Toxic AI Content | How Lakera Prevents Harmful Language Generation

Toxic Content Generation: How this works and how Lakera stops it

The production of harmful, offensive, or unsafe language by an AI system.

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Statistics

15,000 Average number of threats detected per day
0.01% False-positive rate
100+ Languages covered
<12ms Average latency

How the attack works

Unprotected System

Protected by Lakera

UNTRUSTED USER PROMPT
Write a funny comment insulting a coworker for making mistakes.

LLM ASSISTANT RESPONSE

Input & Context

A malicious user may leverage an organization's chatbot to deviate from its grounding and internal guardrails to generate harmful, offensive, or unsafe context for a variety of reasons, one being reputational damage for the brand.

Policy snippet (copy/paste)

{
  "data": {
    "name": "AI Policy",
    "policy_mode": "IO",
    "input_detectors": [
      {
        "type": "prompt_attack",
        "threshold": "l2_very_likely"
      }
    ],
    "output_detectors": [
      {
        "type": "pii/credit_card",
        "threshold": "l2_very_likely"
      }
    ],
    "id": "policy-9b52e331-d609-4ce3-bbb9-d2b1e72a0f20"
  }
}

Lakera Decision

Lakera Guard’s Prompt Defense guardrails can detect the attempt when checking the input prompt, preventing the message from reaching the LLM. As it is sensible to scan both input and output content from the LLM, should the prompt reach the LLM, scanning the output would also trigger a moderation alert. Lakera flags unsafe instructions and output content, detects disguised intent and logs the event for audit and review.

Log & audit fields

{
  "payload": [],
  "flagged": true,
  "dev_info": {
    "timestamp": "2025-11-24T12:35:12Z"
  },
  "metadata": {
    "request_uuid": "ce8180b1-26bc-4177-9d7f-54ca7377378a"
  },
  "breakdown": [
    {
      "project_id": "project-7539648934",
      "policy_id": "policy-a2412e48-42eb-4e39-b6d8-8591171d48f2",
      "detector_id": "detector-lakera-default-prompt-attack",
      "detector_type": "prompt_attack",
      "detected": true,
      "message_id": 0
    }
  ]
}

How Lakera stops toxic content generation

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Real-Time, Context-Aware Detection: Catch instruction overrides, jailbreaks, indirect injections, and obfuscated prompts as they happen, before they reach your model.
Enforcement You Control: Block, redact, or warn. Fine-tune with allow-lists and per-project policies to minimize false positives without weakening protection.
Precision & Adaptivity: Lakera Guard continuously learns from 100K+ new adversarial samples each day. Adaptive calibration keeps false positives exceptionally low.
Broad Coverage: Protects across 100+ languages and evolving multimodal patterns, with ongoing support for image and audio contexts.
Enterprise-Ready: Full audit logging, SIEM integrations, and flexible deployment options, SaaS or self-hosted, built for production-scale GenAI systems.

Frequently asked questions

Can customers tune moderation sensitivity for different use cases or regions?
Absolutely. Each “policy” in Lakera Guard lets you set a flagging sensitivity level (L1 lenient → L4 strict) so you can tailor strictness by use case or risk profile.
Does Lakera log moderation events for review and policy improvement?
Yes. Lakera logs policy changes (creations, edits, deletes) and retains full audit history of those actions.
What types of content does Lakera Guard cover?
1. Crime: content that mentions criminal activities, including theft, fraud, etc.
2. Hate: harassment and hate speech.
3. Profanity: obscene or vulgar language.
4. Sexual: sexually explicit content.
5. Violence: content describing acts of violence.
6. Weapons: content that mentions weapons or weapon usage.
7. Custom content moderation guardrails can be created as well.

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