Beyond the Hype: Real-World AI Security Concerns Emerge

Author: Denis Avetisyan

A new analysis of developer forums reveals a disconnect between theoretical AI risks and the practical security challenges faced in building and deploying AI-powered projects.

The escalating volume of AI security reports over time underscores a growing awareness - and likely incidence - of vulnerabilities as the technology matures and its deployment expands. — The escalating volume of AI security reports over time underscores a growing awareness – and likely incidence – of vulnerabilities as the technology matures and its deployment expands.

Research identifies prevalent vulnerabilities and solutions discussed by developers on platforms like Hugging Face and GitHub, emphasizing the need for improved AI supply chain security practices and data provenance.

Despite the increasing focus on potential vulnerabilities, a comprehensive understanding of real-world security challenges in AI systems remains surprisingly limited. This research, ‘Securing the AI Supply Chain: What Can We Learn From Developer-Reported Security Issues and Solutions of AI Projects?’, addresses this gap by analyzing over 312,000 developer discussions from platforms like Hugging Face and GitHub. Our analysis reveals a nuanced taxonomy of 32 security issues and 24 solutions, clustered around system software, external tools, model integrity, and data provenance-highlighting a persistent disconnect between identified risks and practical remediation strategies. Ultimately, how can we translate these developer-identified concerns into robust, proactive security measures across the entire AI supply chain?

Unmasking the Illusion: The Hallucinations of Language Models

Despite their impressive capacity to generate human-quality text, Large Language Models frequently exhibit a phenomenon known as “hallucination,” wherein they confidently produce statements disconnected from established facts. This isn’t a matter of simple error, but rather a core limitation arising from the models’ predictive nature; they excel at statistically plausible text generation, prioritizing coherence over truthfulness. Essentially, these models learn patterns and relationships within vast datasets, allowing them to simulate understanding without possessing genuine knowledge or a mechanism for verifying the accuracy of their outputs. Consequently, a seemingly authoritative response can be entirely fabricated, presenting misinformation with the same conviction as a factually correct statement, and posing significant challenges to the reliable application of this technology.

Large language models, despite their impressive capabilities, are fundamentally limited by their internal architecture. These models don’t “know” facts in the way a human does; instead, they operate by identifying statistical relationships within the vast datasets they were trained on. This means their responses are generated based on patterns learned from these internal parameters, rather than a grounding in external, verifiable knowledge. Consequently, the models can struggle to differentiate between plausible-sounding statements and actual truths, leading to outputs that, while grammatically correct and contextually relevant, are factually inaccurate. The absence of a robust mechanism for integrating and validating information from external sources – like knowledge graphs or real-time databases – restricts their ability to ensure the reliability of the generated text, creating a significant challenge for applications requiring factual precision.

The potential for large language models to disseminate misinformation poses a significant threat to trust and reliability in information ecosystems. These models, trained to predict and generate human-like text, can present confidently stated falsehoods as factual information, largely because they lack a mechanism for verifying their outputs against external sources. This isn’t simply a matter of occasional errors; the models can fabricate details, misrepresent events, and even construct entirely plausible but untrue narratives. Consequently, users may struggle to distinguish between genuine knowledge and convincingly presented fiction, eroding confidence in the information they encounter and potentially leading to real-world consequences based on inaccurate data. The absence of built-in validation mechanisms highlights a critical vulnerability, demanding innovative solutions to ensure these powerful tools are used responsibly and do not become vectors for widespread deception.

Hugging Face models originate from diverse sources, with associated security vulnerabilities varying based on origin type.

Bridging the Knowledge Gap: The Retrieval-Augmented Generation Approach

Retrieval-Augmented Generation (RAG) addresses the limitations of Large Language Models (LLMs) by allowing them to leverage information from external knowledge sources. LLMs, while powerful, are constrained by the data they were initially trained on and lack access to current or specialized information. RAG systems integrate a retrieval component that accesses and identifies relevant documents or data points from these external sources – which can include databases, knowledge graphs, or web pages – and provides this information as context to the LLM during the generation process. This enables the LLM to base its responses on a broader and more up-to-date knowledge base than its internal parameters alone would allow.

The retrieval process in Retrieval-Augmented Generation (RAG) typically involves formulating a query based on the user’s input and utilizing this query to search a vector database or other knowledge source for semantically similar documents or passages. These retrieved results are then incorporated into the prompt provided to the Large Language Model (LLM). This can be achieved through various methods, including concatenating the retrieved content directly into the prompt, or by using the retrieved information to re-rank or filter potential responses generated by the LLM. The LLM then leverages this augmented prompt to generate a response grounded in both its pre-trained knowledge and the retrieved external information.

The integration of external knowledge sources in Retrieval-Augmented Generation (RAG) systems functions as a mitigation strategy against the phenomenon of ‘hallucination’ in Large Language Models (LLMs). LLMs, trained on vast datasets, may generate outputs that are factually incorrect or not supported by evidence; grounding the model in retrieved, verifiable data reduces the likelihood of these unsupported assertions. By providing the LLM with relevant context prior to generation, RAG ensures responses are based on documented information, increasing the overall factual accuracy and, consequently, the trustworthiness of the model’s outputs. This approach shifts the reliance from the LLM’s parametric knowledge – potentially containing inaccuracies – to the demonstrably accurate content of the external knowledge source.

Analysis of AI domains reveals that the top five tasks with potential security vulnerabilities are concentrated in areas like automatic speech recognition (<span class="katex-eq" data-katex-display="false">asr</span>) and reinforcement learning (<span class="katex-eq" data-katex-display="false">rl</span>). — Analysis of AI domains reveals that the top five tasks with potential security vulnerabilities are concentrated in areas like automatic speech recognition ( $asr$ ) and reinforcement learning ( $rl$ ).

Deconstructing the Pipeline: From Data to Contextualized Generation

Retrieval-Augmented Generation (RAG) pipelines begin by converting source data, referred to as ‘Knowledge Sources’, into a numerical format using ‘Embedding Models’. These models utilize machine learning to map textual information into high-dimensional vectors, capturing semantic meaning. The resulting vector representations allow for efficient similarity comparisons and are stored within ‘Vector Databases’. These specialized databases are designed for quick searches based on vector proximity, enabling the system to identify knowledge fragments most relevant to a given user query. The quality of the embedding model directly impacts the accuracy and relevance of the retrieved information, influencing the overall performance of the RAG system.

Vector databases facilitate efficient information retrieval by leveraging the principles of vector similarity search. User queries are also transformed into vector embeddings using the same embedding model applied to the knowledge sources. The database then identifies knowledge vectors with the highest cosine similarity to the query vector, effectively pinpointing the most relevant information. This process avoids the need for keyword matching and allows for semantic search, returning results based on meaning rather than exact terms. Indexing techniques, such as Hierarchical Navigable Small World (HNSW) graphs or Approximate Nearest Neighbor (ANN) algorithms, are employed to accelerate the search process, enabling rapid retrieval even from massive datasets. The resulting vectors are ranked by their similarity scores, providing a prioritized list of relevant knowledge to be used for context augmentation.

Following retrieval from the vector database, relevant knowledge is incorporated into the input provided to the Large Language Model’s generative component. This process isn’t simply concatenation; effective ‘Prompt Engineering’ is crucial. Specifically, the retrieved context is structured within the prompt to guide the model’s generation process, indicating that the provided information should be used as the basis for its response. This involves crafting the prompt to clearly delineate the context from the user’s query and instructing the model to prioritize information within the provided context. Without careful prompt construction, the model may disregard the retrieved knowledge or improperly integrate it, leading to outputs that are irrelevant or factually incorrect. The quality of prompt engineering directly impacts the accuracy and coherence of the final generated text.

This example demonstrates the coding process and theme synthesis used to address both research questions RQ2 and RQ3.

Measuring the Signal: Faithfulness and Relevance in RAG Performance

Evaluating the effectiveness of Retrieval-Augmented Generation (RAG) systems hinges on two core principles: faithfulness and context relevance. Faithfulness assesses whether the information presented in a generated response is genuinely supported by the retrieved source documents, preventing the system from fabricating or misrepresenting facts. Context relevance, conversely, measures how well the retrieved documents actually address the user’s query; a system might be faithful in citing a source, but irrelevant if the source doesn’t pertain to the question at hand. These metrics are crucial because a high-performing RAG system isn’t simply about retrieving something – it’s about retrieving and utilizing information that is both accurate and directly applicable to the user’s need, ensuring the generated output is trustworthy and insightful.

Rigorous evaluation of Retrieval-Augmented Generation (RAG) systems demands the use of quantifiable metrics to ensure objectivity. A recent analysis of 312,868 developer discussions sourced from platforms like Hugging Face and GitHub exemplifies this approach. Researchers employed a distilBERT classifier, achieving a Matthews Correlation Coefficient of 0.79 in identifying security-focused conversations within the dataset. This automated assessment not only demonstrated the feasibility of large-scale RAG evaluation but also revealed specific patterns within the data – identifying 32 distinct issue codes and 24 associated solution codes – highlighting how these metrics can move beyond simple accuracy scores to provide nuanced insights into system performance and the nature of the information being retrieved.

The evaluation process benefitted from a distilBERT classifier, a machine learning model demonstrating strong performance with a Matthews Correlation Coefficient (MCC) of 0.79 in pinpointing security-related discussions within a large dataset. This achievement suggests a viable path toward automating the assessment of Retrieval-Augmented Generation (RAG) systems, reducing reliance on manual review. Complementing this automated identification, a thematic analysis uncovered 32 distinct issue codes and 24 corresponding solution codes prevalent in the discussions, offering a granular understanding of the specific security challenges and potential remedies being actively debated by developers. The combination of automated classification and detailed thematic coding provides a robust framework for evaluating the faithfulness and relevance of RAG outputs in the context of real-world security concerns.

Analysis of developer discussions revealed a striking disconnect between identified software vulnerabilities and their formal documentation; less than 0.1% of reported issues were linked to Common Vulnerabilities and Exposures (CVE) identifiers. This suggests a substantial underreporting of practical security concerns, indicating that a vast majority of vulnerabilities discussed within developer communities do not propagate to official databases. The findings highlight a critical gap in the current security ecosystem, where real-world experiences and potential threats aren’t consistently translated into standardized, publicly available information, potentially leaving systems vulnerable and hindering proactive security measures. This disparity underscores the need for improved mechanisms to bridge the gap between informal issue reporting and formal vulnerability disclosure.

The research delves into the practical realities of AI security, revealing a disconnect between anticipated threats and those developers actually confront. This echoes Claude Shannon’s sentiment: “Communication is the process of conveying meaning between entities using some shared medium.” In the context of the AI supply chain, ‘communication’ isn’t simply data transfer, but the sharing of vulnerability insights and solutions. The study’s focus on developer-reported issues – the shared medium of practical experience – highlights the importance of robust channels for disseminating knowledge about threats and mitigation strategies, ultimately strengthening the entire system against attack. The thematic analysis, by identifying patterns in these developer discussions, acts as a critical form of ‘noise reduction’-distilling actionable intelligence from the complex signal of real-world AI development.

What’s Next?

The analysis reveals a predictable asymmetry: discussions of security often lag significantly behind the introduction of vulnerabilities. This isn’t negligence, but a fundamental characteristic of complex systems. The real threat isn’t malicious attack, but the relentless pressure to build – to push boundaries before fully mapping the consequence space. Future work must move beyond cataloging risks and towards predictive modeling of failure modes, focusing on the points of maximal leverage within the AI supply chain. Identifying these points isn’t about creating stronger defenses, but about understanding the inherent fragility of the whole construct.

A critical limitation lies in the data itself. Developer forums, while rich, represent a biased sample-those actively solving problems, not necessarily those encountering them silently. Truly comprehensive understanding demands passive monitoring of a broader range of projects, coupled with methods for inferring vulnerability even from the absence of reported issues. The challenge isn’t merely detecting breaches, but quantifying the undetected breaches.

Ultimately, the best hack is understanding why it worked. Every patch is a philosophical confession of imperfection. The field needs to shift from a mindset of ‘security as feature’ to ‘security as inherent limitation’. The goal isn’t to eliminate risk, but to map the contours of acceptable failure, and to design systems that fail gracefully – or, preferably, that reveal their flaws before they’re exploited.

Original article: https://arxiv.org/pdf/2512.23385.pdf

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

Unmasking the Illusion: The Hallucinations of Language Models

Bridging the Knowledge Gap: The Retrieval-Augmented Generation Approach

Deconstructing the Pipeline: From Data to Contextualized Generation

Measuring the Signal: Faithfulness and Relevance in RAG Performance

What’s Next?

See also: