mlsanityData to 14 Oct 2026

Prompt injections as a tool for preserving identity in GAI image descriptions

arXiv 2510.16128 ↗First posted 17 Oct 2025cs.CR cs.CYcited by 0

On the map: LLM safety and security → Prompt injection and agent security

Abstract (arXiv)

Generative AI risks such as bias and lack of representation impact people who do not interact directly with GAI systems, but whose content does: indirect users. Several approaches to mitigating harms to indirect users have been described, but most require top down or external intervention. An emerging strategy, prompt injections, provides an empowering alternative: indirect users can mitigate harm against them, from within their own content. Our approach proposes prompt injections not as a malicious attack vector, but as a tool for content/image owner resistance. In this poster, we demonstrate one case study of prompt injections for empowering an indirect user, by retaining an image owner's gender and disabled identity when an image is described by GAI.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Prompt injection and agent security, one of 12 directions in LLM safety and security. Builds on 10 of its 36 references that are in the map. First posted on arXiv 17 Oct 2025.

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10 of 36 references.

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  1. 2024InjecAgent: Benchmarking Indirect Prompt Injections in Tool-Integrated Large Language Model Agents5 Mar

Other references · 9

  1. 2023Glaze: Protecting Artists from Style Mimicry by Text-to-Image Modelscited by 205
  2. 2023Anti-DreamBooth: Protecting users from personalized text-to-image synthesiscited by 120
  3. 2024A New Era in LLM Security: Exploring Security Concerns in Real-World LLM-based Systemscited by 77
  4. 2023Nightshade: Prompt-Specific Poisoning Attacks on Text-to-Image Generative Modelscited by 75
  5. 2024Identifying and Improving Disability Bias in GPT-Based Resume Screeningcited by 28
  6. 2021Black Feminist Musings on Algorithmic Oppressioncited by 12
  7. 2022Tackling Algorithmic Disability Discrimination in the Hiring Process: An Ethical, Legal and Technical Analysiscited by 10
  8. 2023An Autoethnographic Case Study of Generative Artificial Intelligence's Utility for Accessibilitycited by 7
  9. 2024Laissez-Faire Harms: Algorithmic Biases in Generative Language Modelscited by 5

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  1. 2024Harm Amplification in Text-to-Image Models
  2. 2024Can Prompt Modifiers Control Bias? A Comparative Analysis of Text-to-Image Generative Models
  3. 2025Safer Prompts: Reducing Risks from Memorization in Visual Generative AI
  4. 2026Leave My Images Alone: Preventing Multi-Modal Large Language Models from Analyzing Images via Visual Prompt Injection
  5. 2024Self-interpreting Adversarial Images
  6. 2025Semantic Mismatch and Perceptual Degradation: A New Perspective on Image Editing Immunity
  7. 2023Raising the Cost of Malicious AI-Powered Image Editing
  8. 2025PromptFlare: Prompt-Generalized Defense via Cross-Attention Decoy in Diffusion-Based Inpainting