When AI Gets Facts Wrong: A New Stress Test for High-Stakes Systems

Author: Denis Avetisyan

Researchers have created a challenging benchmark to expose how easily large language models can produce incorrect information in critical fields like healthcare, finance, and law.

A deliberately crafted adversarial prompt-combining assertive phrasing, subtly corrupted information, and the initial query-demonstrates how even robust systems can be led astray, highlighting the fragility of knowledge integration in complex architectures.

AdversaRiskQA assesses factual robustness under adversarial conditions, revealing vulnerabilities in large language models across high-risk domains.

Despite advances in large language models (LLMs), ensuring factual consistency-particularly when confronted with misinformation-remains a critical challenge, especially in high-stakes domains. To address this, we introduce AdversaRiskQA: An Adversarial Factuality Benchmark for High-Risk Domains, a novel benchmark designed to systematically evaluate LLM robustness against deliberately injected factual errors across Health, Finance, and Law. Our findings reveal performance scaling with model size and domain specificity, with injected misinformation showing limited impact on long-form factual output, but highlight persistent vulnerabilities requiring further investigation. How can we best leverage such benchmarks to build more reliable and trustworthy LLMs for critical applications?

The Inevitable Mirage: LLMs and the Construction of Falsehood

Large Language Models (LLMs), despite their impressive ability to generate human-quality text, frequently produce statements that appear factual but are not supported by their training data – a phenomenon known as hallucination. This isn’t simply random error; LLMs are designed to predict the most probable continuation of a given text, and this predictive process can lead them to confidently assert information that is entirely fabricated or based on spurious correlations within the vast datasets they’ve processed. While seemingly innocuous in casual conversation, these hallucinations present a substantial challenge, particularly when LLMs are applied to domains requiring precision, such as legal advice, medical diagnosis, or scientific research, where the presentation of false information could have serious consequences. The models don’t “know” what is true; they skillfully construct plausible-sounding text, making the identification and mitigation of these unsupported statements a critical area of ongoing investigation.

The potential for Large Language Models to fabricate information, termed “hallucinations,” introduces substantial risks, especially within critical applications. In fields like healthcare, legal advice, or financial forecasting, even seemingly minor inaccuracies can have severe consequences, leading to misdiagnosis, flawed legal strategies, or poor investment decisions. This is not merely a matter of occasional errors; the plausibility of these fabricated statements often makes them difficult to detect, increasing the chance of uncritical acceptance. Consequently, reliance on LLM outputs in sensitive domains demands rigorous verification processes and a cautious approach to automation, as the cost of a hallucination far outweighs the convenience of rapid information access.

Responsible deployment of Large Language Models necessitates rigorous evaluation and mitigation of the risks associated with their propensity to generate hallucinatory content. Addressing these inaccuracies isn’t simply a matter of improving performance metrics; it demands a proactive approach focused on identifying scenarios where unsupported statements are likely to occur and developing strategies to minimize them. This includes refining training data, implementing fact-checking mechanisms, and designing systems that can signal uncertainty or abstain from answering when lacking sufficient evidence. Without such measures, the potential for misinformation and flawed decision-making – particularly in critical applications like healthcare, finance, and legal counsel – remains substantial, hindering the widespread and trustworthy adoption of this powerful technology.

Despite the rapid growth in size and complexity of Large Language Models, the propensity to generate factually incorrect or nonsensical outputs – often termed “hallucinations” – does not diminish with scale alone. Simply increasing the number of parameters doesn’t inherently instill truthfulness; rather, it can amplify existing biases or create new avenues for error. Consequently, a shift toward systematic evaluation is paramount. This necessitates the development of robust benchmarks and testing methodologies that move beyond superficial assessments and delve into the model’s reasoning processes, knowledge consistency, and ability to discern reliable information. Such evaluations must encompass diverse datasets and challenging scenarios to accurately gauge the risk of hallucinations and inform strategies for mitigation, ensuring these powerful tools are deployed responsibly and with appropriate safeguards.

This evaluation process highlights how models successfully navigate or fail to address adversarial prompts within the health domain.

The Illusion of Robustness: Exposing LLM Vulnerabilities

Large language models (LLMs) exhibit vulnerability to adversarial factuality attacks, wherein intentionally false or misleading information is introduced within prompts or input data. This manipulation can induce the model to generate responses containing fabricated statements or “hallucinations” presented as factual information. The success of these attacks doesn’t necessarily rely on semantic similarity to truthful content; even subtly inaccurate premises can be sufficient to trigger erroneous outputs. This susceptibility arises from the LLM’s reliance on statistical patterns in training data rather than a grounding in verifiable facts, making them prone to propagating falsehoods when presented with deceptive input.

Current LLM evaluation benchmarks typically utilize datasets composed of factual statements designed to assess knowledge recall. However, these benchmarks often lack the deliberate inclusion of subtly incorrect or misleading information, failing to adequately probe an LLM’s susceptibility to adversarial factuality attacks. Consequently, performance metrics derived from these standard evaluations can be inflated, presenting an overly optimistic view of an LLM’s reliability and its ability to discern truth from falsehood when confronted with compromised inputs. This discrepancy between benchmark performance and real-world robustness necessitates the development of specialized testing methodologies that explicitly target this vulnerability.

A robust testing strategy for Large Language Models (LLMs) necessitates the proactive use of adversarial techniques. This involves deliberately introducing misleading or subtly incorrect information into input prompts to assess the model’s susceptibility to generating factually inconsistent or hallucinatory outputs. Such techniques extend beyond standard benchmark datasets, which often lack the nuanced falsehoods required to effectively challenge model robustness. Testing should encompass a variety of adversarial methods, including the injection of plausible but false statements, contradictions within the prompt, and subtly altered facts, to comprehensively evaluate the LLM’s ability to discern truthfulness and maintain factual consistency under duress. The goal is to identify failure points and quantify the model’s resilience to intentionally deceptive inputs, thereby improving its reliability in real-world applications.

Evaluating Large Language Models (LLMs) with intentionally misleading or adversarial inputs is critical for assessing their robustness and identifying potential failure points. This type of testing moves beyond standard benchmark datasets, which often present clean and unambiguous data, to simulate real-world scenarios where inaccuracies or subtly false information may be present. By systematically challenging LLMs with such inputs, developers can pinpoint specific vulnerabilities, understand the conditions under which hallucinations or incorrect outputs occur, and quantify the model’s susceptibility to manipulation. This process allows for targeted improvements in model architecture, training data curation, and the implementation of defensive mechanisms to enhance overall reliability and trustworthiness.

An automated three-stage pipeline efficiently generates and validates a dataset of adversarial prompts.

AdversaRiskQA: A Stress Test for High-Stakes Intelligence

AdversaRiskQA is a newly developed benchmark dataset created to assess the performance of Large Language Models (LLMs) when presented with intentionally false or misleading information. The dataset focuses on high-risk domains – specifically health, finance, and law – where inaccurate responses can have significant consequences. It comprises prompts engineered to include factual inaccuracies, challenging LLMs to identify and avoid perpetuating false claims. The primary goal of AdversaRiskQA is to provide a standardized method for evaluating an LLM’s robustness against adversarial factuality attacks and to quantify its ability to provide reliable information in sensitive application areas.

AdversaRiskQA employs a methodology of adversarial prompting, constructing test cases that incorporate demonstrably false statements. These prompts are designed to specifically challenge Large Language Models (LLMs) by requiring them to either identify the inaccuracies or, critically, to propagate them in their responses. The inclusion of false premises tests an LLM’s ability to perform fact-checking and resist generating outputs based on incorrect information, thereby exposing potential vulnerabilities in its knowledge retrieval and reasoning processes. This approach differs from standard question answering by actively introducing misinformation to assess robustness rather than simply evaluating performance on verified facts.

AdversaRiskQA evaluates Large Language Models (LLMs) by presenting prompts intentionally containing factual inaccuracies. The benchmark measures the extent to which models can identify and reject false premises before generating responses, thereby assessing their ability to avoid propagating misinformation. Specifically, the evaluation focuses on whether the LLM will confidently affirm a false statement embedded within the prompt, or if it will correctly identify the inaccuracy and provide a truthful and relevant response. Performance is quantified by the rate at which models successfully discern fact from fiction and refrain from generating outputs based on the false information presented, providing a metric for assessing the reliability and trustworthiness of LLMs in high-stakes applications.

Evaluation of Large Language Models on the AdversaRiskQA benchmark indicates an average accuracy exceeding 70%, however, performance degrades when models are required to identify and correct false premises within prompts. Specifically, LLMs exhibit weaknesses in domain-specific reasoning tasks, even with high overall accuracy scores. Analysis of model performance revealed that Qwen3-Next-80B, despite its substantial 80 billion parameter size, demonstrated the highest number of failures, suggesting that increased model scale does not inherently correlate with improved robustness against adversarial factuality challenges or guarantee reliable performance in high-risk domains.

The Mirage Persists: Automated Evaluation and the Pursuit of Ground Truth

Traditional evaluation metrics, such as BLEU and ROUGE, were designed to assess text similarity and fluency, not factual correctness. These metrics operate on surface-level lexical overlap and fail to identify instances where generated text contradicts established knowledge or contains fabricated information. Consequently, high scores on these metrics do not guarantee factual accuracy. This limitation has driven the development of automated evaluation methods that directly address factuality by employing techniques like knowledge retrieval, external knowledge base comparison, and, increasingly, the use of large language models (LLMs) as evaluators to assess the truthfulness of generated statements, requiring a shift towards methods capable of verifying claims against verifiable sources.

LLM-as-Judge automates factuality assessment by utilizing a separate Large Language Model (LLM) to evaluate the claims made in generated text. This approach circumvents the limitations of traditional metrics like ROUGE or BLEU, which primarily focus on surface-level similarity and do not assess truthfulness. The process typically involves presenting the LLM-judge with both the generated text and a source document, or a set of relevant context, and prompting it to determine if the generated claims are supported by the provided evidence. This method offers scalability because the LLM-judge can process large volumes of text without requiring manual annotation. Performance is commonly evaluated using metrics like agreement with human annotations or correlation with established fact verification datasets, and the LLM-judge’s output can be further refined through techniques like ensemble methods or calibration.

Search-Augmented Factuality Evaluation (SAFE) enhances automated evaluation by combining the reasoning capabilities of Large Language Models (LLMs) with external knowledge retrieval. The process typically involves decomposing a generated claim into atomic factual statements. These statements are then used as search queries against a web search engine, retrieving supporting evidence from online sources. The LLM then assesses the consistency between the original claim and the retrieved evidence. This approach allows for verification of claims against a broader knowledge base than the LLM’s internal parameters, improving accuracy in identifying factual errors and unsupported statements. The retrieved context is used to both support correct claims and refute incorrect ones, providing a more robust assessment of generated text than relying solely on the LLM’s pre-trained knowledge.

F1@K is a metric used in evaluating factuality that moves beyond simple accuracy by considering both precision and recall. Traditional accuracy scores only indicate if a generated statement aligns with a reference, but F1@K assesses the proportion of relevant factual statements that are correctly identified (recall) and the proportion of identified statements that are actually factual (precision). The “@K” notation signifies that the metric evaluates the top K retrieved or generated statements. Specifically, F1@K is the harmonic mean of precision and recall calculated at rank K: $F1@K = 2 <i> (Precision@K </i> Recall@K) / (Precision@K + Recall@K)$ . This provides a more nuanced assessment, especially in scenarios with multiple potential factual statements, as it penalizes systems that either miss relevant facts (low recall) or include irrelevant or incorrect information (low precision).

The Inevitable Convergence: Towards Truly Reliable Artificial Intelligence

The reliable implementation of large language models hinges on proactively identifying and addressing potential weaknesses through rigorous adversarial evaluation. This process involves subjecting models to carefully crafted inputs designed to expose vulnerabilities – such as susceptibility to misleading prompts, factual inaccuracies, or biased responses – before they are deployed in real-world applications. By simulating challenging and unexpected scenarios, researchers can pinpoint specific failure modes and implement targeted mitigation strategies, ranging from refined training data and architectural improvements to the development of robust defense mechanisms. This preemptive approach is crucial, as vulnerabilities discovered post-deployment can have significant consequences, eroding user trust and potentially leading to harmful outcomes; therefore, continuous and comprehensive adversarial testing forms a cornerstone of responsible AI development.

The development of standardized benchmarks, such as AdversaRiskQA, represents a crucial step towards ensuring the reliability of large language models (LLMs). These frameworks move beyond simple accuracy metrics by systematically evaluating LLM performance under intentionally challenging, or ‘adversarial’, conditions. AdversaRiskQA, specifically, assesses a model’s susceptibility to misleading prompts and its ability to maintain factual consistency across diverse domains. By providing a common ground for evaluation, benchmarks allow researchers and developers to rigorously compare different models, pinpoint vulnerabilities, and track progress in building more robust AI systems. This standardized approach fosters transparency and accelerates the refinement of LLMs, ultimately paving the way for their safe and dependable deployment in real-world applications.

The dynamic nature of large language models necessitates evaluation techniques that extend beyond one-time assessments. Automated evaluation methods offer a pathway to continuous monitoring, allowing developers to track performance shifts as models are updated or exposed to new data. These systems employ diverse strategies, from generating adversarial examples to assessing consistency across multiple responses, providing a granular view of model strengths and weaknesses. Importantly, automation facilitates rapid iteration; identifying performance regressions or vulnerabilities quickly allows for targeted retraining or fine-tuning, ensuring LLMs remain reliable and aligned with intended behavior over time. This ongoing assessment isn’t merely about detecting errors, but about proactively building more robust and trustworthy AI systems capable of adapting to evolving real-world demands.

Recent evaluations utilizing the AdversaRiskQA benchmark reveal significant performance disparities among large language models, with GPT-5 currently exhibiting the most robust capabilities. However, even this leading model demonstrates varying degrees of accuracy depending on the domain of questioning; notably, performance is substantially higher when assessing financial queries compared to those related to healthcare or legal matters. This inconsistency underscores the inherent challenges in achieving generalized intelligence and suggests that LLMs, despite their advancements, struggle to consistently apply knowledge and reasoning across diverse subject areas. The domain-specific accuracy gaps indicate a need for tailored training data and evaluation metrics to improve reliability, particularly in sensitive fields where factual precision is critical.

The reliable deployment of large language models hinges critically on their ability to consistently provide factually accurate information. Establishing this fidelity isn’t merely about avoiding errors; it’s foundational to fostering genuine trust in these increasingly powerful AI systems. Without a demonstrable commitment to truthfulness, widespread adoption across sensitive domains – from healthcare and legal advice to financial planning – remains untenable. The potential benefits of LLMs, including enhanced decision-making, accelerated research, and personalized services, are only fully realized when users can confidently rely on the information they provide. Therefore, ongoing research and development efforts are heavily focused on techniques for verifying knowledge, mitigating hallucinations, and ensuring that LLMs present information grounded in evidence, ultimately unlocking their transformative potential while safeguarding against misinformation and its associated risks.

The pursuit of flawlessly factual large language models feels less like engineering and more like tending a garden riddled with unseen thorns. This work, introducing AdversaRiskQA, doesn’t merely expose vulnerabilities in high-risk domains-health, finance, law-it confirms a fundamental truth: systems aren’t built, they evolve, and every architectural choice is a prophecy of future failure. As Barbara Liskov observed, “It’s one of the most difficult things as a computer scientist to program something that will work ten years from now.” The adversarial nature of this benchmark highlights how quickly even the most promising models succumb to subtle manipulations, a testament to the inevitable chaos inherent in complex systems. Order, in this context, is simply a temporary cache between failures, a fleeting illusion of control.

What Lies Ahead?

The introduction of AdversaRiskQA is not a destination, but a charting of the currents. It reveals, predictably, that these large language models, when pressed against the shoals of high-stakes domains, exhibit the same failings as all systems built on brittle certainty. Every question answered correctly is merely a postponement of inevitable error; every adversarial example, a gentle reminder that fluency is not understanding. The benchmark itself will age, its challenges overcome, its limitations exposed – as all benchmarks must.

The true work lies not in chasing ever-higher scores on contrived tests, but in accepting the inherent fragility of these constructed intelligences. Future efforts should not focus solely on ‘fixing’ hallucination, but on understanding why these models insist on inventing realities. Perhaps the goal isn’t perfect factuality, but graceful degradation – a system that acknowledges its ignorance, rather than confidently proclaiming falsehoods.

One suspects that the pursuit of ‘reliable’ AI in these domains is a fool’s errand, akin to building a dam against the tide. The system will leak. The challenge, then, is not to prevent the leaks, but to design for them – to build systems that can contain the damage, and learn from the inevitable failures. Every refactor begins as a prayer and ends in repentance; this, it seems, is the natural order.

Original article: https://arxiv.org/pdf/2601.15511.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/