AI Misinformation Explained: When Your AI Confidently Gets It Wrong (OWASP LLM Top 10 #9)
Large language models do not understand what they say. They generate text by predicting the most statistically likely next token based on patterns learned during training, which means they can produce information that sounds authoritative, reads fluently, and is entirely wrong. Misinformation, ranked #9 in the 2025 OWASP Top 10 for LLM Applications, addresses this fundamental risk: AI systems that generate false or misleading content that users trust and act upon. For nonprofits that depend on accurate information to serve vulnerable populations, secure funding, and maintain public trust, AI-generated misinformation is not merely an inconvenience. It is a direct threat to mission, credibility, and the communities these organizations exist to protect.
A nonprofit housing organization deploys an AI chatbot to help clients navigate available assistance programs. A single mother asks about rental assistance eligibility in her county. The AI responds with a detailed, well-structured answer citing a specific program name, dollar amounts, application deadlines, and qualification criteria. The response is completely fabricated. The program does not exist. The mother spends two weeks pursuing an application that leads nowhere, missing the deadline for a real program she could have qualified for. The AI did not flag any uncertainty. It presented false information with the same confidence it uses for accurate responses.
This is not a hypothetical edge case. It is the central risk that the OWASP LLM Top 10 addresses with the Misinformation category. The phenomenon, commonly called "hallucination," occurs when language models generate content that has no basis in their training data or in reality. The term "hallucination" can be misleading because it implies the model is experiencing something; in truth, the model is simply completing a statistical pattern without any mechanism for distinguishing fact from fiction. The result is output that ranges from subtle inaccuracies, such as incorrect dates or slightly wrong statistics, to wholesale fabrication of events, legal citations, scientific claims, and organizational policies that never existed.
This is the ninth article in our series covering every vulnerability in the OWASP Top 10 for LLM Applications. The first article covered prompt injection, the mechanism by which attackers manipulate AI inputs. The second examined sensitive information disclosure. The third explored supply chain risks. The fourth covered data and model poisoning. The fifth examined insecure output handling. The sixth addressed excessive agency. The seventh covered system prompt leakage. And the eighth examined vector and embedding weaknesses. Misinformation interacts closely with several of these vulnerabilities: poisoned training data (LLM04) can increase hallucination rates, insecure output handling (LLM05) can amplify the damage of false outputs, and excessive agency (LLM06) means hallucinated instructions could trigger real-world actions.
What makes misinformation particularly dangerous compared to other OWASP categories is that it requires no attacker. While prompt injection, data poisoning, and supply chain attacks all involve a malicious actor deliberately exploiting the system, misinformation arises from the fundamental nature of how language models work. Every organization using an LLM is exposed to this risk, regardless of how well they have secured their AI infrastructure against external threats. The model itself is the source of the problem, and the risk scales with every additional use case, user, and decision that depends on AI-generated content.
This article explains why LLMs produce misinformation, the specific patterns that make hallucinated content dangerous, why traditional quality assurance approaches fall short, and how organizations can build layered defenses that reduce the likelihood and impact of false AI outputs. For nonprofits operating in environments where accuracy directly affects people's lives, understanding this vulnerability is not optional.
What AI Misinformation Actually Is
In the context of the OWASP Top 10 for LLM Applications, misinformation refers to the generation of false, misleading, or fabricated content by a language model that appears credible and is presented without appropriate uncertainty signals. This is broader than just "hallucination," though hallucination is the most commonly discussed form. Misinformation from LLMs includes factual errors where the model states something incorrect as though it were true, fabricated references where the model invents citations, legal cases, or research papers that do not exist, outdated information where the model presents information that was once accurate but no longer reflects current reality, and conflated facts where the model combines elements from different real sources to create a plausible but incorrect composite.
The root cause is architectural. Language models are next-token prediction engines. During training, they learn statistical patterns from vast amounts of text, including which words and phrases tend to follow other words and phrases. When generating a response, the model selects the most likely continuation at each step. It has no internal fact database, no truth-verification mechanism, and no awareness of whether its output corresponds to reality. The model that correctly states that water boils at 100 degrees Celsius at sea level and the model that fabricates a nonexistent Supreme Court ruling are using exactly the same process: statistical prediction. The difference is only in whether the training data happened to produce patterns that align with reality for that particular query.
This is fundamentally different from how traditional software fails. When a database query returns wrong results, it is typically because the data was entered incorrectly or the query logic is flawed, and both can be debugged and traced. When a language model generates misinformation, there is often no specific "bug" to find. The model is operating exactly as designed; the design simply does not include a concept of truth. This distinction matters because it means misinformation cannot be "fixed" through code patches or configuration changes alone. It requires a fundamentally different approach to quality assurance, one that treats every AI output as potentially unreliable until verified.
For organizations accustomed to traditional software where outputs are deterministic and reproducible, this represents a paradigm shift. The same question asked twice may produce different answers, both of which may contain different errors. The model's confidence level, as expressed in how assertive its language is, has little correlation with accuracy. An LLM can state something with complete certainty one moment and contradict itself the next. Understanding this fundamental unreliability is the starting point for building effective defenses.
Why LLMs Hallucinate: The Core Mechanisms
Training Data Gaps
When the model encounters a query about topics underrepresented in its training data, it fills gaps with statistically plausible content rather than acknowledging uncertainty.
- •Niche nonprofit programs and local regulations
- •Recent policy changes after the training cutoff date
- •Organization-specific procedures and eligibility criteria
Pattern Completion Without Grounding
The model generates text based on what "sounds right" rather than what "is right," producing confident-sounding output that may be entirely fabricated.
- •Fabricated statistics that follow expected numerical patterns
- •Invented citations in academic formatting style
- •Plausible-sounding legal references that do not exist
How AI Misinformation Works in Practice
Misinformation from AI systems takes many forms, and not all of them are as obvious as a completely fabricated fact. In practice, the most harmful misinformation often involves subtle inaccuracies embedded within otherwise correct information, making it far harder to detect. Here are the primary patterns that organizations encounter when deploying LLMs in operational contexts.
Fabricated Citations and References
The model invents sources, legal cases, research papers, or statistics that do not exist
One of the most well-documented forms of AI misinformation involves the fabrication of references. When asked to support a claim with evidence, LLMs frequently generate citations that look completely legitimate, with proper formatting, plausible author names, realistic journal titles, and consistent date ranges, but point to sources that were never published. This has caused real legal consequences: according to reporting from the Cronkite News Service, courts have documented hundreds of instances where lawyers submitted briefs containing AI-hallucinated case citations, resulting in sanctions, fines, and professional discipline.
For nonprofits, fabricated citations can appear in grant applications where AI is used to support program rationale with research evidence, in policy briefs shared with legislators, in compliance documentation referencing specific regulations, or in reports to funders citing program outcomes. A fabricated citation in a grant application does not just risk rejection; it damages the organization's credibility with that funder permanently. When a foundation discovers that the research supporting a grant request does not exist, the conversation shifts from program funding to organizational integrity.
The danger is compounded by how convincing these fabrications can be. The model does not randomly generate gibberish. It produces citations that follow the exact formatting conventions of the field, use author names that sound real (and sometimes are real researchers working in adjacent fields), and reference journals that actually exist but never published the specific article cited. Verifying a single citation takes minutes of research. Verifying dozens across a long document requires a systematic process that many organizations do not have in place.
Confidently Wrong Guidance
The model provides incorrect advice on regulations, eligibility, or procedures with full confidence
Perhaps the most operationally dangerous form of misinformation occurs when an AI provides incorrect guidance about legal requirements, regulatory compliance, or program eligibility. Air Canada learned this lesson when its customer service chatbot invented a bereavement fare discount policy that did not exist; a tribunal subsequently ruled that Air Canada was liable for its chatbot's false claims, establishing that organizations cannot disclaim responsibility for AI-generated misinformation in customer-facing contexts.
For nonprofits, this pattern is especially hazardous in contexts involving regulatory compliance. An AI advising staff on HIPAA requirements might state confidently that a particular data sharing practice is permitted when it is not. An AI helping with grant compliance might describe reporting requirements that differ from the actual terms of the award. An AI chatbot helping clients navigate benefits enrollment might describe eligibility criteria that are subtly wrong, leading people to apply for programs they do not qualify for or, worse, to skip programs they could have accessed.
The core problem is that the model does not express uncertainty proportional to its actual reliability. It uses the same authoritative tone whether it is reciting well-established facts or generating content from sparse or conflicting training data. Users, especially those without deep domain expertise, have no reliable way to distinguish between accurate guidance and confident fabrication without independent verification. Over time, as staff experience the AI being correct on easy questions, they build a false sense of trust that extends to the harder, more consequential queries where the model is most likely to hallucinate.
Fabricated Data and Statistics
The model generates plausible-sounding numbers, percentages, and data points that have no basis in reality
When asked to quantify something, LLMs have a strong tendency to produce specific numbers rather than acknowledging that they do not have the data. Ask a model about homelessness rates in a specific county, program completion rates for a particular intervention, or the average cost of a certain service, and it will often respond with precise figures that look like they came from an authoritative source. These numbers may be in the right general range, which makes them even harder to identify as fabricated, or they may be wildly inaccurate.
For organizations that use AI to support data-driven decision making, fabricated statistics represent a serious risk. Board presentations built on hallucinated outcome metrics, fundraising appeals citing invented impact numbers, and program evaluations referencing nonexistent benchmarks all erode the integrity of the organization's work. When a funder or auditor later checks the numbers and cannot find the source, the damage extends beyond the specific claim to the organization's overall credibility.
Research into hallucination rates shows that the problem is far from solved. According to a 2026 analysis by All About AI, even the best-performing models still hallucinate in a measurable percentage of responses, with some models producing fabricated content in over 25% of their outputs. The variation depends heavily on the topic, the specificity of the question, and how well-represented the subject matter was in the model's training data. Niche topics relevant to nonprofits, such as local government programs, specific grant requirements, and regional service providers, fall squarely in the high-hallucination zone.
Subtle Bias and Distortion
The model reflects biases from its training data, presenting skewed perspectives as objective fact
Not all misinformation involves outright fabrication. LLMs can also produce content that is technically not false but presents a significantly biased or incomplete picture. Because models learn from the distribution of perspectives in their training data, they tend to reflect the dominant narratives present in that data. Topics where training data is skewed toward particular viewpoints, industries, or demographic groups will produce outputs that present those perspectives as though they are universal truths.
For nonprofits serving marginalized communities, this type of misinformation can reinforce the very systemic biases the organization is working to address. An AI helping with program design might consistently recommend approaches that worked in well-resourced contexts but are inappropriate for the communities actually being served. An AI generating communications might use framing or language that subtly reinforces deficit narratives about the populations a nonprofit supports. These distortions are harder to catch than outright fabrication because each individual output may seem reasonable; the bias only becomes apparent in aggregate.
The OWASP framework explicitly includes training data bias as a contributing factor to the Misinformation vulnerability. While bias is often discussed as a fairness concern, in the security context it represents a form of misinformation because it causes users to make decisions based on a distorted representation of reality. An AI that consistently underestimates the capabilities or needs of a particular community is generating misinformation just as surely as one that fabricates statistics, even if every individual statement it makes is technically defensible in isolation.
Why Traditional Quality Assurance Fails
Traditional software testing operates on a simple principle: given the same input, the system should produce the same output, and that output can be verified against a known correct answer. Language models break every part of this assumption. The same input can produce different outputs. There is often no single "correct" answer to check against. And the volume of possible outputs is effectively infinite, making exhaustive testing impossible.
Standard security tools are equally ineffective. Web application firewalls, code scanners, penetration testing frameworks, and intrusion detection systems are all designed to identify attacks from external actors. Misinformation requires no attacker. It is generated by the system itself as part of its normal operation. A perfectly secured AI application with robust input validation, encrypted data at rest and in transit, and comprehensive access controls will still hallucinate. Security testing that focuses exclusively on external threats leaves this vulnerability completely unaddressed.
Even RAG architectures, which are specifically designed to ground AI responses in verified information, do not eliminate misinformation. While retrieval-augmented generation significantly reduces hallucination rates by providing the model with relevant source material, the model can still misinterpret retrieved content, combine information from multiple sources incorrectly, generate statements that go beyond what the retrieved documents actually say, or ignore the retrieved content entirely when the model's internal patterns produce a more "confident" response. RAG is a powerful mitigation, not a cure.
This is why misinformation requires a fundamentally different defensive approach, one built on the assumption that the AI's output is unreliable by default rather than trusted until proven otherwise. A professional AI application security assessment evaluates not just whether the system is protected from external attacks, but whether the system's own outputs meet accuracy and reliability standards appropriate for the organization's use cases.
Who Is at Risk
Every organization using an LLM faces misinformation risk, but the severity varies dramatically based on the use case. The more consequential the decisions informed by AI output, the greater the potential harm from hallucinated content. Here are the AI application types with the highest exposure.
Client-Facing Chatbots and Assistants
AI systems that interact directly with clients, beneficiaries, or the public carry the highest misinformation risk. Users often have no way to verify the information provided and may take immediate action based on AI responses. A chatbot that provides incorrect eligibility information or fabricates program details can cause direct harm to the people the organization serves.
Organizations subject to data privacy regulations face additional risk, as AI-generated misinformation about data handling practices could create compliance violations.
Document Generation and Content Creation
AI used to draft grant applications, reports, policy briefs, compliance documents, or public communications can embed misinformation in materials that carry the organization's name and reputation. Unlike a chatbot conversation that disappears, published documents create a permanent record that can be scrutinized by funders, regulators, and the public.
The risk increases when multiple people review AI-generated content but each assumes someone else verified the factual claims.
Decision Support and Analysis
AI systems that summarize data, identify trends, or provide recommendations for organizational decisions can introduce misinformation into the strategic planning process. When leadership relies on AI-generated analysis to make budget allocations, program expansions, or staffing decisions, hallucinated data points or fabricated trend analyses can lead to resource misallocation.
This is particularly risky for AI agents that chain multiple AI calls together, as errors compound through each step of the analysis pipeline.
Automated Communications
AI-generated emails, newsletters, social media posts, and donor communications that go out under the organization's brand carry reputational risk when they contain inaccuracies. A fundraising email that cites an incorrect program statistic or a social media post that references a nonexistent research finding undermines donor trust and organizational credibility.
The volume and speed of automated communications make manual review of every piece of content increasingly difficult as organizations scale their AI usage.
Why Nonprofits Face Elevated Risk
Mission-driven organizations face a distinct set of factors that amplify misinformation risk beyond what commercial organizations typically encounter. Nonprofit staff often have deep program expertise but less familiarity with AI limitations, creating a knowledge gap where hallucinated content about their specific domain can be harder to spot. The populations nonprofits serve are frequently more vulnerable to the consequences of misinformation: incorrect eligibility guidance, fabricated program information, or wrong legal advice can have life-altering consequences for people already in crisis.
Additionally, nonprofits typically operate with smaller teams and tighter budgets, which creates pressure to adopt AI for efficiency gains without investing proportionally in verification processes. When an organization deploys AI to handle tasks that previously required a human expert, the expectation is often that the AI will perform at or near human accuracy. The reality is that without structured verification, the AI may produce output that no human expert would have generated, and the organization may lack the capacity to catch the errors before they cause harm.
Finally, the trust relationship between nonprofits and their stakeholders, including clients, donors, funders, and the community, is the foundation of organizational effectiveness. A commercial company that publishes AI-generated misinformation faces reputational damage. A nonprofit that provides misinformation to a vulnerable client faces a breach of the trust that makes its mission possible.
Defense Strategies: A Layered Approach
Because misinformation cannot be eliminated through a single intervention, effective defense requires multiple layers that work together to reduce both the frequency and impact of false AI outputs. The following framework progresses from foundational measures that every organization should implement to advanced strategies for high-stakes applications.
Layer 1: Retrieval-Augmented Generation and Knowledge Grounding
Ground AI responses in verified, organizational knowledge to reduce fabrication
The single most effective architectural defense against misinformation is grounding AI responses in verified, authoritative sources. Retrieval-Augmented Generation (RAG) connects the language model to a curated knowledge base containing your organization's approved policies, verified program information, and authoritative reference material. Instead of generating answers from its training data alone, the model retrieves relevant documents and uses them as the basis for its response. This dramatically reduces hallucination rates for topics covered by the knowledge base, though it does not eliminate them entirely.
- Build curated knowledge bases with verified, current information for every use case where your AI provides guidance or answers questions
- Implement source attribution so that every AI response includes references to the specific documents it used, enabling users to verify claims
- Establish regular knowledge base review cycles to ensure stored information remains current and accurate, particularly for regulatory and compliance content
- Configure the model to acknowledge when retrieved sources do not contain sufficient information to answer a query, rather than filling gaps with generated content
Layer 2: Output Validation and Verification Workflows
Establish systematic processes for checking AI outputs before they reach end users
Even with RAG in place, AI outputs require verification before being acted upon or shared externally. Output validation can be automated for certain types of checks and must involve human review for others. The key is building verification into the workflow so that it happens consistently, not just when someone happens to notice something suspicious.
- Implement automated fact-checking layers that cross-reference AI claims against authoritative databases, particularly for statistics, dates, legal citations, and regulatory references
- Require human review for all externally published AI-generated content, with reviewers specifically trained to check factual claims rather than just editing for style
- Use secondary AI models as cross-validators: have a second model independently verify key claims from the first, flagging discrepancies for human review
- Create verification checklists tailored to each use case, defining exactly which types of claims must be independently confirmed before the output is considered reliable
Layer 3: User Interface Design and Transparency
Design AI interactions that make uncertainty visible and encourage verification
How AI output is presented to users significantly affects whether misinformation causes harm. Interface design choices can either encourage blind trust or promote appropriate skepticism. The goal is not to make users distrust the AI entirely, which would defeat the purpose of deploying it, but to calibrate trust appropriately so that users verify claims where accuracy matters most.
- Clearly label all AI-generated content as such, using persistent visual indicators that cannot be missed or ignored by users
- Display confidence indicators or source attribution alongside AI responses, showing users where the information came from and how certain the system is
- Include disclaimers and limitations notices that are specific to the use case, not generic boilerplate that users learn to ignore
- Design easy-to-use feedback mechanisms where users can flag suspected inaccuracies, creating a continuous improvement loop for the organization's verification processes
Layer 4: Organizational Policies and Training
Build organizational capacity to recognize, prevent, and respond to AI-generated misinformation
Technical controls alone are insufficient without organizational policies that define how AI should be used, what level of verification is required for different types of output, and what happens when misinformation is discovered. Building a culture of appropriate AI skepticism, where staff understand both the capabilities and limitations of the tools they use, is the most durable defense against overreliance.
- Develop an AI acceptable use policy that categorizes organizational tasks by misinformation risk level and specifies the verification requirements for each category
- Train all AI users on the specific ways LLMs generate misinformation, using real examples relevant to the organization's work so staff can recognize hallucination patterns
- Establish an incident response process for when AI-generated misinformation is discovered after it has been acted upon or shared externally
- Create a regular audit process that samples AI outputs across use cases, verifying accuracy rates and identifying patterns in the types of misinformation the system produces
Common Mistakes Organizations Make
Even organizations that recognize the misinformation risk frequently make defensive mistakes that leave them more exposed than they realize. These are the patterns that professional AI security assessments consistently identify across organizations of all sizes.
Trusting Confidence as Accuracy
The most pervasive mistake is equating the confidence of an AI's language with the accuracy of its claims. When a model says "The program requires a household income below 200% of the federal poverty level" with the same tone it uses for every other statement, users naturally assume the specific threshold is correct. But the model's linguistic confidence is a feature of its text generation process, not a reflection of factual certainty. Organizations that do not train staff to understand this distinction will consistently accept hallucinated specifics, particularly numbers, dates, thresholds, and citations, because they "sound right."
Assuming RAG Eliminates Hallucination
Organizations that have implemented retrieval-augmented generation often believe they have "solved" the hallucination problem. While RAG significantly reduces fabrication for topics covered by the knowledge base, it does not eliminate it. The model can still misinterpret retrieved documents, generate responses that go beyond what the sources say, combine information from multiple sources incorrectly, or fall back on its training data when the retrieved content does not seem relevant enough. Treating RAG as a complete solution creates a false sense of security that leads organizations to deploy AI in high-stakes contexts without adequate verification.
Relying on Generic Disclaimers Instead of Specific Safeguards
Adding a disclaimer that says "AI-generated content may contain errors" and considering the problem addressed is a common but ineffective approach. Generic disclaimers become invisible to users within days of deployment. Effective misinformation defense requires specific, contextual safeguards: flagging outputs where confidence is low, requiring verification for specific categories of claims, and designing workflows that prevent unverified AI content from reaching critical decision points. A disclaimer without operational safeguards is a liability defense, not a safety measure.
Failing to Monitor Accuracy Over Time
Many organizations evaluate AI accuracy during initial deployment and then assume the system will continue performing at the same level. In practice, accuracy can degrade as the context changes: regulations update, programs evolve, organizational policies shift, and the knowledge base grows stale. Without ongoing monitoring that regularly samples AI outputs and verifies them against current authoritative sources, organizations have no way to detect accuracy degradation until a harmful error surfaces. By that point, the AI may have been providing incorrect information to users for weeks or months.
What a Professional Assessment Covers
A comprehensive AI application security assessment evaluates your organization's exposure to misinformation through systematic testing across multiple dimensions. Unlike general security audits that focus on external threats, a misinformation-focused assessment examines the trustworthiness of the AI's own outputs.
Hallucination Rate Testing
Systematic testing across your organization's specific use cases to measure how frequently the AI generates fabricated or incorrect information, with particular attention to the types of queries most relevant to your operations.
RAG Effectiveness Evaluation
Assessment of how effectively your retrieval-augmented generation system grounds responses in authoritative sources, including testing for scenarios where the model ignores or misinterprets retrieved content.
Overreliance Pattern Analysis
Evaluation of how your users interact with AI outputs, identifying workflows where unverified AI content reaches decision points, external communications, or client-facing touchpoints without appropriate review.
Verification Process Audit
Review of your organization's policies, workflows, and technical controls for verifying AI output, identifying gaps where misinformation could propagate without detection.
Bias and Distortion Detection
Testing for systematic biases in AI outputs that could produce misleading content about specific populations, programs, or topics relevant to your organizational mission.
Accuracy Monitoring Framework
Design and implementation of ongoing monitoring processes that track AI output accuracy over time, alerting your team when hallucination rates increase or new patterns of misinformation emerge.
A professional assessment helps organizations move beyond reactive responses to misinformation, where errors are caught after they cause harm, toward proactive prevention, where systematic controls reduce the likelihood and impact of false AI outputs before they reach users. For organizations deploying AI in contexts where accuracy directly affects people's lives, this shift from reactive to proactive is essential. Learn more about our comprehensive AI security assessment approach.
The OWASP Top 10 for LLM Applications: Full Series
This article is part of our comprehensive series covering every vulnerability in the OWASP Top 10 for LLM Applications. Each article provides a deep dive into a specific risk category with practical defenses for your organization.
Prompt Injection
Published: February 25, 2026
Sensitive Information Disclosure
Published: February 26, 2026
Supply Chain Vulnerabilities
Published: February 27, 2026
Data and Model Poisoning
Published: February 28, 2026
Insecure Output Handling
Published: March 1, 2026
Excessive Agency
Published: March 2, 2026
System Prompt Leakage
Published: March 3, 2026
Vector and Embedding Weaknesses
Published: March 4, 2026
Misinformation
You are here
Unbounded Consumption
Coming soon
Building Trust in an Era of AI Uncertainty
Misinformation sits at #9 in the OWASP Top 10 for LLM Applications, but in many ways it is the most pervasive vulnerability on the list. Every other risk requires some form of external exploit or system misconfiguration. Misinformation emerges from the fundamental architecture of language models themselves. Every organization using an LLM is exposed, every interaction carries some risk of hallucinated content, and the consequences scale with the importance of the decisions being informed by AI output.
The defenses outlined in this article follow a clear logic: ground AI responses in verified knowledge through RAG, validate outputs through systematic verification workflows, design interfaces that make uncertainty visible to users, and build organizational policies that prevent unverified AI content from reaching critical touchpoints. No single layer eliminates the risk, but together they reduce both the frequency and impact of misinformation to manageable levels. The goal is not to make AI perfectly accurate, which is currently impossible, but to ensure that when the AI is wrong, the organization catches it before it causes harm.
For nonprofits, the stakes extend beyond organizational reputation. When an AI provides incorrect guidance to a client in crisis, fabricates statistics in a grant application, or generates misleading compliance advice, the consequences fall on the people and communities the organization exists to serve. Building robust defenses against misinformation is not just a technical best practice; it is a matter of mission integrity. Organizations that invest in verification processes, staff training, and ongoing monitoring demonstrate to their stakeholders that they take the responsibility of AI deployment as seriously as they take every other aspect of their service delivery.
If your organization deploys AI in any capacity where the accuracy of its output matters, a professional AI application security assessment can systematically evaluate your exposure to misinformation, test your verification processes, measure hallucination rates across your specific use cases, and recommend targeted improvements. The trust your stakeholders place in your organization depends on the reliability of the information you provide, and in an AI-augmented environment, that reliability requires deliberate, layered defense.
Can You Trust What Your AI Tells People?
Misinformation is the #9 risk in the OWASP Top 10 for LLM Applications. Hallucinated facts, fabricated citations, and confidently wrong guidance erode trust and harm the communities you serve. Our AI Application Security assessments test hallucination rates, verify RAG effectiveness, and evaluate your verification workflows across every use case.
Start with a free consultation to assess your AI application's accuracy and identify where misinformation could reach your users.