Kling AI Content Policy

Kling AI Content Policy

Overview

Kling AI enforces content policies server-side. Tasks with policy-violating prompts return task_status: "failed" with a content policy message. This skill covers pre-submission filtering to avoid wasted credits and API calls.

Restricted Content Categories

Kling AI prohibits prompts that generate:

Pre-Submission Prompt Filter

import re

class PromptFilter:
    """Filter prompts before sending to Kling AI to save credits."""

    BLOCKED_PATTERNS = [
        r"\b(nude|naked|explicit|nsfw|porn)\b",
        r"\b(gore|dismember|torture|mutilat)\b",
        r"\b(bomb|terroris|weapon|firearm)\b",
        r"\b(suicide|self.harm|kill.yourself)\b",
        r"\b(deepfake|impersonat)\b",
    ]

    BLOCKED_TERMS = {
        "blood splatter", "graphic violence", "child abuse",
        "drug manufacturing", "hate speech",
    }

    def __init__(self):
        self._patterns = [re.compile(p, re.IGNORECASE) for p in self.BLOCKED_PATTERNS]

    def check(self, prompt: str) -> tuple[bool, str]:
        """Returns (is_safe, reason)."""
        lower = prompt.lower()

        for term in self.BLOCKED_TERMS:
            if term in lower:
                return False, f"Blocked term: '{term}'"

        for pattern in self._patterns:
            match = pattern.search(prompt)
            if match:
                return False, f"Blocked pattern: '{match.group()}'"

        if len(prompt) > 2500:
            return False, "Prompt exceeds 2500 character limit"

        if len(prompt.strip()) < 5:
            return False, "Prompt too short"

        return True, "OK"

    def sanitize(self, prompt: str) -> str:
        """Remove problematic terms and return cleaned prompt."""
        for pattern in self._patterns:
            prompt = pattern.sub("[removed]", prompt)
        return prompt.strip()

Safe Negative Prompts

Always include safety-related negative prompts:

DEFAULT_NEGATIVE_PROMPT = (
    "violence, gore, blood, nudity, sexual content, "
    "weapons, drugs, hate symbols, distorted faces, "
    "watermark, text overlay, low quality, blurry"
)

def safe_request(prompt: str, negative_prompt: str = ""):
    """Build request with safety defaults."""
    combined_negative = f"{DEFAULT_NEGATIVE_PROMPT}, {negative_prompt}".strip(", ")
    return {
        "model_name": "kling-v2-master",
        "prompt": prompt,
        "negative_prompt": combined_negative,
        "duration": "5",
        "mode": "standard",
    }

Integration with Client

class SafeKlingClient:
    """Kling client with pre-submission content filtering."""

    def __init__(self, base_client):
        self.client = base_client
        self.filter = PromptFilter()

    def text_to_video(self, prompt: str, **kwargs):
        is_safe, reason = self.filter.check(prompt)
        if not is_safe:
            raise ValueError(f"Content policy violation: {reason}")

        # Add safety negative prompt
        kwargs.setdefault("negative_prompt", "")
        kwargs["negative_prompt"] = (
            f"{DEFAULT_NEGATIVE_PROMPT}, {kwargs['negative_prompt']}".strip(", ")
        )

        return self.client.text_to_video(prompt, **kwargs)

Handling Server-Side Rejections

def handle_policy_rejection(task_id: str, result: dict):
    """Handle content policy rejections gracefully."""
    status_msg = result["data"].get("task_status_msg", "")

    if "content policy" in status_msg.lower() or "policy violation" in status_msg.lower():
        return {
            "error": "content_policy_violation",
            "message": "Your prompt was rejected by Kling AI's content policy. "
                      "Please revise to remove restricted content.",
            "task_id": task_id,
            "credits_consumed": False,  # policy rejections typically don't consume credits
        }
    return {"error": "generation_failed", "message": status_msg, "task_id": task_id}

User-Facing Guidelines

When building apps with user-submitted prompts:

1. Filter before API call -- saves credits on obvious violations
2. Explain rejections clearly -- tell users what to change
3. Log violations -- track patterns for filter improvement
4. Rate limit prompt submissions -- prevent abuse
5. Review flagged content -- human review for edge cases

Resources

• Kling AI Terms of Service
• Developer Portal