Solving Sora 2 API error moderation system blocked self-harm: 5 Causes and Fixes

Author's Note: A detailed analysis of the causes and solutions for the The request is blocked by our moderation system - self-harm error when calling the official Sora 2 API.

Getting a The request is blocked by our moderation system when checking inputs. Possible reasons: self-harm error while trying to generate videos with the Sora 2 API? In this post, we'll dive deep into the 5 common reasons this error is triggered and provide you with targeted solutions to fix it.

Core Value: By the end of this article, you'll understand exactly how Sora 2's content moderation system works. You'll also learn prompt optimization techniques to dodge the "self-harm" filter so your video generation requests can sail through without a hitch.

Key Takeaways: Sora 2 API Moderation Errors

Deconstructing the Sora 2 API Content Moderation Mechanism

OpenAI has built the industry's most rigorous content safety system for Sora 2, following a "prevention-first" philosophy. The system uses multimodal classifiers to simultaneously analyze text prompts, reference frames, and audio content across three stages: input, generation, and output.

This conservative approach means that even if your creative intent is perfectly legitimate, certain word combinations might trigger the automatic filters. Specifically, regarding the "self-harm" category, the system intercepts any content that might imply dangerous behavior.

5 Reasons Behind Sora 2 API Self-Harm Errors

Reason 1: Prompts Containing Sensitive Action Descriptions

Even if your intent is perfectly innocent, certain action-related words can be misidentified by the system as self-harm content:

Reason 2: High-Intensity Emotional Language

Sora 2's moderation system is extremely sensitive to intense emotional expressions. The following words might trigger self-harm detection:

• desperate / despair – Desperate emotions are often linked to self-harm intent.
• suffering / agony – Descriptions of pain trigger protective mechanisms.
• alone / isolated – Isolation is frequently associated with mental health risks.
• hopeless / give up – "Giving up" can be interpreted as a dangerous signal.
• tears / crying – Scenes involving emotional breakdowns need to be handled with care.

🎯 Optimization Tip: Use a "film director's" perspective to describe scenes. For example, replace character is suffering with character faces a difficult moment. You can quickly verify the pass rate of different phrasings using the APIYI (apiyi.com) test environment.

Reason 3: Reference Image Content

If you're using the input_image or input_reference parameters, the content of the image itself can trigger moderation:

Reason 4: Risk Stacking in Contextual Combinations

A single word might not trigger moderation on its own, but combining multiple "medium-risk" terms can lead to a trigger due to risk stacking:

❌ High-Risk Combination:
"A person standing alone on a rooftop at night, looking down at the city"
- alone (isolation) + rooftop (height) + night (darkness) + looking down (intent) = Triggered

✅ Safe Alternative:
"A photographer capturing city lights from an observation deck at dusk"
- photographer (professional identity) + observation deck (safe location) + capturing (positive action) = Passed

Reason 5: Specific Scenario Pattern Recognition

Sora 2’s moderation system recognizes certain scenario patterns associated with self-harm:

• Bathroom/Restroom scenes + any description of sharp objects.
• Pill bottles/Medication + descriptions of large quantities or "excessive" use.
• High-altitude scenes + descriptions related to the edge or jumping.
• Confined spaces + descriptions of being unable to escape.
• Ropes/Bindings + descriptions related to the neck or hanging.

Important Note: These scenes might be perfectly reasonable in normal creative work (like a bathroom product ad or a pharmaceutical promo), but you'll need to be extra careful with your wording and composition to avoid triggering automatic moderation.

Solutions for Sora 2 API Moderation Errors

Solution 1: Use Professional Filmmaking Terminology

Converting everyday descriptions into professional cinematic terms can significantly reduce the false positive rate:

Solution 2: Add Safety Context

Explicitly add a safe and positive context to your prompt:

# ❌ Prompt likely to trigger moderation
prompt = "A person sitting alone on the edge of a bridge at night"

# ✅ After adding safety context
prompt = """A professional photographer setting up camera equipment
on a well-lit bridge observation platform at twilight,
wearing safety gear, capturing the city skyline for a travel magazine"""

Solution 3: Test with Minimalist Code

Use the APIYI platform to quickly test the pass rate of different prompts:

import requests

def test_prompt_safety(prompt: str) -> dict:
    """Test if a prompt passes Sora 2 moderation"""
    response = requests.post(
        "https://vip.apiyi.com/v1/videos/generations",
        headers={
            "Authorization": "Bearer YOUR_API_KEY",
            "Content-Type": "application/json"
        },
        json={
            "model": "sora-2",
            "prompt": prompt,
            "duration": 4  # Use the shortest duration to save costs
        }
    )
    return response.json()

# Testing different phrasings
safe_prompt = "A dancer performing a graceful leap in a sunlit studio"
result = test_prompt_safety(safe_prompt)
print(f"Test Result: {result}")

import requests
import time
from typing import List, Dict

class SoraPromptChecker:
    """Sora 2 Prompt Safety Checker Tool"""

    # List of known high-risk words
    HIGH_RISK_WORDS = [
        "suicide", "kill", "die", "death", "blood", "bleeding",
        "cut", "cutting", "hurt", "harm", "pain", "suffer",
        "fall", "jump", "crash", "drown", "hang", "choke"
    ]

    CONTEXT_RISK_WORDS = [
        "alone", "isolated", "desperate", "hopeless", "crying",
        "rooftop", "bridge", "cliff", "edge", "night", "dark"
    ]

    def __init__(self, api_key: str):
        self.api_key = api_key
        self.base_url = "https://vip.apiyi.com/v1"

    def check_local(self, prompt: str) -> Dict:
        """Fast local check for prompt risks"""
        prompt_lower = prompt.lower()

        high_risk = [w for w in self.HIGH_RISK_WORDS if w in prompt_lower]
        context_risk = [w for w in self.CONTEXT_RISK_WORDS if w in prompt_lower]

        risk_level = "low"
        if len(high_risk) > 0:
            risk_level = "high"
        elif len(context_risk) >= 2:
            risk_level = "medium"

        return {
            "risk_level": risk_level,
            "high_risk_words": high_risk,
            "context_risk_words": context_risk,
            "suggestion": self._get_suggestion(risk_level)
        }

    def _get_suggestion(self, risk_level: str) -> str:
        suggestions = {
            "high": "We suggest rewriting the prompt using professional cinematic terms instead of sensitive words.",
            "medium": "Try adding safety context to clarify positive intent.",
            "low": "Prompt risk is low; you're likely good to go."
        }
        return suggestions[risk_level]

    def test_with_api(self, prompt: str) -> Dict:
        """Actually test the prompt via API"""
        local_check = self.check_local(prompt)

        if local_check["risk_level"] == "high":
            return {
                "passed": False,
                "error": "Failed local check; please optimize the prompt first.",
                "local_check": local_check
            }

        response = requests.post(
            f"{self.base_url}/videos/generations",
            headers={
                "Authorization": f"Bearer {self.api_key}",
                "Content-Type": "application/json"
            },
            json={
                "model": "sora-2",
                "prompt": prompt,
                "duration": 4
            }
        )

        result = response.json()

        if "error" in result:
            return {
                "passed": False,
                "error": result["error"],
                "local_check": local_check
            }

        return {
            "passed": True,
            "task_id": result.get("id"),
            "local_check": local_check
        }

# Example Usage
checker = SoraPromptChecker("YOUR_API_KEY")

# Fast local check
result = checker.check_local("A person standing alone on a rooftop at night")
print(f"Risk Level: {result['risk_level']}")
print(f"Suggestion: {result['suggestion']}")

Recommendation: Get your API Key through APIYI (apiyi.com) to start testing. The platform offers free credits for new users, which you can use to verify prompt safety and avoid unexpected blocks in your live projects.

Sora 2 API: Comparing Two Moderation Errors

Quick Fix Workflow for sentinel_block

1. Check the Possible reasons prompt within the error message.
2. Cross-reference with the sensitive vocabulary list in this article to locate the problematic words.
3. Replace sensitive phrasing with professional or technical terminology.
4. Resubmit for testing.

Deep Fix Workflow for moderation_blocked

1. Analyze whether the overall scene matches any high-risk patterns.
2. Deconstruct your prompt and check each element individually.
3. Re-imagine the scene and add "safety context" to clarify intent.
4. Run a low-cost test using a short duration (4 seconds).
5. Once the test passes, proceed with generation using your target duration.

Best Practices for Sora 2 API Safety Prompts

Following these principles can significantly reduce "self-harm" false positives:

1. Use positive verbs instead of negative ones

• ❌ fall → ✅ descend / land
• ❌ hurt → ✅ challenge / test
• ❌ suffer → ✅ experience / face

2. Add professional or identity-based context

• ❌ person alone → ✅ photographer working / artist creating
• ❌ standing on edge → ✅ safety inspector checking / tour guide presenting

3. Be clear about positive intent

• ❌ night scene → ✅ twilight photography session
• ❌ high place → ✅ observation deck / scenic viewpoint

4. Use film industry terminology

• ❌ painful scene → ✅ dramatic performance
• ❌ violent action → ✅ choreographed stunt sequence

🎯 Pro-tip: Build your own "Safety Prompt Library" and collect templates that have been tested and verified. Use the APIYI (apiyi.com) platform to test numerous prompt variations at a low cost and quickly build up your collection of effective templates.

FAQ

Summary

Here are the core takeaways for resolving Sora 2 API self-harm moderation errors:

1. Understand the mechanism: Sora 2 uses three layers of moderation and is particularly sensitive to the self-harm category. Even normal word combinations can sometimes lead to false positives.
2. Identify the triggers: Sensitive action verbs, high-intensity emotional words, reference image content, specific context combinations, and certain scene patterns can all trigger moderation.
3. Master the fixes: Using professional cinematic terminology, adding safe context, and clearly stating positive intent are the most effective solutions.

Don't panic when you run into a moderation error. By following the systematic troubleshooting and optimization methods in this guide, you'll be able to resolve the vast majority of cases.

We recommend grabbing some free test credits from APIYI (apiyi.com) to build your own safe prompt library and improve your success rate with Sora 2 video generation.

📚 References

⚠️ Link Format Note: All external links use the Resource Name: domain.com format. This makes them easy to copy while remaining unclickable to avoid SEO weight loss.

1. OpenAI Community Discussion: Sora 2 moderation system sensitivity issues

   • Link: community.openai.com/t/moderation-is-way-too-sensitive-sora-2
   • Description: Developer discussions and shared experiences regarding moderation false positives.

2. Sora 2 Content Restrictions Decoded: Why your prompts are always being blocked

   • Link: glbgpt.com/hub/sora-2-content-restrictions-explained
   • Description: An in-depth analysis of Sora 2 content moderation strategies.

3. APIYI Help Center: Complete guide to Sora 2 API error codes

   • Link: help.apiyi.com
   • Description: A comprehensive collection of solutions for various Sora 2 API error messages.

Author: Technical Team
Technical Exchange: Feel free to join the discussion in the comments! For more resources, visit the APIYI (apiyi.com) technical community.