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Nightshade is a University of Chicago SAND Lab tool that alters an image so it can act as a “poison” sample if an AI developer includes it in training. The intended result is that a text-to-image model learns a wrong association for a targeted concept and may produce distorted results for related prompts. It is not a takedown notice, a watermark, or a guarantee that a scraping service will be affected.
Nightshade is often mentioned alongside Glaze, but they address different problems. Nightshade targets training-data poisoning; Glaze targets an AI model’s attempt to imitate an individual artist’s style.
What is Nightshade?
Nightshade makes a specially modified image that is intended to influence a model during training. The artist selects a key concept or object—such as a dog, landscape, or particular item—and the software generates a poison tag and image treatment for that concept.
The Tool Desk
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What is the difference between Nightshade and Glaze?
| Tool | Primary target | When it acts | Practical limitation |
|---|---|---|---|
| Nightshade | A learned concept in a text-to-image model | When treated images are included in model training | It cannot ensure collection or training, and results depend on the model and dataset |
| Glaze | Mimicry of an individual artist’s visual style | When a model learns style features, particularly through fine-tuning | Protection against image-to-image editing, style transfer, and inpainting is inconsistent |
Glaze changes image pixels in an image-specific way intended to look nearly unchanged to people while presenting a different style signal to an AI model. The project says these changes are not a hidden message or watermark. Its strongest stated use is disrupting individualized style imitation, not stopping every form of image manipulation.
The developers describe using both tools as a possible defense in a broader workflow. That is project guidance, not proof of comprehensive protection against every current or future model.
How does Nightshade work?
1. It creates a targeted poison sample
Nightshade optimizes small image changes around a selected concept. The goal is not merely to obscure the image from a viewer; it is to make the training signal associated with the concept differ from the artist’s intended subject.
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The effect is conditional. A model trainer has to obtain the image, keep the relevant pixels, associate it with a useful text label, and include it in training. A copied thumbnail, transformed image, filtered dataset, or model that excludes the file may reduce or eliminate the intended effect.
3. Related prompts may be affected
Once a model has learned poisoned examples, prompts for the targeted concept or nearby concepts may produce unintended associations. The precise outcome depends on the model architecture, training procedure, captions, dataset composition, and the number and distribution of poison samples.
What do the published experiments show?
The Nightshade research paper, published in 2023, reports controlled experiments rather than a universal performance guarantee. In tested settings, the authors report that fewer than 100 poison samples could affect an SDXL prompt. Elsewhere in the paper, experiments using 200 samples report high attack success.
Those figures belong to the named study conditions: specific models, prompts, datasets, and attack settings. They should not be read as a threshold for every commercial image generator, current model, or future filtering pipeline. No cited, current independent benchmark establishes a single ranking of protection across all major services.
What should I check before I run Nightshade on my image?
- Choose one dominant concept. Follow the guide’s instruction to make sure the poison tag identifies the single key object or concept in the image. A crowded composition with several equally important subjects can make the intended target ambiguous.
- Review the intensity setting. Greater intensity is generally associated with a stronger intended poison effect, but it can also make changes more visible. The trade-off is not a promise of a particular attack result.
- Inspect the exported image at full size. Check edges, flat areas, gradients, skin, text, and other details that viewers may notice. Keep an untreated original so you can compare versions and publish the one that meets your quality standard.
- Confirm your own workflow. Make sure the file you upload or publish is the treated export, not a cached original, a screenshot, or an automatically recompressed copy that changes the pixels.
- Set realistic expectations. Nightshade does not prevent scraping, identify who used an image, or provide legal control over a dataset. It is a conditional training-time intervention.
What should artists know before using Glaze?
Glaze can be useful when the concern is a model learning to reproduce a recognizable personal style. Its documentation warns that effects can be more visible on flat-color artwork and smooth backgrounds. It also cautions that future methods may overcome current defenses and that Glaze is less effective against styles already well represented in a model’s base training data.
The Glaze FAQ, last updated August 25, 2026, states: “At this time, we do not believe Glaze provides consistent protection against img2img attacks, including style transfer and inpainting.” In other words, an image may be protected against one form of style-learning attempt while remaining usable as input to a stronger image-to-image workflow.
Local Glaze and WebGlaze
The project describes Glaze as running locally. WebGlaze is an alternative for artists without suitable local hardware; it is free, invite-only, and subject to daily and weekly quotas. Its documentation says submitted and processed images are deleted after processing. Invitations, quotas, supported systems, and software behavior can change, so check the project’s current documentation before relying on them.
How visible are the changes?
Both tools are designed around a tension between model-facing effect and human-facing image quality. Nightshade’s guide links higher intensity with generally stronger intended poisoning but potentially more visible alterations. Glaze’s documentation specifically notes visibility risks in flat-color art and smooth backgrounds.
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What these tools cannot do
- They cannot force an AI company to collect or train on an image.
- They cannot guarantee a result against a particular commercial model.
- They cannot stop every kind of scraping, screenshotting, reposting, or manual copying.
- Glaze does not reliably defend against image-to-image editing, style transfer, or inpainting.
- Current defenses may be weakened by future model-training, preprocessing, or image-editing methods.
Why artists are using them
The tools respond to a practical concern: public artwork can be collected and used in systems that imitate artists or learn associations without the artist’s consent. During Glaze development, a University of Chicago News report said more than 1,000 professional and part-time artists were surveyed; it also described tests involving four working artists and 195 historical artists. The report said more than 90 percent of surveyed artists were willing to use the software after seeing Glaze results. Those figures describe that survey and launch coverage, not all artists.
Ben Zhao, a Neubauer Professor of Computer Science at the University of Chicago, said, “Artists really need this tool; the emotional impact and financial impact of this technology on them is really quite real.” Glaze co-author Shawn Shan explained the design goal this way: “We don’t need to change all the information in the picture to protect artists, we only need to change the style features.”
A practical decision guide
Use Nightshade when your concern is training-data concept poisoning
Choose Nightshade when you want a published image to carry a targeted, model-facing poison signal if it enters a text-to-image training set. Select a clear concept, inspect the intensity result, and accept that the outcome depends on later collection and training decisions.
Use Glaze when your concern is personal-style imitation
Choose Glaze when the main risk is a model fine-tuned or otherwise trained to imitate your recognizable style. Review the output carefully, especially for flat-color or smooth-background work, and do not assume it will stop image-to-image transformations.
Consider both only as layered risk reduction
The projects’ recommendation to apply both addresses two different threat paths. It should be understood as layered mitigation, not a complete technical or legal shield.
How widely are the tools being used?
The Glaze Project reports more than 8.5 million Glaze downloads since March 2023 and more than 2.5 million Nightshade downloads since January 2024. The page does not state the measurement date for those totals, so they should be treated as reported cumulative figures rather than a current user count.
The Bottom Line
Nightshade is best understood as a conditional poison sample for AI training, while Glaze is a style-mimicry defense. Neither tool guarantees protection: Nightshade must be included in training, and Glaze acknowledges inconsistent results against image-to-image methods and future workarounds. Use the correct tool for the threat, inspect every export, and treat published efficacy figures as results from bounded experiments rather than promises about every AI service.
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