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Toxic, Biased or Harmful AI-generated Content

What Is Toxic, Biased or Harmful AI-generated Content?

An LLM, whether jailbroken or simply operating within its normal range of outputs, can produce content that's toxic, biased, or otherwise harmful to an organization, its employees, or its customers, with consequences ranging from an embarrassing screenshot circulating on social media to a damaged customer relationship to legal exposure. This risk exists even when a system is functioning exactly as designed, since model outputs are non-deterministic by nature.

Key Concerns

  • Inappropriate Content: filtering material unsuitable for the intended audience.
  • Competitive Missteps: preventing AI systems from inadvertently promoting or favoring competitors.

‍

FAQ

No, that's a common misconception. Toxic or biased output can occur without any manipulation at all, simply as a result of how the model was trained and how it responds to an unusual or ambiguous prompt.

The widely reported case of a Chevrolet dealership chatbot being manipulated into agreeing to sell a car for a dollar, covered on the Prompt Injection page, is a mild but illustrative example of an LLM app producing an off-script, brand-damaging response.


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Related Terms


Jailbreak

Jailbreaking is a category of prompt injection focused on getting a model to ignore its safety training and guardrails rather than hijacking it for a specific downstream action.

Prompt Injection

Prompt injection is an attack where crafted input causes a large language model to deviate from its intended instructions and follow the attacker's instead.

Related Resources

Prompt Injection 101

AI Risks

Nov 3rd, 2024

Uncover real-world prompt injection examples and learn how these attacks work, why they’re hard to block & what you can do to protect AI systems.

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