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1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minuteAdversarial attacks can get tested AI image generators to create not-safe-for-work (NSFW) images despite safety measures. They do this by targeting different parts of the generation process: altering prompts, combining text and images, changing an image supplied to an image-to-image model, or exploiting interactions between several safeguards. Published results apply to the particular models and test setups studied—not necessarily to the versions of commercial services available today.
How AI image-generator safeguards can be bypassed
Image generators may use more than one safety check. A prompt filter can block or alter text before generation; a concept-erasure component can suppress certain content in the model; and an image checker can inspect the result afterward. These measures address different points in the pipeline, so testing a prompt filter alone does not show how the full system will behave.
Researchers have studied attacks against each of these points. Some attempt to make a harmful request pass as acceptable text. Others supply visual input, or probe how defenses work together. The underlying issue is that a filter or checker must interpret inputs and outputs that can be indirect or unexpected; a safety system can miss cases even when its intended rules are clear.
What kinds of attacks have researchers tested?
Iteratively changing text prompts
SneakyPrompt automates the search for prompt wording that gets past a filter. As described by IEEE Spectrum, the method repeatedly replaces filtered words with alternatives, queries the generator, and adjusts the prompt based on the output. In the study’s reported setup, it achieved an average bypass rate of about 96% on Stable Diffusion and roughly 57% on DALL·E 2. The researchers estimated that prior manual attempts against Stable Diffusion succeeded about 33% of the time. These figures are experimental results, not current success rates for those services.
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Prompt-learning attacks take a related but distinct approach. The authors of PLA: Prompt Learning Attack against Text-to-Image Generative Models, published in the ICCV 2025 proceedings, study black-box attacks intended to bypass text-to-image safety mechanisms. Black-box means the attack is designed around querying a system rather than assuming access to its internal model. IEEE Spectrum also reported on a later automated method called Jailbreaking Prompt Attack (JPA), which researchers said worked on the open and closed models they tested, including Stable Diffusion, DALL·E, and Midjourney. That report does not establish how current versions behave.
Combining text and visual input
MMA-Diffusion: MultiModal Attack on Diffusion Models, presented at CVPR 2024, studies attacks that use both text and image input to get around prompt filters and post-generation checkers. This matters because a system that scrutinizes text may still have a separate path for visual information. A defense that works on one input type is not, by itself, evidence that the combined system is robust.
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Altering an image given to an image-to-image model
Not every image-generation request begins with text alone. Image-to-image systems use an existing picture as input, often alongside a prompt. In AdvI2I: Adversarial Image Attack on Image-to-Image Diffusion Models, published in ICML 2025, researchers describe optimizing the input image to induce NSFW output without changing the text prompt. The paper reports attacks against defenses including Safe Latent Diffusion. The attack’s target is therefore a different part of the workflow from methods that search for alternative prompt words.
Targeting multiple defense layers
Some attacks focus on how safeguards interact, rather than trying to defeat only one. The NeurIPS 2025 paper Transstratal Adversarial Attack: Compromising Multi-Layered Defenses in Text-to-Image Models describes prompt filters, concept erasers, and image filters as sequential layers. Across 14 text-to-image models and 17 safety modules, its authors report an 85.6% average attack success rate in their evaluation, exceeding the compared state-of-the-art methods by 73.5%. These are benchmark-specific results; they should not be read as a forecast of how often a current consumer product can be bypassed.
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Why the reported percentages cannot be compared directly
A percentage is meaningful only alongside the test conditions that produced it. The studies vary in what the attack can submit, which defenses are present, and which models are evaluated. They may also define a successful attack differently. For example, a prompt-only experiment is not directly comparable to one that can alter an input image or target several defense layers.
| Study | Attack input or focus | What the reported evidence establishes |
|---|---|---|
| SneakyPrompt, reported by IEEE Spectrum | Automated, iterative text-prompt changes | About 96% average bypass on Stable Diffusion and roughly 57% on DALL·E 2 in the study’s tested setup; the report also estimates about 33% for earlier manual attempts on Stable Diffusion. The figures are not a current product scorecard. |
| PLA, ICCV 2025 | Prompt-learning attack designed for black-box access | Studies prompt-based bypasses of text-to-image safety mechanisms; the cited source does not establish a directly comparable rate for current commercial versions. |
| MMA-Diffusion, CVPR 2024 | Text and image inputs together | Studies multimodal attacks against prompt filters and post-generation checkers. |
| AdvI2I, ICML 2025 | Adversarially altered image input | Reports attacks on image-to-image diffusion models, including against Safe Latent Diffusion; the cited source does not establish a directly comparable rate for current commercial versions. |
| Transstratal, NeurIPS 2025 | Attacks across sequential safety layers | Reports an 85.6% average attack success rate across the study’s evaluation of 14 models and 17 safety modules. |
The figures in this table come from different papers and experiments, not a shared benchmark. They cannot be used to rank the approaches or infer the odds of bypassing a particular service. Model versions, access assumptions, defense configurations, and success criteria all matter.
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How red-teaming helps identify overlooked cases
Some harmful outputs arise from prompts that are adversarial in effect but not obviously suspicious. Google Research’s 2024 Adversarial Nibbler work focuses on these “implicitly adversarial prompts.” It reports a collection of more than 10,000 prompt-image pairs with machine safety annotations, including a 1,500-sample subset with richer human annotations of harm types and attack styles.
Red-teaming is useful because it can reveal failure cases that straightforward, known-bad prompts may not expose. The Nibbler authors emphasize continual auditing and adaptation as new vulnerabilities emerge. The Transstratal findings add a related lesson: safeguards that appear effective when evaluated separately may still have weaknesses when combined.
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What these findings mean for users and developers
For people using image generators
Safety controls reduce risk, but the cited studies show they are not guarantees. They also do not provide a reliable way to predict the behavior of a particular service today. Products can change their models, policies, and safeguards, and the reviewed sources do not provide a comprehensive, independently verified test of current commercial versions as of October 5, 2026.
For teams building or evaluating generators
- Evaluate the full pipeline, including input filters, model-level safeguards, and output checks.
- Test text, image, and combined-input paths when a product supports them.
- Use red-team prompts that include indirect or unexpected cases, not only obvious examples.
- Report model versions, defense configurations, access assumptions, and success definitions so results can be interpreted accurately.
- Repeat evaluations as models and safeguards change; passing an earlier test does not establish present-day robustness.
What remains uncertain
The studies establish that tested systems and defenses can be bypassed under their experimental conditions. They do not show that every generator is vulnerable in the same way, or how frequently current commercial systems would fail. Answering that requires fresh, version-specific testing with clearly stated models, safeguards, and success criteria.
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