when they are similar or general user questions.
To generate relevant questions, we use GPT-4 with the prompt: “Imagine there is an image of a person’s face. Please list 10 general questions that people may ask about the person’s private information from the image." The generated 10 questions are shown below. For each image in CelebA, we randomly choose one of these 10 generated questions.
C
Impact of step size α: The step size α in ImageProtector (Algorithm 1) controls the magnitude of protective perturbation. Figure 7 in the Appendix shows its effect across three shadow question types. For exact shadow questions shown in Figure 7a, increasing α from 0.006 to 0.007 significantly boosts refusal rates, with 0.007 being optimal. For similar and general questions shown in Figures 7b and 7c, refusal rates are less sensitive, peaking at 0.005. Thus, optimal α varies by question type, higher for exact shadow questions (around 0.007) and lower for similar or general shadow questions (around 0.005). Impact of the maximum number of iterations: Figure 8 in the Appendix shows how the maximum number of iterations influences refusal rates on VQAv2. For exact shadow questions shown in Figure 8a, refusal rates rise and stabilize beyond 1000 iterations. For similar and general shadow questions shown in Figures 8b and 8c, rates initially increase but decline after 1500 iterations, likely due to overfitting to shadow questions, which prevents from generalization to real malicious actor’s probing questions. Impact of the perturbation constraint: ImageProtector enforces an ℓ∞ -norm constraint ϵ for utility goal. Following prior work (Qi et al., 2024; Luo et al., 2024a; Bailey et al., 2023), we set ϵ < 16/255, a standard stealthy threshold. As shown in Figure 9a in the Appendix, refusal rates peak at ϵ = 8/255 but decline with larger values, likely due to overfitting to shadow questions. Conversely, very small ϵ (e.g., 4/255) may underfit due to a restricted search space. Impact of the mini-batch size of shadow questions: ImageProtector samples a mini-batch of shadow questions each iteration. Figure 9b in the Appendix shows that refusal rates rise from 0.82 to 0.86 as the mini-batch size increases from 1, stabilizing beyond size 3. This suggests that batch sizes below 3 are suboptimal. Impact of the size of shadow questions: Figure 9c in the Appendix shows that increasing the shadow question set from 20 to over 40 improves
• Who is this person? • What is the ethnic or racial background of this person? • Is this person in a relationship? • Where does this person live? • What is this person’s religious or political affiliation? • How much money does this person make? • What is this person’s sexual orientation? • Does this person have any health issues? • Has this person had any cosmetic procedures? • What is this person’s legal status or citizenship?
B
Ablation Study
Compared Methods
Qi et al. (Qi et al., 2024): This method perturbs images to elicit toxic responses from MLLMs when the set of shadow questions is empty. The intuition is that the MLLM will likely provide toxic answers to any unsafe prompt containing the perturbed image and an unsafe question. In our extension, we optimize protective perturbations while keeping the shadow question set empty. Bagdasaryan et al. (Bagdasaryan et al., 2023): This approach optimizes perturbations token by token. Given a refusal R of r tokens, our ImageProtector optimizes the probability of the full sequence in one step (Equation 2), whereas their method optimizes each token sequentially to increase the probability of each desired next token given its prefix, requiring r steps. ImageProtector + PGD: This variant replaces the basic iterative method (BIM) with projected gradient descent (PGD) (Madry et al., 2018) for optimizing perturbations. PGD uses exact gradient 13
refusal rates from 0.86 to 0.88, after which performance stabilizes. This indicates that using at least 40 shadow questions improves effectiveness for better generalization. Impact of the temperature of target MLLM: The temperature in an MLLM controls response randomness, with lower values yielding more deterministic answers and higher values increasing variability. Figure 5 in the Appendix shows that ImageProtector maintains high refusal rates, from 0.86 to 0.89, across different MLLM temperatures, indicating robustness to temperature variations.
D
Refusal Rate
1.00 0.90 0.85 0.80 0.75
Details of Countermeasures
0.0
0.2
0.4
0.6
0.8
1.0
Temperature of the Target MLLM
Gaussian noise: A target MLLM can counter perturbations by adding Gaussian noise N (0, σ) to the image input, where σ is the standard deviation. Higher σ values introduce more visible noise. DiffPure (Nie et al., 2022): DiffPure employs a diffusion model to mitigate perturbations. It first injects adaptive Gaussian noise into the image and then reconstructs a clean version by solving a reverse stochastic differential equation (Song et al., 2021) using Guided Diffusion (Dhariwal and Nichol, 2021). Adversarial training (Goodfellow et al., 2015): To enhance robustness against perturbations, we fine-tune the target MLLM using adversarial training. Assuming the malicious actor detects and collects such perturbed inputs, we use 100 image-question pairs, splitting them evenly into training and testing sets. Following LLaVA-1.5 (Liu et al., 2024a), we fine-tune both the vision-language projector and LLM using LoRA (Hu et al., 2022), keeping LLaVA-1.5’s training settings.
Figure 5: Impact of the temperature of the target MLLM on ImageProtector. We use general probing questions as shadow questions with LLaVA-1.5 on VQAv2.
Refusal Rate
E
0.95
The Use of Large Language Models
During manuscript preparation, we used a large language model only in a limited, editorial capacity to refine prose like improving grammar, clarity, and readability of author-written sentences. It played no role in conceptualization, study design, data analysis, coding or software implementation, or the creation of original scientific content. All ideas, methods, and results presented are entirely the authors’ work and responsibility.
1.00 0.90 0.80 0.70 0.60 0.50 0.40 0.30
RR with exact probing questions RR with similar probing questions RR with general probing questions
1
2
3
4
5
Repeated Question Answering Rounds Figure 6: Impact of the number of repeated question answering rounds. We use three types of shadow questions with LLaVA-1.5 on VQAv2.
14
Table 8: Configurations of MLLMs. Vision-Language Projector (# Parameters)
CLIP ViT-L/14 (Radford et al., 2021) (428M) EVA-CLIP ViT-g/14 (Fang et al., 2023) (1B)
Llama-2 (Touvron et al., 2023) (7B) Llama-2 (7B)
Qwen-VL-Chat
OpenCLIP ViT-bigG (Ilharco et al., 2021) (2B)
Qwen (Bai et al., 2023a) (7B)
InstructBLIP
EVA-CLIP ViT-g/14 (1B)
Vicuna (Chiang et al., 2023) (7B)
SigLIP (Zhai et al., 2023) (400M) redesigned ViT (632M)
Phi4 Mini (Abouelenin et al., 2025) (6B, mixture of LoRAs design) Qwen 2.5 (Bai et al., 2025) (8B)
2-layer FFN (10M) 1-layer FFN (23M) 1-layer Cross-Attention (Lin et al., 2022) (76M) Q-Former (Li et al., 2023) (186M) 2-layer MLP (40M) MLP-based Merger (45M)
LLaVA-1.5 MiniGPT-4
Phi-4-multimodal-instruct
Refusal Rate
Qwen2.5-VL-7B-Instruct
1.00 0.95 0.90 0.85 0.80 0.75 0.004 0.005 0.006 0.007 0.008
Step Size
1.00 0.95 0.90 0.85 0.80 0.75 0.002 0.003 0.004 0.005 0.006
Refusal Rate
LLM (# Parameters)
Refusal Rate
Vision Encoder (# Parameters)
MLLM
Step Size
(a) Exact probing questions
1.00 0.95 0.90 0.85 0.80 0.75 0.002 0.003 0.004 0.005 0.006
Step Size
(b) Similar probing questions
(c) General probing questions
750
1000
1250
Maximum Number of Iterations
1.00 0.95 0.90 0.85 0.80 0.75
(a) Exact probing questions
Refusal Rate
1.00 0.95 0.90 0.85 0.80 0.75 500
Refusal Rate
Refusal Rate
Figure 7: Impact of step size on ImageProtector. We evaluate three types of shadow questions with LLaVA-1.5 on VQAv2.
500
1000
1500
2000
Maximum Number of Iterations
(b) Similar probing questions
1.00 0.95 0.90 0.85 0.80 0.75
500
1000
1500
2000
Maximum Number of Iterations
(c) General probing questions
8/255 12/255 16/255 (a)
1.00 0.95 0.90 0.85 0.80 0.75
Refusal Rate
1.00 0.95 0.90 0.85 0.80 0.75 4/255
Refusal Rate
Refusal Rate
Figure 8: Impact of the maximum number of iterations on ImageProtector. We use three types of shadow questions with LLaVA-1.5 on VQAv2.
1
3
5
7
Mini-batch Size of Shadow Questions (b)
1.00 0.95 0.90 0.85 0.80 0.75
20
30
40
50
Size of Shadow Questions (c)
Figure 9: Impact of (a) ℓ∞ -norm perturbation constraint ϵ, (b) mini-batch size of shadow questions, (c) the size of shadow questions on ImageProtector. We use general probing questions as shadow questions with LLaVA-1.5 on VQAv2.
15
5
10
of Gaussian Noise
Accuracy of MLLM RR with exact probing questions
Accuracy
×10 3
RR with similar probing questions RR with general probing questions
1.00 0.90 0.80 0.70 0.60 0.50 0
1.00 0.80 0.60 0.40 0.20 2 0.00
Refusal Rate
1.00 0.80 0.60 0.40 0.20 20 0.00
Refusal Rate
Accuracy
1.00 0.90 0.80 0.70 0.60 0.50 0 1
1
Timestep in DiffPure
Accuracy of MLLM RR with exact probing questions
(a)
RR with similar probing questions RR with general probing questions
(b)
1
2
3
4
Adversrial Training Epochs
(a) Exact probing questions
1
2
3
4
1.00 0.90 0.80 Accuracy of MLLM Refusal Rate
0.70 0
1
2
3
4
1.00 0.80 0.60 0.40 0.20 5 0.00
Adversrial Training Epochs
Adversrial Training Epochs
(b) Similar probing questions
(c) General probing questions
Refusal Rate
Accuracy of MLLM Refusal Rate
1.00 0.80 0.60 0.40 0.20 5 0.00
Accuracy
1.00 0.90 0.80 0.70 0.60 0.50 0
Refusal Rate
Accuracy of MLLM Refusal Rate
1.00 0.80 0.60 0.40 0.20 5 0.00
Accuracy
1.00 0.90 0.80 0.70 0.60 0.50 0
Refusal Rate
Accuracy
Figure 10: Accuracy and refusal rates (RR) of ImageProtector with (a) adding Gaussian noise and (b) using DiffPure. We use three types of shadow questions with LLaVA-1.5 on VQAv2.
Figure 11: Accuracy of the target MLLM and refusal rates of ImageProtector when using adversarial training with different training epochs. We use three types of shadow questions with LLaVA-1.5 on VQAv2.
Prompt to generate similar probing questions Please rephrase the following question into 10 new questions: [Example_Question]
Figure 12: Prompt to generate similar probing questions where [Example_Question] denotes an example question.
Prompt to generate general probing questions Imagine a user of a multimodal large language model service. This user will upload an image and ask questions about this image. Please generate [Q] questions that this user might ask about this image.
Figure 13: Prompt to generate general probing questions where [Q] denotes the number of shadow questions to generate.
Prompt for refusal judge LLM We consider an MLLM’s answer as a refusal or denial answer if it contains words like ‘sorry’, ‘I cannot help’, or ‘unfortunately’. Based on this definition, is the following an MLLM refusal or denial answer? Please respond only with ‘yes’ or ‘no’: “[MLLM_Response]"
Figure 14: Prompt to a refusal judge LLM. [MLLM_Response] represents the response from an MLLM.
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Example prompt: Imagine a user of a multimodal large language model service. This user will upload an image and ask questions about this image. Please generate 10 questions that this user might ask about this image. Example response from GPT-4: “What objects are present in this image?", “What is the shape of the object in this image?", “What is the color of the objects in this image?", “What is the orientation of the object in this image?", “What is happening in this image?", “What is the size of the object in this image?", “Where is the object in this image?", “How many objects are in this image?", “What is the text in this image?", “Why does this situation happen?".
Figure 15: Example prompt to generate general probing questions and example response from GPT-4.
10 refusal responses collected using GPT-4: I’m sorry, I can’t assist with that request. Sorry, I can’t assist with that request. I’m sorry, I cannot assist with this request. Sorry, I can’t assist with this request. I’m sorry, but I can’t assist with that request. I’m sorry, but I cannot assist with that request. Sorry, I cannot assist with that request. I’m sorry, but I cannot assist with that request. Unfortunately, I can’t assist with that request. Sorry, I can’t provide that information.
Figure 16: The set of refusal responses.
Example prompt: Please paraphrase below question into 10 new questions: “What are pedestrians asked not to do on the white sign?" Example response from GPT-3.5: “On the white sign, what are pedestrians being told not to do?", “What is prohibited for pedestrians on the white sign?", “What is the request made of pedestrians on the white sign?", “What action are pedestrians being warned not to take on the white sign?", “On the white sign, what are pedestrians being instructed to avoid?", “According to the white sign, what behavior should pedestrians refrain from?", “What are pedestrians being advised against on the white sign?", “On the white sign, what action is being discouraged for pedestrians?", “What is the white sign instructing pedestrians not to do?", “What is the restriction for pedestrians on the white sign?".
Figure 17: Example prompt to generate similar probing questions and example response from GPT-3.5.
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