Alibaba Cloud Model Studio: DataInspectionFailed 400 blocked by content safety reviewu0026type=skill

Not specified by the original author.

TL;DR

For DataInspectionFailed, adjust the input or output prompts (or change the input image) to pass moderation; do not try to work around the content policy. Other common codes: InvalidApiKey 401 means the key is missing or invalid (check the DASHSCOPEAPIKEY env var), Model.NotExist 400 means a wrong model name or no access, Throttling/Throttling.RateQuota 429 means lower concurrency and retry with exponential backoff.

Fix

1. For DataInspectionFailed, adjust the input or output prompts (or change the input image) to pass moderation; do not try to work around the content policy. Expected: You get the expected result; the problem is gone.

When to use

• You are setting up or using this Alibaba Cloud Model Studio feature.
• The symptom matches: DataInspectionFailed 400 blocked by content safety review.

When NOT to use

• Unrelated Alibaba Cloud Model Studio issues (different feature, different failure).
• You need general documentation for the tool; check the official docs instead.

Compatibility

Reported against Alibaba Cloud Model Studio. Source: https://github.com/dashscope/dashscope-sdk-python/blob/HEAD/dashscope/acli/examples/dashscope-sdk-expert/.acli/skills/te.

Variant phrasings

DataInspectionFailed 400 blocked by content safety review

Why it happens

A DashScope (Model Studio) call returns HTTP 400 with code DataInspectionFailed instead of a model response. This means the prompt or image was blocked by the content safety review, not a parameter mistake. A related gotcha: Generation.call does not raise on HTTP failure, it returns a response with status_code != 200 and the error in code/message, so check the response code explicitly.

Edge cases

• If your error message differs even slightly, this is probably a different issue; search the exact text.
• If the fix does not help, capture the full error output and check the source link for updates.

Source

https://github.com/dashscope/dashscope-sdk-python/blob/HEAD/dashscope/acli/examples/dashscope-sdk-expert/.acli/skills/text-generation.md

Source: A DashScope (Model Studio) call returns HTTP 400 with code DataInspectionFailed instead of a model response. This means the prompt or image was blocked by the content safety review, not a parameter mistake. A related gotcha: Generation.call does not raise on HTTP failure, it returns a response with status_code != 200 and the error in code/message, so check the response code explicitly.

Verified resolution: [DashScope SDK skill notes]: For DataInspectionFailed, adjust the input or output prompts (or change the input image) to pass moderation; do not try to work around the content policy. Other common codes: InvalidApiKey 401 means the key is missing or invalid (check the DASHSCOPEAPIKEY env var), Model.NotExist 400 means a wrong model name or no access, Throttling/Throttling.RateQuota 429 means lower concurrency and retry with exponential backoff.

Source URL: https://github.com/dashscope/dashscope-sdk-python/blob/HEAD/dashscope/acli/examples/dashscope-sdk-expert/.acli/skills/text-generation.md

Not specified by the original author.

TL;DR

Fix this in the answer library by acting on the exact message first: add an approved AI governance answer set covering model inventory, whether customer content trains models, the opt-out control and human review, sourced from the current AI policy, and tag it for the AI question family. Do not rerun the whole job unchanged, because the same inputs produce the same failure. Prove the fix on one item, then rerun the full scope and reconcile the counts.

knowledge base search error security answers missing for AI governance questions

Use this when

• The operation in the answer library fails with this exact message: knowledge base search error security answers missing for AI governance questions.
• The same failure repeats on retry with unchanged inputs.
• A review, export or check is blocked until this item completes.

Not for this skill when

• The message is a permission or outage notice from the platform. Resolve that first, then return here if this error remains.
• The operation completes and you only disagree with its output. That is a quality review, not this failure.

Steps

1. Reproduce the failure with the smallest scope that still shows it in the answer library, and copy the full message plus the questionnaire, answer or evidence item involved into the run notes. Verify: the error is reproduced on demand with a named item, not only seen in a log.
2. Apply the specific fix for this error: add an approved AI governance answer set covering model inventory, whether customer content trains models, the opt-out control and human review, sourced from the current AI policy, and tag it for the AI question family. Verify: searching the library for the failing AI governance questions returns approved answers for each one.
3. Check the neighbouring items that share the same config, template, feed or credential, because this class of failure rarely affects only one item. Verify: the neighbouring items are listed and each is marked affected or clear.
4. Compare the result counts, decisions or alerts against the source in the answer library so a silent partial success is caught before sign off. Verify: counts match the source or every difference is named and explained.
5. Rerun the original operation in the answer library on the previously failing scope, watching for the same message rather than assuming success from a quiet log. Verify: the operation completes and the original message does not appear.
6. Record the fix, the triggering input and the verification result on the item so the next occurrence is recognised immediately. Verify: the record names the error, the fix applied and the evidence of success.

Why it happens

AI governance questions are a new family that arrived after the library was built, so no approved answers existed for the search to find.

Variant phrasings

knowledge base search error security answers missing for AI governance questions

The verbatim form searchers paste from the terminal or console. Treat it identically: same fix, same single item proof, then the full rerun.

How to fix: knowledge base search error security answers missing for AI governance questions failure

The how-to phrasing for the same failure in the answer library. The steps above apply unchanged; start at the reproduction step rather than skipping to the rerun.

the answer library troubleshooting for this failure

The symptom-first phrasing used when the console hides the full message. Surface the full message first, confirm it matches the code block above, then follow the steps.

Compatibility: the answer library at current versions, plus the platform API or console the query names; no extra tooling beyond a text editor and the source system access needed to verify counts.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_721ZNVR6rzAt0W7JjDRdnw

Resolved from the public thread: https://vectle.com/posts/pst_721ZNVR6rzAt0W7JjDRdnw

Thread query: knowledge base search error security answers missing for AI governance questions failure

TL;DR: Fix this in the answer library by acting on the exact message first: add an approved AI governance answer set covering model inventory, whether customer content trains models, the opt-out control and human review, sourced from the current AI policy, and tag it for the AI question family. Do not rerun the whole job unchanged, because the same inputs produce the same failure. Prove the fix on one item, then rerun the full scope and reconcile the counts.

Not specified by the original author.

TL;DR

Reword or narrow the request to avoid the policy trigger. For user-supplied inputs, run them through the moderation endpoint first so your app fails gracefully instead of mid-flow.

Steps

1. The error: HTTP 400 with a safety-system message (for example image or chat inputs rejected as a result of the safety system). Meaning: the content triggered policy filters.
2. What the agent should do:
3. Classify as terminal-for-this-content. Retrying the identical prompt cannot succeed; backoff is pointless.
4. Reword or narrow the request to avoid the policy trigger. For user-supplied inputs, run them through the moderation endpoint first so your app fails gracefully instead of mid-flow.
5. If you believe it is a false positive, use the support or forum route, but omit any sensitive information and never paste the user's content into a public post.
6. Log the refusal category for your own tuning, not the raw user content.
7. The trap: treating a safety 400 like a malformed-request 400 and "fixing" JSON shape or parameters. The request is well-formed; the content is the problem. A second trap is debugging it by posting the offending prompt publicly.
8. Evidence: https://platform.openai.com/docs/guides/error-codes

When to use

You are seeing this: The error: HTTP 400 with a safety-system message (for example image or chat inputs rejected as a result of the safety system). Use this skill when you run into "OpenAI 400 safety rejection: not retryable, fix the content never the request".

When not to use

If your error message or symptom does not match what is described above, this is probably not your fix. Search for your exact error text instead of forcing this one to fit.

Versions

No specific versions are mentioned in the source material, so treat the fix as generally applicable and check the examples against whatever you have installed.

Why this happens

The original report does not dig into a root cause. It documents the symptom and the fix that resolved it.

Sources

• https://platform.openai.com/docs/guides/error-codes

Verified against official OpenAI docs 2026-09-26.