Promises and challenges of generative artificial intelligence for human learning

Nature Human Behaviour, Год журнала: 2024, Номер 8(10), С. 1839 - 1850

Опубликована: Окт. 22, 2024

Язык: Английский

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Foundations of Large Language Models in Software Vulnerability Detection

Advances in information security, privacy, and ethics book series, Год журнала: 2024, Номер unknown, С. 41 - 74

Опубликована: Окт. 18, 2024

This chapter explores the foundational aspects of large language models (LLMs) and their application in detecting software vulnerabilities. As complexity systems grows, traditional methods vulnerability detection are often insufficient. LLMs, with advanced natural processing capabilities, provide a novel approach to identifying potential security threats codebases. The delves into architecture these models, training mechanisms, challenges they face domain cybersecurity. Additionally, it discusses ethical implications future directions for integrating LLMs automated detection.

Язык: Английский

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Harnessing the Power of Large Language Models for Cybersecurity

Advances in information security, privacy, and ethics book series, Год журнала: 2024, Номер unknown, С. 1 - 40

Опубликована: Окт. 18, 2024

The LLMs not only have changed the overall nature of NPL but also helped a lot in setting standards cyber security. Within confines this review, authors discuss benefits, progressions, difficulties, as well future paths aimed to be taken cybersecurity field LLMs. They delve into how help companies process unstructured textual data for text dangers detections, vulnerability assessments, and incident responses. In addition, they investigate ethical societal consequences using cybersecurity, facing challenges like algorithmic bias, privacy, safety. Besides that, find that critical research questions crossroads language include unique assessing techniques improvement algorithms clarify information. Through development many-faceted interdisciplinary cooperation ethics-based considerations, we can maximize opportunities present world build more resilient secure environment everyone.

Язык: Английский

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Security and Privacy Challenges of Large Language Models: A Survey

ACM Computing Surveys, Год журнала: 2025, Номер unknown

Опубликована: Янв. 13, 2025

Large language models (LLMs) have demonstrated extraordinary capabilities and contributed to multiple fields, such as generating summarizing text, translation, question-answering. Nowadays, LLMs become very popular tools in natural processing (NLP) tasks, with the capability analyze complicated linguistic patterns provide relevant responses depending on context. While offering significant advantages, these are also vulnerable security privacy attacks, jailbreaking data poisoning personally identifiable information (PII) leakage attacks. This survey provides a thorough review of challenges LLMs, along application-based risks various domains, transportation, education, healthcare. We assess extent LLM vulnerabilities, investigate emerging attacks against potential defense mechanisms. Additionally, outlines existing research gaps highlights future directions.

Язык: Английский

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From Vulnerability to Defense: The Role of Large Language Models in Enhancing Cybersecurity

Computation, Год журнала: 2025, Номер 13(2), С. 30 - 30

Опубликована: Янв. 29, 2025

The escalating complexity of cyber threats, coupled with the rapid evolution digital landscapes, poses significant challenges to traditional cybersecurity mechanisms. This review explores transformative role LLMs in addressing critical cybersecurity. With landscapes and increasing sophistication security mechanisms often fall short detecting, mitigating, responding complex risks. LLMs, such as GPT, BERT, PaLM, demonstrate unparalleled capabilities natural language processing, enabling them parse vast datasets, identify vulnerabilities, automate threat detection. Their applications extend phishing detection, malware analysis, drafting policies, even incident response. By leveraging advanced features like context awareness real-time adaptability, enhance organizational resilience against cyberattacks while also facilitating more informed decision-making. However, deploying is not without challenges, including issues interpretability, scalability, ethical concerns, susceptibility adversarial attacks. critically examines foundational elements, real-world applications, limitations highlighting key advancements their integration into frameworks. Through detailed analysis case studies, this paper identifies emerging trends proposes future research directions, improving robustness, privacy automating management. study concludes by emphasizing potential redefine cybersecurity, driving innovation enhancing ecosystems.

Язык: Английский

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Current applications and challenges in large language models for patient care: a systematic review

Communications Medicine, Год журнала: 2025, Номер 5(1)

Опубликована: Янв. 21, 2025

Abstract Background The introduction of large language models (LLMs) into clinical practice promises to improve patient education and empowerment, thereby personalizing medical care broadening access knowledge. Despite the popularity LLMs, there is a significant gap in systematized information on their use care. Therefore, this systematic review aims synthesize current applications limitations LLMs Methods We systematically searched 5 databases for qualitative, quantitative, mixed methods articles published between 2022 2023. From 4349 initial records, 89 studies across 29 specialties were included. Quality assessment was performed using Mixed Appraisal Tool 2018. A data-driven convergent synthesis approach applied thematic syntheses LLM free line-by-line coding Dedoose. Results show that most investigate Generative Pre-trained Transformers (GPT)-3.5 (53.2%, n = 66 124 different examined) GPT-4 (26.6%, 33/124) answering questions, followed by generation, including text summarization or translation, documentation. Our analysis delineates two primary domains limitations: design output. Design include 6 second-order 12 third-order codes, such as lack domain optimization, data transparency, accessibility issues, while output 9 32 example, non-reproducibility, non-comprehensiveness, incorrectness, unsafety, bias. Conclusions This maps care, providing foundational framework taxonomy implementation evaluation healthcare settings.

Язык: Английский

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Large Language Models for Chatbot Health Advice Studies

JAMA Network Open, Год журнала: 2025, Номер 8(2), С. e2457879 - e2457879

Опубликована: Фев. 4, 2025

Importance There is much interest in the clinical integration of large language models (LLMs) health care. Many studies have assessed ability LLMs to provide advice, but quality their reporting uncertain. Objective To perform a systematic review examine variability among peer-reviewed evaluating performance generative artificial intelligence (AI)–driven chatbots for summarizing evidence and providing advice inform development Chatbot Assessment Reporting Tool (CHART). Evidence Review A search MEDLINE via Ovid, Embase Elsevier, Web Science from inception October 27, 2023, was conducted with help sciences librarian yield 7752 articles. Two reviewers screened articles by title abstract followed full-text identify primary accuracy AI-driven (chatbot studies). then performed data extraction 137 eligible studies. Findings total were included. Studies examined topics surgery (55 [40.1%]), medicine (51 [37.2%]), care (13 [9.5%]). focused on treatment (91 [66.4%]), diagnosis (60 [43.8%]), or disease prevention (29 [21.2%]). Most (136 [99.3%]) evaluated inaccessible, closed-source did not enough information version LLM under evaluation. All lacked sufficient description characteristics, including temperature, token length, fine-tuning availability, layers, other details. describe prompt engineering phase study. The date querying reported 54 (39.4%) (89 [65.0%]) used subjective means define successful chatbot, while less than one-third addressed ethical, regulatory, patient safety implications LLMs. Conclusions Relevance In this chatbot studies, heterogeneous may CHART standards. Ethical, considerations are crucial as grows

Язык: Английский

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When LLMs meet cybersecurity: a systematic literature review

Cybersecurity, Год журнала: 2025, Номер 8(1)

Опубликована: Фев. 5, 2025

Abstract The rapid development of large language models (LLMs) has opened new avenues across various fields, including cybersecurity, which faces an evolving threat landscape and demand for innovative technologies. Despite initial explorations into the application LLMs in there is a lack comprehensive overview this research area. This paper addresses gap by providing systematic literature review, covering analysis over 300 works, encompassing 25 more than 10 downstream scenarios. Our three key questions: construction cybersecurity-oriented LLMs, to cybersecurity tasks, challenges further study aims shed light on extensive potential enhancing practices serve as valuable resource applying field. We also maintain regularly update list practical guides at https://github.com/tmylla/Awesome-LLM4Cybersecurity .

Язык: Английский

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AI Agents Under Threat: A Survey of Key Security Challenges and Future Pathways

ACM Computing Surveys, Год журнала: 2025, Номер unknown

Опубликована: Фев. 7, 2025

An Artificial Intelligence (AI) agent is a software entity that autonomously performs tasks or makes decisions based on pre-defined objectives and data inputs. AI agents, capable of perceiving user inputs, reasoning planning tasks, executing actions, have seen remarkable advancements in algorithm development task performance. However, the security challenges they pose remain under-explored unresolved. This survey delves into emerging threats faced by categorizing them four critical knowledge gaps: unpredictability multi-step complexity internal executions, variability operational environments, interactions with untrusted external entities. By systematically reviewing these threats, this paper highlights both progress made existing limitations safeguarding agents. The insights provided aim to inspire further research addressing associated thereby fostering more robust secure applications.

Язык: Английский

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A review on machine learning applications in hydrogen energy systems

International Journal of Thermofluids, Год журнала: 2025, Номер unknown, С. 101119 - 101119

Опубликована: Фев. 1, 2025

Язык: Английский

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