Understanding and Addressing Inaccurate or Misleading Outputs in AI Systems

Inaccurate outputs weaken the trustworthiness of AI systems, particularly large language models (LLMs), by generating responses that appear credible but lack accuracy. When incorrect information is presented as a fact, users may unknowingly rely on flawed outputs that seem correct but ultimately are fabricated. The underlying causes can stem from weaknesses in training data, insufficient prompt preparation, or inherent model limitations, leading to hallucinations that convincingly present false information. When AI systems produce authoritative but incorrect responses, they can mislead users into adopting unreliable conclusions or introducing errors that influence broader organizational operations. Addressing these challenges requires continuous evaluation of AI-generated content, ensuring safeguards are in place to detect unreliable outputs before they can cause harm. Without this level of scrutiny, inaccuracies can persist, creating deeper risks that extend beyond a single flawed response and affect the overall integrity of AI-driven processes.

What Are Inaccurate or Misleading Outputs?

Inaccurate or misleading outputs occur when AI systems generate responses that do not align with factual accuracy, meaning they fail to provide accurate and verifiable information. These systems may also produce outputs that do not adequately address the user's intended context or requirements. These problems can originate from errors in the training data, misinterpretations of the input context, or deliberate actions by external actors. Such failures reduce the usefulness of AI systems, exposing individuals and businesses to potential reputational damage.

A notable example involves an attorney who, in 2023, used ChatGPT to prepare a legal brief for a federal court case. The AI-generated brief included six fictitious case citations, which Schwartz unknowingly submitted as legitimate precedents. When the inaccuracies were exposed, the court determined that the lawyer and his firm had acted in bad faith, resulting in a $5,000 sanction imposed by U.S. District Judge P. Kevin Castel. This case damaged the firm’s reputation and highlighted the risks of unverified AI-generated content. (Reuters, Legal Dive)

More recently, in February 2025, additional attorneys faced potential sanctions after submitting fabricated case citations in a lawsuit against a large retailer. The case involved a Wyoming family alleging that a hoverboard purchased from Walmart malfunctioned, causing a fire that destroyed their home and resulted in severe burns and emotional trauma. Upon review, U.S. District Judge Kelly Rankin discovered that eight cases cited in the plaintiffs' pretrial motion were non-existent. Judge Rankin ordered the plaintiffs' attorneys to explain the origin of these citations and why they should not face disciplinary action. In response, the lawyers admitted that their internal AI platform had "hallucinated" the cases, leading to the inclusion of false information in the court filing. (Reuters, 5 News Online)

Why Inaccurate or Misleading Outputs Matter to Organizations

When AI produces factually incorrect information, it undermines trust in its reliability, misguides users, and disrupts decision-making processes. The consequences of unreliable AI outputs can be particularly damaging in industries where accuracy is critical. In legal settings, as seen in the cases above where attorneys unknowingly submitted fabricated case citations, reliance on incorrect AI-generated information can result in professional sanctions and reputational harm. In healthcare, incorrect recommendations could lead to misguided treatment plans, putting patients at risk. In finance, flawed AI-driven analysis may lead to poor investment decisions or regulatory non-compliance, exposing firms to financial losses and legal scrutiny. Organizations must ensure that AI-generated information is reliable and verifiable to maintain trust. When AI-driven processes fail to deliver accurate information, the resulting damage can extend well beyond a single mistake, shaping public perception and influencing the willingness of businesses and institutions to integrate AI into their workflows.

Examples of Inaccurate or Misleading Output Risks

1.????? Hallucinated Professional Advice: AI systems used in professional settings, such as legal or financial advisory services, can generate factually incorrect responses that appear legitimate. As seen in the above cases, AI-generated legal research can fabricate case citations that, if relied upon, lead to professional or legal consequences.

2.????? Medical Misdiagnosis and Risk to Patient Safety: AI tools assisting in diagnosis or treatment recommendations can misinterpret symptoms, suggest ineffective treatments, or overlook critical conditions in healthcare. If these outputs are trusted without verification, they can contribute to misdiagnoses, improper treatment, and patient harm.

3.????? Incorrect AI-Generated Business Reports: AI-powered tools used for corporate decision-making may produce misleading analyses based on incomplete or outdated training data. A company using AI for strategic planning could receive flawed insights into market trends, leading to poor investment decisions.

Strategies to Mitigate Inaccurate or Misleading Outputs

The following are some strategies that provide a structured approach to mitigating inaccurate or misleading AI outputs, helping organizations maintain the integrity and effectiveness of their AI systems.

Strengthening Training Data Quality and Model Validation

Organizations must ensure that AI models are trained on verifiable, representative, and current information to reduce inaccuracies.

• Establish strict data curation processes that remove outdated, incomplete, or inaccurate information from training datasets.
• Validate AI models through continuous testing, ensuring they produce accurate and reliable results across different scenarios.
• Use real-world examples and independent verification to identify and correct patterns that lead to fabricated or inaccurate responses.

Implementing Human Oversight for AI-Generated Content

AI-generated outputs should include human validation in environments where accuracy can affect life, limb, and property.

• Train users to critically assess AI-generated responses rather than treating them as definitive sources of truth.
• Establish human review as a required step for mission-critical AI-generated content.
• Develop explainability tools that allow users to understand how AI models arrive at conclusions.

Improving Prompt Design to Reduce Misinterpretation

AI-generated inaccuracies often stem from poorly constructed prompts that fail to provide enough context or clarity.

• Train users on effective, prompt engineering, ensuring they ask questions that lead to factually grounded responses.
• Develop structured input guidelines that prevent AI from generating speculative or fabricated answers.
• Refine AI systems to clarify uncertainty, enabling models to recognize when they lack sufficient data rather than filling gaps with inaccurate information.

Establishing Boundaries for AI’s Scope of Use

AI should not be used beyond its intended expertise. If models lack relevant data, they should not generate responses rather than risk misleading users.

• Define specific domains where AI can provide recommendations and prevent AI from responding to queries outside its scope.
• Ensure AI models do not generate authoritative-sounding responses when the information provided is speculative.
• Require AI-generated responses to indicate uncertainty when confidence is low, prompting users to verify critical information before relying on it.

Building Confidence in AI Systems

Responsible AI deployment is not just about reducing errors. It is about ensuring that these systems remain effective, trustworthy, and capable of delivering real value without compromising integrity. Organizations must implement safeguards that ensure AI models operate within their intended scope, clearly communicate uncertainty when necessary, and provide outputs grounded in verified data. Without these measures, AI risks becoming an unreliable tool that introduces confusion rather than supporting informed decision-making. Prioritizing accuracy at every stage—from data selection to model deployment—is essential to prevent misleading outputs from influencing decisions. Organizations that take a disciplined approach to managing AI can reinforce confidence in its role, ensuring that it enhances operations rather than detracts from them.

Further Reading

Read my previous articles in my series on the OWASP Top 10 for Large Language Model (LLM) Applications.

• Understanding and Addressing Prompt Injection in AI Systems
• Understanding and Addressing Sensitive Information Disclosure in AI Systems
• Understanding and Addressing Supply Chain Risks in AI Systems
• Understanding and Addressing Data and Model Poisoning in AI Systems
• Understanding and Addressing Improper Output Handling in AI Systems
• Understanding and Addressing Excessive Agency in AI Systems
• Understanding and Addressing System Prompt Leakage in AI Systems
• Understanding and Addressing Vector and Embedding Weaknesses in AI Systems