1,720,954 research outputs found
Leveraging Uncensored, Self-hosted Large Language Models for Interactive Penetration Testing
No full textGenerative AI, especially large language models (LLMs), have quickly advanced and transformed cybersecurity in the recent years, bringing powerful new capabilities, but at the same time with unprecedented security risks and potential for misuse. With uncensored LLMs now widely available, there is a growing concern how these powerful models can be utilized and exploited in the context of penetration testing. Understanding how they behave in realistic and interactive environments is essential for assessing their potential and risks. Much of the identified existing research had focused and was limited mostly to comparisons of chat prompts and the differences in the responses between publicly available LLMs and uncensored ones. This leaves a major gap of not assessing how the uncensored models can perform in practical penetration testing scenarios and dynamic real-world like settings. Our thesis aims to address that by practically evaluating the integration of uncensored, self-hosted Large Language Models (LLMs) into penetration testing workflows. The goal was to understand their operational capabilities, ethical risks, and potential value for cybersecurity practitioners in offensive security contexts. We deployed multiple uncensored LLMs on cloud-hosted infrastructure and integrated them with an interface capable of querying the LLMs for valid commands and executing them as well as using the LLMs as a live chatbot for assistance during the different scenarios when needed. We ran a series of penetration testing scenarios, like vulnerability scanning, exploit generation and phishing simulation, against vulnerable machines from ethical hacking platforms. Outputs, command accuracy, and quality of interaction were recorded and later analysed. Uncensored LLMs consistently outperformed censored ones in response flexibility. They generated more detailed payloads, offering accurate command-line suggestions, and adapted well to iterative prompts. However, we noticed that they also introduced risks such as hallucinating commands, suggesting unsafe actions, and lacked ethical constraints. While we did not specifically benchmark it, task completion and automation success were significantly higher in uncensored models across all tested categories. We concluded that uncensored LLMs could significantly enhance penetration testing when used responsibly, serving as powerful co-pilots for security professionals. But their lack of internal guardrails means they must be handled with extra caution. The findings highlight both the upsides and risks of these tools, and point to the need for ethical governance, access controls, and improved safeguards. For cybersecurity practitioners and researchers, this work adds practical insight into how LLMs might reshape offensive security work in the near future
Leveraging Uncensored, Self-hosted Large Language Models for Interactive Penetration Testing
No full textGenerative AI, especially large language models (LLMs), have quickly advanced and transformed cybersecurity in the recent years, bringing powerful new capabilities, but at the same time with unprecedented security risks and potential for misuse. With uncensored LLMs now widely available, there is a growing concern how these powerful models can be utilized and exploited in the context of penetration testing. Understanding how they behave in realistic and interactive environments is essential for assessing their potential and risks. Much of the identified existing research had focused and was limited mostly to comparisons of chat prompts and the differences in the responses between publicly available LLMs and uncensored ones. This leaves a major gap of not assessing how the uncensored models can perform in practical penetration testing scenarios and dynamic real-world like settings. Our thesis aims to address that by practically evaluating the integration of uncensored, self-hosted Large Language Models (LLMs) into penetration testing workflows. The goal was to understand their operational capabilities, ethical risks, and potential value for cybersecurity practitioners in offensive security contexts. We deployed multiple uncensored LLMs on cloud-hosted infrastructure and integrated them with an interface capable of querying the LLMs for valid commands and executing them as well as using the LLMs as a live chatbot for assistance during the different scenarios when needed. We ran a series of penetration testing scenarios, like vulnerability scanning, exploit generation and phishing simulation, against vulnerable machines from ethical hacking platforms. Outputs, command accuracy, and quality of interaction were recorded and later analysed. Uncensored LLMs consistently outperformed censored ones in response flexibility. They generated more detailed payloads, offering accurate command-line suggestions, and adapted well to iterative prompts. However, we noticed that they also introduced risks such as hallucinating commands, suggesting unsafe actions, and lacked ethical constraints. While we did not specifically benchmark it, task completion and automation success were significantly higher in uncensored models across all tested categories. We concluded that uncensored LLMs could significantly enhance penetration testing when used responsibly, serving as powerful co-pilots for security professionals. But their lack of internal guardrails means they must be handled with extra caution. The findings highlight both the upsides and risks of these tools, and point to the need for ethical governance, access controls, and improved safeguards. For cybersecurity practitioners and researchers, this work adds practical insight into how LLMs might reshape offensive security work in the near future
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
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