Erased but Exploitable: Black-box Embedding-Aware Prompting Against Unlearned Text-to-Image Diffusion Models

• URL: http://arxiv.org/abs/2605.26332v1
• Date: Mon, 25 May 2026 21:11:59 GMT
• Title: Erased but Exploitable: Black-box Embedding-Aware Prompting Against Unlearned Text-to-Image Diffusion Models
• Abstract summary: Machine unlearning aims to remove specific concepts from pretrained text-to-image diffusion models.<n>White- and black-box attacks have been introduced to make the model generate such unlearned concepts.<n>We introduce BEAP, a black-box, embedding-aware adversarial prompting attack.
• Score: 7.779776003329964
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Machine unlearning aims to remove specific concepts from pretrained text-to-image diffusion models, yet several white- and black-box attacks have been introduced to make the model generate such unlearned concepts. These attacks, nevertheless, do not assume a realistic threat model, i.e. they either assume access to the model weights, or result in gibberish adversarial prompts that could be easily detected even through naive rule-based safeguarding. We aim to address this gap in this paper. We introduce BEAP, a black-box, embedding-aware adversarial prompting attack that leverages a large language model (LLM) to iteratively generate effective adversarial prompts and exploit such hidden vulnerabilities. BEAP performs an embedding-aware search in text space, combining multiple reward signals: unlearned concept presence, text-image alignment, and image quality, to refine generated prompts. Unlike previous attack methods, BEAP keeps its prompts undetectable to safety filters while producing high-quality images. Extensive experiments show that BEAP improves the Attack Success Rate (ASR) by more than 60% over prior methods, while requiring only an average of fifteen prompts per successful attack. Warning: This paper contains model outputs that may be offensive or upsetting in nature.

Related papers

• Localization then Neutralization: Gradient-guided Token Suppression against Visual Prompt Injection Attack [78.02110947708535]
  Adversarial images pose a severe security threat to multimodal large language models through prompt injection.<n>We show that successful adversarial attacks do not rely on the entire image uniformly but instead depend on a small subset of critical image tokens.<n>We propose Gradient Token Masking (GTM), which localizes these tokens via gradient analysis and neutralizes them through masking.
  arXiv  Detail & Related papers  (2026-05-24T17:51:34Z)
• A Generative Adversarial Approach to Adversarial Attacks Guided by Contrastive Language-Image Pre-trained Model [12.15621649989295]
  A generative adversarial attack method is proposed that uses the CLIP model to create highly effective and visually imperceptible adversarial perturbations.<n>Our approach integrates the concentrated perturbation strategy from Saliency-based Auto-Encoder with the dissimilar text embeddings similar to Generative Adversarial Multi-Object Scene Attacks (GAMA)
  arXiv  Detail & Related papers  (2025-11-03T08:02:48Z)
• TRCE: Towards Reliable Malicious Concept Erasure in Text-to-Image Diffusion Models [53.937498564603054]
  Recent advances in text-to-image diffusion models enable photorealistic image generation, but they also risk producing malicious content, such as NSFW images.<n>To mitigate risk, concept erasure methods are studied to facilitate the model to unlearn specific concepts.<n>We propose TRCE, using a two-stage concept erasure strategy to achieve an effective trade-off between reliable erasure and knowledge preservation.
  arXiv  Detail & Related papers  (2025-03-10T14:37:53Z)
• Rethinking the Vulnerability of Concept Erasure and a New Method [9.044763606650646]
  Concept erasure (defense) methods have been developed to "unlearn" specific concepts through post-hoc finetuning.<n>Recent concept restoration (attack) methods have demonstrated that these supposedly erased concepts can be recovered using adversarially crafted prompts.<n>We introduce **RECORD**, a novel coordinate-descent-based restoration algorithm that consistently outperforms existing restoration methods by up to 17.8 times.
  arXiv  Detail & Related papers  (2025-02-24T17:26:01Z)
• Mitigating Sexual Content Generation via Embedding Distortion in Text-conditioned Diffusion Models [2.2917707112773593]
  Distorting Embedding Space (DES) is a text encoder-based defense mechanism.<n>DES transforms unsafe embeddings, extracted from a text encoder using unsafe prompts, toward carefully calculated safe embedding regions.<n>DES also neutralizes the nudity'' embedding, by aligning it with neutral embedding to enhance robustness against adversarial attacks.
  arXiv  Detail & Related papers  (2025-01-31T04:14:05Z)
• HTS-Attack: Heuristic Token Search for Jailbreaking Text-to-Image Models [28.28898114141277]
  Text-to-Image(T2I) models have achieved remarkable success in image generation and editing.<n>These models still have many potential issues, particularly in generating inappropriate or Not-Safe-For-Work(NSFW) content.<n>We propose HTS-Attack, a token search attack method.
  arXiv  Detail & Related papers  (2024-08-25T17:33:40Z)
• MirrorCheck: Efficient Adversarial Defense for Vision-Language Models [55.73581212134293]
  We propose a novel, yet elegantly simple approach for detecting adversarial samples in Vision-Language Models. Our method leverages Text-to-Image (T2I) models to generate images based on captions produced by target VLMs. Empirical evaluations conducted on different datasets validate the efficacy of our approach.
  arXiv  Detail & Related papers  (2024-06-13T15:55:04Z)
• Jailbreak Vision Language Models via Bi-Modal Adversarial Prompt [60.54666043358946]
  This paper introduces the Bi-Modal Adversarial Prompt Attack (BAP), which executes jailbreaks by optimizing textual and visual prompts cohesively. In particular, we utilize a large language model to analyze jailbreak failures and employ chain-of-thought reasoning to refine textual prompts.
  arXiv  Detail & Related papers  (2024-06-06T13:00:42Z)
• White-box Multimodal Jailbreaks Against Large Vision-Language Models [61.97578116584653]
  We propose a more comprehensive strategy that jointly attacks both text and image modalities to exploit a broader spectrum of vulnerability within Large Vision-Language Models. Our attack method begins by optimizing an adversarial image prefix from random noise to generate diverse harmful responses in the absence of text input. An adversarial text suffix is integrated and co-optimized with the adversarial image prefix to maximize the probability of eliciting affirmative responses to various harmful instructions.
  arXiv  Detail & Related papers  (2024-05-28T07:13:30Z)
• Revisiting the Adversarial Robustness of Vision Language Models: a Multimodal Perspective [42.04728834962863]
  Pretrained vision-language models (VLMs) like CLIP exhibit exceptional generalization across diverse downstream tasks. Recent studies reveal their vulnerability to adversarial attacks, with defenses against text-based and multimodal attacks remaining largely unexplored. This work presents the first comprehensive study on improving the adversarial robustness of VLMs against attacks targeting image, text, and multimodal inputs.
  arXiv  Detail & Related papers  (2024-04-30T06:34:21Z)
• Jailbreaking Prompt Attack: A Controllable Adversarial Attack against Diffusion Models [10.70975463369742]
  We present the Jailbreaking Prompt Attack (JPA)<n>JPA searches for the target malicious concepts in the text embedding space using a group of antonyms.<n>A prefix prompt is optimized in the discrete vocabulary space to align malicious concepts semantically in the text embedding space.
  arXiv  Detail & Related papers  (2024-04-02T09:49:35Z)
• Adversarial Prompt Tuning for Vision-Language Models [86.5543597406173]
  Adversarial Prompt Tuning (AdvPT) is a technique to enhance the adversarial robustness of image encoders in Vision-Language Models (VLMs) We demonstrate that AdvPT improves resistance against white-box and black-box adversarial attacks and exhibits a synergistic effect when combined with existing image-processing-based defense techniques.
  arXiv  Detail & Related papers  (2023-11-19T07:47:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.