Wan 3.0 "Inappropriate Content" Error: Causes, Fixes & What to Do Next

TL;DR: The message is DataInspectionFailed, and it comes from Green Net — Alibaba's content-compliance layer — not from Wan 3.0 itself. It screens both your input and the finished video, which is why a prompt can pass and the generation still fail at the end. It is known to fire on material that looks entirely harmless, and the error never tells you which part tripped it. Your realistic options are to rewrite, retry, or generate on a stack that doesn't sit behind Green Net.

If you run Wan 3.0 through Alibaba Cloud Model Studio, sooner or later a job comes back FAILED with a message about inappropriate content. The wording is vague by design, it doesn't say whether your prompt or the output caused it, and it often lands on requests with nothing objectionable in them.

This post is a mechanical walkthrough: the exact strings you'll see, which layer produces them, why false positives are structural rather than accidental, and what you can actually do about it.

The exact messages

These are the strings Alibaba's Model Studio documentation defines for content-safety failures. The error code is DataInspectionFailed (also written data_inspection_failed, and in some SDK traces InternalError.Algo.DataInspectionFailed):

Both sides flagged: Input or output data may contain inappropriate content.

Input flagged: Input data may contain inappropriate content.

Output flagged: Output data may contain inappropriate content.

Specific input flagged: Input xxx data may contain inappropriate content.

The task itself fails rather than returning a video — task_status becomes FAILED, with the code and message attached.

Three related codes come from the same subsystem and are worth recognising, because they mean different things:

If you're getting IPInfringementSuspect rather than DataInspectionFailed, the system isn't objecting to the subject matter — it thinks you're reproducing someone's intellectual property. That's a different fix, and we come back to it below.

Where the block actually comes from

The important thing to understand is that this is not the model refusing. Wan 3.0 is a video generation model; it has no opinion about your prompt. The refusal comes from a separate compliance layer that Alibaba calls Green Net (绿网), which sits in front of and behind the model in Model Studio's serving stack.

Green Net runs in two places:

Before generation. Your prompt text and any reference images are screened. If something trips the classifier, the job fails immediately and nothing is generated. These failures come back fast.

After generation. The finished video is screened as well. This is the part that surprises people: the job can accept your prompt, spend real time rendering, and then discard the result. You're told the output "may contain inappropriate content" about a video you never get to see.

That two-sided design is why the message reads "Input or output" — the generic string is used when the response doesn't narrow it down for you.

Why it fires on prompts that look fine

Three structural reasons, none of which are bugs you can appeal:

1. It's a classifier, not a rulebook. Green Net makes a probabilistic judgement. Anything probabilistic has a false-positive rate, and a compliance filter is deliberately tuned to over-block rather than under-block — the cost of wrongly refusing you is much lower, for Alibaba, than the cost of wrongly allowing something.

2. The output check has no idea what you asked for. The post-generation pass looks at pixels. A perfectly innocent prompt can produce a frame that reads, to a classifier, as something else — skin tones in a swimming scene, stage blood in a fight scene, a face that resembles a public figure by coincidence. Your intent isn't part of the evaluation.

3. Ambiguity is resolved against you. With no explanation of which term or which frame caused the block, you can't correct the specific thing. You can only guess and resubmit — and this is exactly the pattern developers report, where the same request sometimes succeeds on a retry.

Developers hitting this on Alibaba's own tooling have filed issues describing precisely that: content that contains nothing unsafe, refused anyway (qwen-code #3119, #3145).

What to do about it

Retry once, but only once. Because the output check is probabilistic, an identical resubmission genuinely does sometimes pass. If two attempts fail, the third almost certainly will too — the trigger is in your input, and retrying stops being worth the time.

Rewrite the prompt around the concept, not the word. Filters match on surface features. Describing the same scene in different terms often clears it: replace loaded nouns with descriptions of what's visually happening, drop intensity adverbs, and remove anything naming a real person, a brand, or a franchise.

For IPInfringementSuspect, remove the reference, not the adjective. This code fires on named properties and on reference images carrying recognisable IP. Rewording won't help if you've uploaded a frame from a film. Describe the aesthetic instead of naming the source.

Check whether it's really moderation. Two other failures get mistaken for content blocks: an unreachable media URL, and upstream capacity problems. Both surface as a failed task with a different code, so read the code rather than assuming.

Or use a stack that isn't behind Green Net. Green Net is specific to Alibaba's own serving infrastructure. Wan 3.0 is available through other providers, and those don't inherit Alibaba's compliance layer — the model is the same, the filter in front of it isn't.

What that looks like on VidCella

We serve Wan 3.0 through a different upstream to Alibaba Model Studio, so DataInspectionFailed isn't part of the path — it's an error from a stack we don't route through.

We do run safeguards of our own — any serious platform does. The difference is precision. Ours are built to catch genuinely prohibited material rather than to sweep broad categories, so ordinary creative work isn't what they're looking for. You shouldn't find yourself rewording a harmless prompt three times to get a scene past a classifier.

There's also no second pass that renders a video and then throws it away. Nothing gets discarded after you've already waited for it.

What you get on Wan 3.0 here:

• Up to 30 seconds in a single generation
• 480p, 720p or 1080p, five aspect ratios
• Native audio generated with the video
• First-and-last-frame control, and multimodal reference input
• From 15 credits per second, pay-as-you-go, no subscription

Handle real-person likeness, trademarks and third-party IP responsibly — that responsibility sits with you wherever you generate.

FAQs

What does the Wan 3.0 "inappropriate content" error mean? It's Alibaba's DataInspectionFailed code, produced by their Green Net compliance layer. It means either your input or the generated output was flagged by a content classifier. The message doesn't tell you which, or why.

Is it Wan 3.0 that's refusing my prompt? No. The model doesn't evaluate your request. Green Net is a separate screening layer in Alibaba Model Studio's serving stack, running before and after generation.

Why did it fail after generating the whole video? Because the output is screened separately from the input. A prompt that passes the pre-check can still produce frames that the post-generation classifier rejects, and the video is discarded before you receive it.

Will retrying the same prompt work? Sometimes. The output-side check is probabilistic, so an identical request can pass on a second attempt. If it fails twice, the cause is almost certainly in your input and further retries won't help.

What's the difference between DataInspectionFailed and IPInfringementSuspect? DataInspectionFailed is a general content-safety block. IPInfringementSuspect specifically means your input looks like copyrighted or trademarked material. The second one is usually fixed by removing a reference image or a named property, not by rewording.

Does this error exist outside Alibaba Cloud? No. Green Net is Alibaba's own compliance system, so the error belongs to their serving stack. The same Wan 3.0 model served through a different provider doesn't return it.

Related reading

• Why GPT Image 2 blocks your prompts — the same two-stage filter pattern, on OpenAI's stack
• Is MiniMax H3 uncensored? We tested it — why the same model behaves differently on different channels
• Open weights doesn't mean uncensored — where content filtering actually lives