Research

RT: https://truthsocial.com/users/swordy/statuses/114702050153279241@realDonaldTrump for Baron and his #marvelous #retarded #undergorund #journeys & #adventures!saucehttps://www.walmart.com/ip/Baron-Trump-s-Marvellous-Underground-Journey-Hardcover-9781946774163/483723647https://www.reddit.com/r/UrbanMyths/comments/1gryffk/baron_trumps_mysterious_adventure_ingersoll/

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Clinical studies have suggested that both MDR1 and MRP may play a significant role in the chemosensitivity and outcome of neuroblastoma. To clarify the nature of multidrug resistance (MDR) in this tumour a series of six neuroblastoma cell lines have ...

Xanthines such as caffeine and theobromine are among the most consumed psychoactive stimulants in the world, either as natural components of coffee, tea and chocolate, or as food additives. The present study assessed if xanthines affect liver sinusoidal endothelial cells (LSEC). Cultured primary rat LSEC were challenged with xanthines at concentrations typically obtained from normal consumption of xanthine-containing beverages, food or medicines; and at higher concentrations below the in vitro toxic limit. The fenestrated morphology of LSEC were examined with scanning electron and structured illumination microscopy. All xanthine challenges had no toxic effects on LSEC ultrastructure as judged by LSEC fenestration morphology, or function as determined by endocytosis studies. All xanthines in high concentrations (150 μg/mL) increased fenestration frequency but at physiologically relevant concentrations, only theobromine (8 μg/mL) showed an effect. LSEC porosity was influenced only by high caffeine doses which also shifted the fenestration distribution towards smaller pores. Moreover, a dose-dependent increase in fenestration number was observed after caffeine treatment. If these compounds induce similar changes in vivo , age-related reduction of LSEC porosity can be reversed by oral treatment with theobromine or with other xanthines using targeted delivery. ### Competing Interest Statement The authors have declared no competing interest.

This video was created in OBS in order to comment openly on the internet-based terminology relating to "involuntary celibacy."

Archived web material for CDC.gov is preserved on the CDC Archive Site

The Master Trust Bank of Japan, Ltd. ist eine japanische Trust- und Investmentbank. Sie arbeitet auch im Bereich der Holdings von Lebensversicherungs-Fonds. Die Gesamtsumme des in Trusts verwalteten Vermögens belief sich zum Ende des Geschäftsjahres 2007 auf 137 Billionen Yen.

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Chinese startup DeepSeek’s new open-source model AI has sent shockwaves across the globe with President Trump calling the release a “wake-up call” for US firms

We know mobile devices can be distracting, but are they really getting in the way of a good night's rest? This study reveals how devices can interrupt sleep habits, along with other insights into how mobile devices affect family life.

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In this video, we take an in-depth look at the astrological, spiritual, and energetic effects of the “May Final Portal” that opened on May 30-31. We explain ...

Learn Prompting is the largest and most comprehensive course in prompt engineering available on the internet, with over 60 content modules, translated into 9 languages, and a thriving community.

Kaspersky’s new global research reveals challenges behind limited family conversations on the internet safety

Learn Prompting is the largest and most comprehensive course in prompt engineering available on the internet, with over 60 content modules, translated into 9 languages, and a thriving community.

Learn Prompting is the largest and most comprehensive course in prompt engineering available on the internet, with over 60 content modules, translated into 9 languages, and a thriving community.

The misuse of large language models (LLMs) has drawn significant attention from the general public and LLM vendors. One particular type of adversarial prompt, known as jailbreak prompt, has emerged as the main attack vector to bypass the safeguards and elicit harmful content from LLMs. In this paper, employing our new framework JailbreakHub, we conduct a comprehensive analysis of 1,405 jailbreak prompts spanning from December 2022 to December 2023. We identify 131 jailbreak communities and discover unique characteristics of jailbreak prompts and their major attack strategies, such as prompt injection and privilege escalation. We also observe that jailbreak prompts increasingly shift from online Web communities to prompt-aggregation websites and 28 user accounts have consistently optimized jailbreak prompts over 100 days. To assess the potential harm caused by jailbreak prompts, we create a question set comprising 107,250 samples across 13 forbidden scenarios. Leveraging this dataset, our experiments on six popular LLMs show that their safeguards cannot adequately defend jailbreak prompts in all scenarios. Particularly, we identify five highly effective jailbreak prompts that achieve 0.95 attack success rates on ChatGPT (GPT-3.5) and GPT-4, and the earliest one has persisted online for over 240 days. We hope that our study can facilitate the research community and LLM vendors in promoting safer and regulated LLMs.

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Learn about obfuscation and token smuggling in prompt hacking. These techniques bypass filters by replacing or encoding sensitive words, making attacks harder to detect.

The jailbreak technique "Bad Likert Judge" manipulates LLMs to generate harmful content using Likert scales, exposing safety gaps in LLM guardrails.

Recent explorations with commercial Large Language Models (LLMs) have shown that non-expert users can jailbreak LLMs by simply manipulating their prompts; resulting in degenerate output behavior, privacy and security breaches, offensive outputs, and violations of content regulator policies. Limited studies have been conducted to formalize and analyze these attacks and their mitigations. We bridge this gap by proposing a formalism and a taxonomy of known (and possible) jailbreaks. We survey existing jailbreak methods and their effectiveness on open-source and commercial LLMs (such as GPT-based models, OPT, BLOOM, and FLAN-T5-XXL). We further discuss the challenges of jailbreak detection in terms of their effectiveness against known attacks. For further analysis, we release a dataset of model outputs across 3700 jailbreak prompts over 4 tasks.