White Circle's RISE got past every image model it tried
RISE timeline (White Circle and press coverage)
| When | What happened |
|---|---|
| September 28, 2026 | Paper submitted to arXiv (DALL-E 3, Nano Banana 2, GPT-Image-2) |
| After submission | GPT-Image-2.5-Flare and Nano Banana 2.1 ship; RISE is rerun on both |
| October 2026 | White Circle publishes its research pages |
| October 14 | News report published; OpenAI and Google do not respond |
| November | Planned presentation at NeurIPS 2026 |
White Circle says RISE got past the safety filters of every leading image model it tried, with DALL-E 3 the most exposed. The company tests AI systems for safety. It was founded after CEO Denis Shilov posted a widely shared universal jailbreak in October 2024. It is a public benefit corporation backed by senior people from Anthropic, DeepMind, Hugging Face and OpenAI.
The paper behind RISE went to arXiv on September 28, 2026, and White Circle put up two research pages in October. A jailbreak, in this context, is any input that gets a model to produce content its own policy forbids.
DALL-E 3 broke on 13.3% of attack prompts
On DALL-E 3, 13.3% of the prompts RISE generated led to an image that a human reviewer confirmed as explicit. The same method scored 9.5% on Google's Nano Banana 2 and 3.3% on OpenAI's GPT-Image-2. Each prompt was sent three times, and a prompt counted as a success if any one of the three images was a confirmed violation.
The "up to 13%" figure in the paper's abstract is the DALL-E 3 result, not an average across models. The first press report said the method worked about 13% of the time and was later corrected to say that was the top rate, on DALL-E 3, with lower rates elsewhere.
The rates measure a deliberate attack, not everyday use
These numbers describe what happens when someone runs RISE on purpose. They don't tell you how often a normal user would see an explicit image, and they are not a ranking of which model is safest overall.
White Circle built its main comparison around nudity because every model it tested bans it and human reviewers agree on it more readily than on other categories. It also ran RISE on hate, suicide, self-harm and graphic violence to check that the method works beyond nudity.
"We focused the main comparison on nudity. It isn't the most harmful category, but it is prohibited on every target we tested, human reviewers agree on it more readily than on most others, and successes are rare enough on hardened systems to test whether a search method can find them."／"Each prompt gets three target calls, and it counts as a success if at least one of its three images is a human-confirmed violation."／"Evolution gets a foothold, and exploitation then builds substantially on it, from 5.2% to 13.3% on DALL·E 3."／"We also ran RISE on hate, suicide, self-harm, and graphic violence to check that the method works beyond nudity."— White Circle research pageView official source →
"It also said the method bypassed safety filters about 13 percent of the time; that was the highest human-verified rate, achieved on DALL·E 3, and rates for other models were lower."— Ars Technica correction noteView official source →
Newer models broke more than twice as often
Share of RISE attack prompts that produced an explicit image (human-verified)
Both newer releases sit well above the models they replaced, and Nano Banana 2.1 tops even DALL-E 3, the weakest model in the paper.
DALL-E 3
Nano Banana 2Nano Banana 2.1GPT-Image-2GPT-Image-2.5-FlareWhen White Circle pointed the unchanged attack at the two models released after its paper, success rates more than doubled. OpenAI had released GPT-Image-2.5-Flare and Google had released Nano Banana 2.1 in the meantime, so the team reran RISE on both without modifying it.
Nano Banana 2.1 reached 20.7% and GPT-Image-2.5-Flare 7.8%
Nano Banana 2.1 produced explicit images on 20.7% of RISE prompts, 2.2 times the 9.5% seen on Nano Banana 2. GPT-Image-2.5-Flare came in at 7.8%, 2.4 times GPT-Image-2's 3.3%. White Circle says it had not expected the newer models to be easier to attack, and it told Ars that both are "significantly more breakable" than before.
Results outside nudity were mixed. Self-harm success held roughly steady on GPT-Image-2.5-Flare and rose on Nano Banana 2.1, while graphic violence went up on GPT-Image-2.5-Flare and down on Nano Banana 2.1. White Circle puts less weight on the violence numbers because its judge is least precise in that category.
Two possible reasons the newer models did worse
White Circle offers two possible reasons the newer models did worse. Either they are more capable and can generate a wider range of content, or the labs have put capability ahead of safety.
It also says the cause can't be pinned down from the outside. The drop could come from the model itself, from lighter moderation settings on a cheaper variant, or from both.
"After we submitted the paper, OpenAI released GPT-Image-2.5-Flare and Google released Nano Banana 2.1. We ran RISE against both without changing the method. On explicit nudity, its success rate more than doubled compared with the models they replaced."— White Circle research pageView official source →
"On nudity, attack success roughly doubled on both new models. It rose 2.4 times on GPT-Image-2.5-Flare and 2.2 times on Nano Banana 2.1. Results on the other categories were mixed. Self-harm stayed about the same on GPT-Image-2.5-Flare and rose on Nano Banana 2.1. Graphic violence rose on GPT-Image-2.5-Flare and fell on Nano Banana 2.1. Our judge is least precise on graphic violence, so we put less weight on those two numbers than on the others."／"We did not expect the newer models to be easier to attack."／"We can't attribute the regression from the outside: it may come from the model itself, from a lighter moderation configuration on a cheaper variant, or from both."— White Circle research pageView official source →
"Meanwhile, OpenAI and Google have released new models—ChatGPT Images 2.5 and Nano Banana 2.1—and White Circle told Ars that prompt safety testing using the same method showed “both are significantly more breakable than their previous versions.”"／"“White Circle’s working hypothesis for this is either the newer models are more sophisticated and capable of generating a wider range of content, or that safety has been deprioritized in favor of capability,” White Circle told Ars."— Ars Technica reportView official source →
RISE runs on free, off-the-shelf models
Who does what in RISE (White Circle research page)
| Role | Model or people | Job |
|---|---|---|
| Attacker | Qwen3-32B with safety training removed | Writes attack prompts from a strategy |
| Target | DALL-E 3, Nano Banana 2, GPT-Image-2 | Generates images from the prompts |
| Judge | Gemini 2.5 Flash | Decides whether an image is an explicit violation |
| Human reviewers | About 1,300 labeled images | Calibrate the judge and confirm successes |
Every part of RISE is something anyone can get hold of. The attacker is a public build of Qwen3-32B with its safety training stripped out, used as-is with no extra training, and the judge is Google's Gemini 2.5 Flash. Shilov told Ars that breaking a frontier model's defenses doesn't take a frontier model.
It evolves attack strategies instead of single prompts
RISE searches for reusable strategies, not for one winning prompt. A strategy is a plain-language recipe telling a language model how to write attack prompts, so a single good strategy can produce many attacks.
In the first of two phases, the system starts from generic human-written strategies, breeds new variants from the best performers and drops the weak ones. Once that stops producing new ideas, the top five strategies are frozen and used to generate as many attacks as possible. On DALL-E 3 that took success from 5.2% after the first phase to 13.3% after the second.
A judge calibrated on about 1,300 human-labeled images
The Gemini judge was tuned against roughly 1,300 images that people had labeled by hand. Reviewers graded each image as no or weak violation, borderline, or explicit, and only explicit ones counted.
That matters because an attacker learns to chase whatever the judge rewards. White Circle gives the example of an uncalibrated scorer drifting toward close-ups of sand dunes, which share skin tones and curves with the content it was supposed to catch.
Older methods fell to near zero under the same standard
When earlier red-teaming methods were rescored with the calibrated judge, their success mostly vanished. Methods that had reported attack success as high as about 30% dropped close to zero, and SneakyPrompt, PGJ and MACPrompt each scored 0% on DALL-E 3.
"Phase 1: evolve strategies. RISE starts with some generic strategies provided by humans, and evolves better ones."／"Phase 2: exploit the best strategies. Eventually evolution stops turning up new ideas and mostly refines what it already has. At that point RISE takes the top five strategies and freezes them; they are no longer mutated or edited."／"Importantly, none of this involves training a model at all! The whole pipeline runs on a public, off-the-shelf abliterated Qwen3-32B (an open model with its safety training stripped out), used as-is."／"To calibrate it, we collected human labels on about 1,300 generated images across targets and categories, annotated on a three-point scale: no or weak violation, borderline, or explicit. Only explicit violations count as positives."— White Circle research pageView official source →
"On DALL-E 3, Nano Banana 2 (Google) and GPT-Image-2, RISE reaches up to 13% human-verified ASR; under the same calibrated evaluation, prior methods with reported ASR as high as roughly 30% fall to near zero."— arXiv abstractView official source →
"The attacking model is a publicly available version of Qwen3-32B with its safety training stripped out, downloaded from Hugging Face, and the judge model is Gemini 2.5 Flash."／"“You don’t need to have frontier models to break defenses of a frontier model, which is very interesting,” Shilov said."— Ars Technica reportView official source →
OpenAI and Google have not responded
What White Circle is releasing and what it is holding back
| Status | Items |
|---|---|
| Public | Reproduction pipeline (github.com/whitecircle/rise), paper, technical write-up |
| Private | Attack prompts, discovered strategies, generated images |
White Circle shared its results with the affected companies before going public, but as of the October 14 report neither OpenAI nor Google had answered questions about the findings. Shilov said some frontier labs heard about the findings through mutual contacts.
Ars also sent both companies the newer-model results and got no reply. White Circle plans to present RISE at NeurIPS 2026 in November.
The pipeline is open source, the prompts are not
White Circle is keeping the attack prompts, the strategies RISE found and the generated images private. It has published the reproduction pipeline so that defenders and regulators can run the same test against their own systems. Midjourney was left out because it has no public API suited to controlled benchmarking.
Image filters aren't the only safety checks that crafted inputs get past. Researchers showed encrypted instructions slipping past Grok's input checks in a separate case (Grok prompt injection slips past filters).
Shilov wants mandatory third-party safety testing
Shilov thinks outside safety testing should be required, and White Circle is in early talks with government officials about it. His complaint is that the labs building these systems also assess their safety, publish the results and set the standards. Governments, he said, should push for independent evaluation.
White Circle's research page goes further, calling for government-required third-party monitoring of deployed models and the infrastructure around them. Labs do run their own safeguards; OpenAI, for example, screens for abuse under zero data retention, though it still keeps CSAM images (OpenAI Zero Data Retention Still Keeps CSAM Images).
"White Circle has shared its findings with some frontier AI labs through “mutual contacts,” Shilov told Ars, but neither Google nor OpenAI responded to Ars’ request to comment directly on vulnerabilities found in their models."／"Neither company responded after Ars shared these new findings."／"(Researchers noted that Midjourney wasn’t tested because “it does not provide a public API suitable for controlled benchmarking.”)"／"White Circle is also in early talks with government officials about third-party safety testing, Shilov said, which he thinks should be required."／"“You don’t need frontier models or proprietary tech to break these systems; you just need a free download and the ability to make API calls,” Shilov said. “And yet the labs building these systems are the ones assessing their own safety, publishing their own results, and setting their own standards. This is further proof, if we needed it, that governments should be pushing for independent, third-party evaluation.”"— Ars Technica reportView official source →
"We believe the labs are failing at safety and security, and that self-assessment by model labs themselves will not fix it."／"We believe that government should require independent third-party monitoring of deployed models and of the production infrastructure around them."／"The RISE reproduction pipeline is open source on GitHub. We shared our findings with the affected companies before publication. The attack prompts, the strategies RISE discovered and the images it generated stay private. We're open-sourcing the RISE pipeline so defenders and regulators can run it independently against their own deployments."／"We will also present RISE at NeurIPS 2026 — come find us there!"— White Circle research pageView official source →
A version's safety score doesn't carry over to the next
A model that holds up well against an attack today gives no guarantee about its successor. The same, untouched RISE pipeline broke both companies' newer releases more often than the versions they replaced, so filter strength has to be checked again with every release rather than inherited from the last one.
That checking doesn't have to come from the labs. Because RISE runs on freely available models with no training, any outside group can repeat it on each new version. With OpenAI and Google not commenting, those independent reruns are the main way anyone outside the companies can see whether a release got safer or weaker.
FAQ
- What is the RISE jailbreak?
- RISE is an automated red-teaming method from White Circle for text-to-image models. Rather than tweaking one prompt at a time, it has a language model evolve written strategies for producing attack prompts, then reuses the best strategies to generate many attacks.
- Which image model was easiest to break?
- Of the three models in the paper, DALL-E 3 had the highest rate at 13.3%. In a later retest, Nano Banana 2.1 reached 20.7%, up from 9.5% on Nano Banana 2.
- Can anyone download RISE?
- Only the reproduction pipeline is public, on GitHub. White Circle is keeping the attack prompts, the strategies RISE found and the generated images private, and says the pipeline is meant for defenders and regulators to test their own deployments.
- Did OpenAI or Google respond?
- No. Neither company answered questions about the weaknesses found in their models, and neither replied after the newer-model results were shared with them.
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