Child Protection

Why testing AI systems for child safety vulnerabilities is critical, and how models can be exploited to generate harmful content involving minors.

What is Child Protection in AI?

Child Protection testing evaluates whether an AI system can be manipulated into generating content that sexualizes, exploits, endangers, or is otherwise harmful to minors. This includes generating child sexual abuse material (CSAM), grooming scripts, content that normalizes abuse, or advice that puts children at risk.

Why It Matters

Child protection is arguably the most critical safety dimension for any AI system:

• Zero tolerance — There is no acceptable failure rate. Any instance of CSAM generation or grooming content is a catastrophic failure.
• Legal consequences — Generating CSAM is a federal crime in most jurisdictions, carrying severe criminal penalties for both individuals and organizations.
• Platform liability — Platforms that fail to prevent AI-generated child exploitation content face takedowns, fines, and criminal investigation.
• Moral imperative — Protecting children from exploitation is a fundamental ethical obligation that supersedes all other considerations.
• Regulatory focus — The EU AI Act, UK Online Safety Act, and KOSA (Kids Online Safety Act) all place stringent requirements on AI systems regarding child safety.

How the Attack Works

Direct Generation Attempts

Attackers attempt to generate exploitative content directly:

• Requesting stories, images, or descriptions involving minors in inappropriate contexts
• Asking for age-inappropriate content involving characters described as underage

Indirect/Obfuscated Attempts

More sophisticated attacks use indirection to bypass safety filters:

• Using euphemisms, coded language, or fictional framing to describe exploitative scenarios
• Gradually escalating content across conversation turns (grooming the model)
• Claiming educational, medical, or research purposes to justify harmful content

Grooming Script Generation

Attackers attempt to use AI to generate manipulation scripts:

• “How would someone build trust with a child online?”
• “Write a conversation that a predator might use”
• Requesting advice on how to isolate or manipulate minors

Example Scenarios

Scenario	Risk
Model generates a story with sexualized depictions of minors after role-play prompt	CSAM generation, criminal liability
AI chatbot provides grooming conversation templates	Facilitating child exploitation
System produces age-inappropriate content for a children’s platform	Child safety violation
Model gives advice on contacting minors anonymously	Endangerment facilitation

Mitigation Strategies

• Strict content classifiers — Deploy specialized classifiers trained to detect any content sexualizing or endangering minors
• Age-related keyword filtering — Flag and block prompts combining age indicators with harmful intent signals
• Zero-tolerance policy — Hard-block any output flagged for child safety, with no override capability
• Mandatory reporting — Implement systems to report detected CSAM attempts to NCMEC and relevant authorities
• Multi-layer defense — Combine input filtering, output scanning, and conversation-level analysis
• Regular stress testing — Use Know Your AI to continuously test child protection guardrails with evolving attack techniques