Risk · R-141 · Generative Content & Information Integrity
Harmful content generation
A system produces toxic, violent, hateful, sexual or otherwise harmful content, and that content reaches users or is published in the organisation's name.
- Severity
- High
- Audience
- Heads-of · Practitioner
- Frameworks
- AML.M0020 · Dangerous Content · Obscene Content
- Responsible AI domain
- Lifecycle stage
- Reviewed
- 2026-11-11
What it is
Models are trained on a broad sample of what people have written and drawn, including the worst of it. Providers add safety training and filters, and these reduce harmful output without eliminating it. It emerges when a user asks for it, when a user tricks the model, and sometimes unprompted, in response to an innocent request. Harm falls on the person who receives it, on the people or groups it targets, and on the organisation whose product said it. Deliberate bypass of safeguards is covered in jailbreaking and safeguard bypass; stereotyped portrayal in stereotyping and demeaning outputs.
How it happens
- The provider's default safety settings are relied on without testing for the use in hand.
- The system is open to the public with no additional filtering.
- An image or voice feature has weaker safeguards than the text model.
- A user supplies content that the model continues or elaborates.
- Safety behaviour weakens after fine-tuning or a model change.
Why it matters
Exposure to violent, sexual or abusive content harms users, and children and vulnerable people most. Some content is unlawful to generate or distribute. A single widely shared example can define public perception of a product.
Warning signs
- No content safety testing was done before launch.
- Filters cover inputs and not outputs, or text and not images.
- User reports of offensive output have no owner.
- Safety was not retested after a change of model.
Who owns it
The system owner is accountable for what the system produces. Engineering implements and tests filtering. Trust and safety, or the equivalent function, sets the content policy and handles reports.
In practice
A retailer adds an image generator to its app so customers can design greetings cards. Within days it has produced violent and sexualised images, some of real public figures, which are shared with the retailer's logo attached. The same pattern generalises to any generative feature released on the assumption that the provider's safeguards are sufficient.
Mitigating controls
Primary controls
- Content safety filtering Preventive
- Classifies and blocks harmful inputs and outputs. Inputs and outputs are classified against defined harm categories by a mechanism independent of the main model, and are blocked, modified or escalated according to policy.
Supporting controls
- AI incident classification and playbooks Corrective
- Provides a response when harmful content is published. The kinds of AI failure are defined in the incident process with severity criteria, each has a playbook, and responders are trained and exercised in using them.
- AI red teaming Detective
- Tests for harmful outputs before release. People skilled in attacking AI systems try to make the system fail as an adversary would, before release and periodically, and their findings are tracked to closure.
- Feedback and complaints channel for AI Detective
- Lets users report what got through. Users and affected people can report a problem with an AI system easily, and reports are triaged, linked to the system concerned and analysed for patterns.
Framework and clause references
| Framework | Clause | Title |
|---|---|---|
| MITRE ATLAS | AML.M0020 | Generative AI Guardrails Generative AI guardrails. |
| NIST Generative AI Profile (NIST AI 600-1) | Dangerous Content | Dangerous, Violent, or Hateful Content Dangerous, violent or hateful content. |
| NIST Generative AI Profile (NIST AI 600-1) | Obscene Content | Obscene, Degrading, and/or Abusive Content Obscene or abusive content. |
Sources
- NIST Generative AI Profile NIST · framework
- MITRE ATLAS MITRE · framework
Related terms
How to cite
Axiom Verity (2026). Harmful content generation. Reference Library, R-141. https://axiom-verity.com/risks/harmful-content-generation. Reviewed 2026-11-11.
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