Do AI Hallucinations Create Legal Liability? A Tech E&O Insurance Perspective

By Steven Barge-Siever, Esq.

Founder, Upward Risk Management LLC

The rise of generative AI brought a new legal dilemma: What happens when your AI gets it wrong?

In the world of large language models (LLMs), hallucinations aren’t glitches - they’re a byproduct of how statistical prediction works. These confident but false outputs can lead to real-world consequences: lawsuits, regulatory actions, and reputational damage.

So who pays when an AI-generated answer causes harm? And more importantly: Is that risk insurable?

This guide breaks down how hallucinations are viewed by courts, regulators, and insurers - and what AI-native companies need to know before relying on boilerplate Tech E&O.

Section 1: What Is an AI Hallucination?

A hallucination is a confident but false output generated by an AI system, usually an LLM. It appears plausible, but is factually inaccurate, misleading, or entirely fabricated.

Common forms of hallucination:

• Fake citations or references

• Mislabeling people, data, or facts

• Unauthorized statements about third parties

• Synthetic quotes or contracts

Systems prone to hallucinations:

• LLMs (e.g., GPT, Claude, Gemini) - prone to text generation errors

• RAG pipelines - if context retrieval fails or is misranked

• Auto-piloted tools - AI agents that take action based on hallucinated content

Section 2: The Legal and Insurance Exposure for AI Hallucinations

AI hallucinations aren’t just embarrassing - they create tangible legal risk.

Key legal exposures include:

• User or customer reliance → If an enterprise client relies on incorrect AI output and suffers harm, they may sue for breach of contract, negligence, or product liability.

  
  

• Regulatory actions → The FTC and SEC have both warned that AI-generated false claims or deceptive marketing will be scrutinized under existing enforcement authority.

  
  

• Contractual breaches → Failing to meet service-level obligations (SLAs) due to faulty AI output can trigger contractual liability or indemnity.

  
  

• Torts and civil claims → If an AI model outputs defamatory content, discriminatory text, or infringes on IP, third parties may sue for damages.

These liabilities are often novel, but insurers treat them as variants of known risk categories, which is why policy structure matters.

Section 3: The Insurance Angle - Is This Covered?

AI hallucinations fall into a gray area. Here’s how existing policies respond:

1. Tech Errors & Omissions (Tech E&O)

This is the primary policy that may respond.

• Trigger: A third party alleges harm caused by your AI product or service.

  
  

• Key hurdle: Was the risk foreseeable? Did your team follow standard of care? Did you disclose limitations?

2. Cyber Liability

Cyber only applies if the hallucination involves data misuse, a security breach, or leaked PII. Most hallucinations fall outside its scope.

3. Media Liability

If your product generates content (text, image, video, code), media liability may apply - particularly for defamation, IP violations, or reputational harm.

Often embedded in Tech E&O or sold separately.

4. Exclusions to Watch

• AI-specific exclusions - Some carriers now exclude "algorithmic decision-making" or "machine learning output"

  
  

• IP exclusions - Broad IP language may eliminate coverage for generated content

  
  

• Knowledge exclusions - Claims based on known bugs or failure to mitigate may be denied

Bottom line: Most generic policies were not built for LLM output.  You need either endorsements or specific insurers that include coverage language to ensure you insurance works.

Section 4: Real-World Scenarios

1. A chatbot gives faulty tax advice → A user acts on it and suffers financial loss → They sue the company behind the model for negligence.

   
   

2. A generative model invents a fake employee → A candidate is publicly misidentified → They sue for defamation.

   
   

3. An LLM inserts incorrect compliance language into a legal document → The enterprise client assumes it's accurate → Breach of contract.

Each of these situations has happened - and in each, the cost of legal defense alone could cripple an early-stage startup.

Section 5: How AI Companies Should Protect Themselves

Mitigation: Build defensibility into your platform

• Use RAG, confidence scoring, and output disclaimers

  
  

• Monitor and log how output is generated

  
  

• Maintain documentation of your model’s limitations and updates

Policy Structure: Build coverage that reflects your product

• Add affirmative AI coverage into Tech E&O

  
  

• Ensure media liability covers generative content risks

  
  

• Watch for silent exclusions that limit LLM output protection

Underwriting: Help your broker help you

• Explain how outputs are flagged, filtered, or tested

  
  

• Share SLAs or disclaimers with enterprise customers

  
  

• Document internal QA processes for AI-generated content

Conclusion

AI hallucinations are more than a UX problem - they’re a legal and financial liability. And like any risk, they’re insurable if you structure your coverage intelligently.

If you're an AI founder, GC, or compliance leader:

• Review your Tech E&O policy

• Confirm whether hallucinations are covered

• Get ahead of this before it becomes a court exhibit

Want an “AI Hallucination Exposure Memo”? We’ll walk through your current insurance stack and flag whether your model outputs are actually insured.

Talk to Us:

Steven Barge-Siever, Esq.

info@upwardriskmanagement.com