BadGPT: Exploring Security Vulnerabilities of ChatGPT via Backdoor Attacks to InstructGPT

Recently, ChatGPT has gained significant attention in research due to its ability to interact with humans effectively. The core idea behind this model is reinforcement learning (RL) fine-tuning, a new paradigm that allows language models to align with human preferences, i.e., InstructGPT. In this study, we propose BadGPT, the first backdoor attack against RL fine-tuning in language models. By injecting a backdoor into the reward model, the language model can be compromised during the fine-tuning stage. Our initial experiments on movie reviews, i.e., IMDB, demonstrate that an attacker can manipulate the generated text through BadGPT.Comment: This paper is accepted as a poster in NDSS202

Solution structure of the second bromodomain of Brd2 and its specific interaction with acetylated histone tails

<p>Abstract</p> <p>Background</p> <p>Brd2 is a transcriptional regulator and belongs to BET family, a less characterized novel class of bromodomain-containing proteins. Brd2 contains two tandem bromodomains (BD1 and BD2, 46% sequence identity) in the N-terminus and a conserved motif named ET (extra C-terminal) domain at the C-terminus that is also present in some other bromodomain proteins. The two bromodomains have been shown to bind the acetylated histone H4 and to be responsible for mitotic retention on chromosomes, which is probably a distinctive feature of BET family proteins. Although the crystal structure of Brd2 BD1 is reported, no structure features have been characterized for Brd2 BD2 and its interaction with acetylated histones.</p> <p>Results</p> <p>Here we report the solution structure of human Brd2 BD2 determined by NMR. Although the overall fold resembles the bromodomains from other proteins, significant differences can be found in loop regions, especially in the ZA loop in which a two amino acids insertion is involved in an uncommon <it>π</it>-helix, termed <it>π</it>D. The helix <it>π</it>D forms a portion of the acetyl-lysine binding site, which could be a structural characteristic of Brd2 BD2 and other BET bromodomains. Unlike Brd2 BD1, BD2 is monomeric in solution. With NMR perturbation studies, we have mapped the H4-AcK12 peptide binding interface on Brd2 BD2 and shown that the binding was with low affinity (2.9 mM) and in fast exchange. Using NMR and mutational analysis, we identified several residues important for the Brd2 BD2-H4-AcK12 peptide interaction and probed the potential mechanism for the specific recognition of acetylated histone codes by Brd2 BD2.</p> <p>Conclusion</p> <p>Brd2 BD2 is monomeric in solution and dynamically interacts with H4-AcK12. The additional secondary elements in the long ZA loop may be a common characteristic of BET bromodomains. Surrounding the ligand-binding cavity, five aspartate residues form a negatively charged collar that serves as a secondary binding site for H4-AcK12. We suggest that Brd2 BD1 and BD2 may possess distinctive roles and cooperate to regulate Brd2 functions. The structure basis of Brd2 BD2 will help to further characterize the functions of Brd2 and its BET members.</p

Personality Openness Predicts Driver Trust in Automated Driving

Maintaining an appropriate level of trust in automated driving (AD) is critical to safe driving. However, few studies have explored factors affecting trust in AD in general, and no study, as far as is known, has directly investigated whether driver personality influences driver trust in an AD system. The current study investigates the relation between driver personality and driver trust in AD, focusing on Level 2 AD. Participants were required to perform a period of AD in a driving simulator, during which their gaze and driving behavior were recorded, as well as their subjective trust scores after driving. In three distinct measures, a significant correlation between Openness and driver trust in the AD system is found: participants with higher Openness traits tend to have less trust in the AD system. No significant correlations between driver trust in AD and other personality traits are found. The findings suggest that driver personality has an impact on driver trust in AD. Theoretical and practical implications of this finding are discussed

Perioperative management and prognosis in over aged patients undergoing non-cardiac surgery： experience with 828 cases in a single center

Objective To investigate perioperative management and prognosis in patients aged≥80 years old undergoing non-cardiac surgery. Methods Clinical data of 828 patients aged≥80 years old undergoing non-cardiac surgery were retrospectively analyzed. Intraoperative hypotension， accumulated time of hypotension， operation time， intraoperative blood loss， postoperative ICU admission， length of ICU stay， length of hospital stay， cost of care， perioperative complications need to be treated， and perioperative death were recorded and analyzed. Results The mean age of 828 patients was （84&#x00B1;4） years old. All cases were classified as American Society of Anesthesiologist （ASA） gradeⅡ-Ⅴ. Postoperative complications occurred in 111 patients （13.4%）， and postoperative death occurred in 24 patients （2.9%）. The incidence of postoperative complications and death in thoracic， neuro-，and vascular surgery was 29% and 17%， the highest among various types of operations （all P &lt; 0.05）. Among patients with different ASA grades， the incidence of postoperative complications and death in patients with ASA gradeⅣand V was significantly higher than that in their counterparts with ASA gradeⅡ（both P &lt; 0.001）. The selection of anesthesia approach did not affect the incidence of postoperative complications and death in different operations except thoracic， neuro-， and vascular surgery under general anesthesia （P &gt; 0.05）. Compared with patients with intraoperative systolic pressure of &lt;120 mmHg， the incidence of postoperative complications was significantly higher in those with systolic pressure of &lt; 90 mmHg（P &lt; 0.05）. The proportion of ICU admission， length of hospital stay， and incidence of postoperative complications were significantly increased over age （all P &lt; 0.01）， whereas postoperative death rate did not differ among patients of different ages （P &gt; 0.05）. Conclusions Patients aged≥80 years old have high incidence of postoperative complications and death rates， which is probably associated with high-risk operation and intraoperative hypotension， especially systolic hypotension of &lt; 90 mmHg enduring for≥10 min

MetaTool Benchmark for Large Language Models: Deciding Whether to Use Tools and Which to Use

Large language models (LLMs) have garnered significant attention due to their impressive natural language processing (NLP) capabilities. Recently, many studies have focused on the tool utilization ability of LLMs. They primarily investigated how LLMs effectively collaborate with given specific tools. However, in scenarios where LLMs serve as intelligent agents, as seen in applications like AutoGPT and MetaGPT, LLMs are expected to engage in intricate decision-making processes that involve deciding whether to employ a tool and selecting the most suitable tool(s) from a collection of available tools to fulfill user requests. Therefore, in this paper, we introduce MetaTool, a benchmark designed to evaluate whether LLMs have tool usage awareness and can correctly choose tools. Specifically, we create a dataset called ToolE within the benchmark. This dataset contains various types of user queries in the form of prompts that trigger LLMs to use tools, including both single-tool and multi-tool scenarios. Subsequently, we set the tasks for both tool usage awareness and tool selection. We define four subtasks from different perspectives in tool selection, including tool selection with similar choices, tool selection in specific scenarios, tool selection with possible reliability issues, and multi-tool selection. We conduct experiments involving nine popular LLMs and find that the majority of them still struggle to effectively select tools, highlighting the existing gaps between LLMs and genuine intelligent agents. However, through the error analysis, we found there is still significant room for improvement. Finally, we conclude with insights for tool developers that follow ChatGPT to provide detailed descriptions that can enhance the tool selection performance of LLMs

Effect of arsenic stress on the intestinal structural integrity and intestinal flora abundance of Cyprinus carpio

Aquatic organisms such as fish can accumulate high concentrations of arsenic (As), which has toxic effects on fish. However, whether the intestinal flora are involved in As damage to fish intestinal tissues and the underlying process are unclear. Common carp (Cyprinus carpio) were exposed to As (2.83 mg/L) in water for 30 days, and blood, muscle, intestine, and intestine samples were collected. Intestinal pathological sections were observed, and the lipopolysaccharide (LPS) levels in serum and the levels of As accumulation and tight junction-related factors in intestinal tissues were measured. The gut microbiota was analysed by 16S rRNA sequencing. The results showed that As treatment decreased the abundance of microbiota, increased the number of harmful bacteria, and decreased the number of beneficial bacteria in the intestine. In our experiment, the top 30 harmful and beneficial bacteria with the highest relative abundance were identified. Among the top 30 harmful and beneficial bacteria, As treatment resulted in a significant (P &lt; 0.05) increase in harmful bacteria (such as Fusobacteriota, Bacteroidota (LPS-producing bacteria), Verrucomicrobiota, Bacteroides, Aeromonas, and Stenotrophomonas) and a significant (P &lt; 0.05) decrease in beneficial bacteria (such as Actinobacteriota, Planctomycetota, Firmicutes, Reyranella, Akkermansia, and Pseudorhodobacter), which further demonstrated that As affects the abundance of intestinal flora. In addition, As exposure increased the LPS level in serum and the abundance of Bacteroidota (LPS-producing bacteria) in the intestine. Bacteroidota exhibits the six highest relative abundance at the phylum level, which indicates that LPS produced by Bacteroidota can increase the LPS level in serum. Additionally, the protein and gene levels of the tight junction markers ZO-1 and occludin in the intestine were reduced by As treatment, which further indicated that As exposure impaired the structural integrity of the intestine. In conclusion, the results obtained in our study indicate that the intestinal flora, LPS, and tight junctions participate in the impairment of the structural integrity of the common carp intestine resulting from As exposure

Research Progress on Food 3D Printing Based on Starch

Three-dimensional (3D) printing, also known as additive manufacturing, is an emerging manufacturing technology that enables personalized product design and precise modeling through digital control. In recent years, 3D printing technology has gained significant attention in the food industry due to its potential advantages, especially in the field of customized food processing. Starch is an important component of human diet, especially in the eastern diet structure primarily based on plant-based foods. Most starches possess excellent rheological, hydration, and gel properties, making them have natural advantages in outflow nozzle and printing molding and thus have great application potential in food 3D printing. In this article, recent progress in starch-based 3D printing is reviewed with respect to printing equipment types commonly used in starch-based 3D printing, printing technology using common starches as raw materials, the correlation between starch physicochemical properties and printing performance, starch modification for quality improvement of 3D printed products, the post-processing of starch-based 3D printed products and the influence of printing on starch structure. Furthermore, future prospects in the 3D printing field are presented

Liver Tumor Screening and Diagnosis in CT with Pixel-Lesion-Patient Network

Liver tumor segmentation and classification are important tasks in computer aided diagnosis. We aim to address three problems: liver tumor screening and preliminary diagnosis in non-contrast computed tomography (CT), and differential diagnosis in dynamic contrast-enhanced CT. A novel framework named Pixel-Lesion-pAtient Network (PLAN) is proposed. It uses a mask transformer to jointly segment and classify each lesion with improved anchor queries and a foreground-enhanced sampling loss. It also has an image-wise classifier to effectively aggregate global information and predict patient-level diagnosis. A large-scale multi-phase dataset is collected containing 939 tumor patients and 810 normal subjects. 4010 tumor instances of eight types are extensively annotated. On the non-contrast tumor screening task, PLAN achieves 95% and 96% in patient-level sensitivity and specificity. On contrast-enhanced CT, our lesion-level detection precision, recall, and classification accuracy are 92%, 89%, and 86%, outperforming widely used CNN and transformers for lesion segmentation. We also conduct a reader study on a holdout set of 250 cases. PLAN is on par with a senior human radiologist, showing the clinical significance of our results.Comment: MICCAI 2023, code: https://github.com/alibaba-damo-academy/pixel-lesion-patient-networ