Klingai 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

Prerequisites

• A versioned policy configuration, an owner for escalation, a review queue, and a documented retention/deletion schedule.
• A synthetic or rights-cleared fixture set for tests. Likeness, voice, and other identifiable-person inputs require documented consent; do not rely on a prompt filter as proof of rights.
• A bounded credit budget and a private, watermarked draft destination. Public distribution requires a separate approval record after policy and quality checks.

Instructions

1. Normalize the prompt and provenance metadata, then run the local filter before creating a task. Preserve only a redacted reason code for rejected content.
2. Check violence, sexual content, hate, illegal activity, self-harm, misinformation, likeness/deepfake, and copyrighted-character risk. Route ambiguous cases to human review rather than trying to evade the policy with sanitization.
3. Confirm that every image, mask, tail frame, and reference asset is synthetic or rights-cleared and that the requested destination and audience are approved.
4. Submit only a short, watermarked sandbox canary within the credit budget. Keep it private until the policy result, visual review, consent record, and owner approval are complete.
5. If the provider rejects the task or a reviewer withdraws approval, do not retry the same request. Quarantine and remove staged media, revoke temporary links, and restore the previous approved version.
6. Retain a redacted receipt with policy version, reason code, opaque task digest, approval state, budget state, retention deadline, and rollback reference; exclude prompts, images, identities, and credentials.

Output

Return one of approved_for_draft, needs_human_review, or blocked, together with an opaque request digest, policy version, reason codes, rights/provenance result, canary state, budget result, and retention/rollback instructions. A blocked result must not create a public artifact or expose the submitted content in logs.

Error Handling

Reject locally when a known restricted pattern, missing consent, unknown provenance, disallowed destination, or budget breach is detected. Treat provider policy failures as final for that request and report a user-safe revision hint; do not claim that sanitization makes an unsafe request permissible. For classifier outages or ambiguous results, fail closed into human review. Quarantine any output that later receives a complaint, remove its distribution links, preserve only the redacted audit receipt, and record the rollback owner.

Examples

An internal canary decision can be recorded as:

fixture=synthetic-product-v4; rights=cleared; likeness=none;
policy=pass-v3; destination=staging-private; canary=watermarked;
budget=within-limit; approval=pending; decision=approved_for_draft

An identifiable-person image without a consent record must instead return blocked and create no generation task.

Resources

• Kling AI Terms of Service
• Developer Portal