Prompt Injection Attacks and Defenses in LLM-Integrated Applications

Large Language Models (LLMs) are increasingly deployed as the backend for a variety of real-world applications called LLM-Integrated Applications. Multiple recent works showed that LLM-Integrated Applications are vulnerable to prompt injection attacks, in which an attacker injects malicious instruction/data into the input of those applications such that they produce results as the attacker desires. However, existing works are limited to case studies. As a result, the literature lacks a systematic understanding of prompt injection attacks and their defenses. We aim to bridge the gap in this work. In particular, we propose a general framework to formalize prompt injection attacks. Existing attacks, which are discussed in research papers and blog posts, are special cases in our framework. Our framework enables us to design a new attack by combining existing attacks. Moreover, we also propose a framework to systematize defenses against prompt injection attacks. Using our frameworks, we conduct a systematic evaluation on prompt injection attacks and their defenses with 10 LLMs and 7 tasks. We hope our frameworks can inspire future research in this field. Our code is available at https://github.com/liu00222/Open-Prompt-Injection

Seizing the window of opportunity to mitigate the impact of climate change on the health of Chinese residents

The health threats posed by climate change in China are increasing rapidly. Each province faces different health risks. Without a timely and adequate response, climate change will impact lives and livelihoods at an accelerated rate and even prevent the achievement of the Healthy and Beautiful China initiatives. The 2021 China Report of the Lancet Countdown on Health and Climate Change is the first annual update of China’s Report of the Lancet Countdown. It comprehensively assesses the impact of climate change on the health of Chinese households and the measures China has taken. Invited by the Lancet committee, Tsinghua University led the writing of the report and cooperated with 25 relevant institutions in and outside of China. The report includes 25 indicators within five major areas (climate change impacts, exposures, and vulnerability; adaptation, planning, and resilience for health; mitigation actions and health co-benefits; economics and finance; and public and political engagement) and a policy brief. This 2021 China policy brief contains the most urgent and relevant indicators focusing on provincial data: The increasing health risks of climate change in China; mixed progress in responding to climate change. In 2020, the heatwave exposures per person in China increased by 4.51 d compared with the 1986–2005 average, resulting in an estimated 92% increase in heatwave-related deaths. The resulting economic cost of the estimated 14500 heatwave-related deaths in 2020 is US$176 million. Increased temperatures also caused a potential 31.5 billion h in lost work time in 2020, which is equivalent to 1.3% of the work hours of the total national workforce, with resulting economic losses estimated at 1.4% of China’s annual gross domestic product. For adaptation efforts, there has been steady progress in local adaptation planning and assessment in 2020, urban green space growth in 2020, and health emergency management in 2019. 12 of 30 provinces reported that they have completed, or were developing, provincial health adaptation plans. Urban green space, which is an important heat adaptation measure, has increased in 18 of 31 provinces in the past decade, and the capacity of China’s health emergency management increased in almost all provinces from 2018 to 2019. As a result of China’s persistent efforts to clean its energy structure and control air pollution, the premature deaths due to exposure to ambient particulate matter of 2.5 μm or less (PM2.5) and the resulting costs continue to decline. However, 98% of China’s cities still have annual average PM2.5 concentrations that are more than the WHO guideline standard of 10 μg/m3. It provides policymakers and the public with up-to-date information on China’s response to climate change and improvements in health outcomes and makes the following policy recommendations. (1) Promote systematic thinking in the related departments and strengthen multi-departmental cooperation. Sectors related to climate and development in China should incorporate health perspectives into their policymaking and actions, demonstrating WHO’s and President Xi Jinping’s so-called health-in-all-policies principle. (2) Include clear goals and timelines for climate-related health impact assessments and health adaptation plans at both the national and the regional levels in the National Climate Change Adaptation Strategy for 2035. (3) Strengthen China’s climate mitigation actions and ensure that health is included in China’s pathway to carbon neutrality. By promoting investments in zero-carbon technologies and reducing fossil fuel subsidies, the current rebounding trend in carbon emissions will be reversed and lead to a healthy, low-carbon future. (4) Increase awareness of the linkages between climate change and health at all levels. Health professionals, the academic community, and traditional and new media should raise the awareness of the public and policymakers on the important linkages between climate change and health.</p

Tirofiban for Stroke without Large or Medium-Sized Vessel Occlusion

The effects of the glycoprotein IIb/IIIa receptor inhibitor tirofiban in patients with acute ischemic stroke but who have no evidence of complete occlusion of large or medium-sized vessels have not been extensively studied. In a multicenter trial in China, we enrolled patients with ischemic stroke without occlusion of large or medium-sized vessels and with a National Institutes of Health Stroke Scale score of 5 or more and at least one moderately to severely weak limb. Eligible patients had any of four clinical presentations: ineligible for thrombolysis or thrombectomy and within 24 hours after the patient was last known to be well; progression of stroke symptoms 24 to 96 hours after onset; early neurologic deterioration after thrombolysis; or thrombolysis with no improvement at 4 to 24 hours. Patients were assigned to receive intravenous tirofiban (plus oral placebo) or oral aspirin (100 mg per day, plus intravenous placebo) for 2 days; all patients then received oral aspirin until day 90. The primary efficacy end point was an excellent outcome, defined as a score of 0 or 1 on the modified Rankin scale (range, 0 [no symptoms] to 6 [death]) at 90 days. Secondary end points included functional independence at 90 days and a quality-of-life score. The primary safety end points were death and symptomatic intracranial hemorrhage. A total of 606 patients were assigned to the tirofiban group and 571 to the aspirin group. Most patients had small infarctions that were presumed to be atherosclerotic. The percentage of patients with a score of 0 or 1 on the modified Rankin scale at 90 days was 29.1% with tirofiban and 22.2% with aspirin (adjusted risk ratio, 1.26; 95% confidence interval, 1.04 to 1.53, P = 0.02). Results for secondary end points were generally not consistent with the results of the primary analysis. Mortality was similar in the two groups. The incidence of symptomatic intracranial hemorrhage was 1.0% in the tirofiban group and 0% in the aspirin group. In this trial involving heterogeneous groups of patients with stroke of recent onset or progression of stroke symptoms and nonoccluded large and medium-sized cerebral vessels, intravenous tirofiban was associated with a greater likelihood of an excellent outcome than low-dose aspirin. Incidences of intracranial hemorrhages were low but slightly higher with tirofiban

A novel gene-based model for prognosis prediction of head and neck squamous cell carcinoma

Background: Head and neck squamous cell carcinoma (HNSCC) is a significant global health challenge. The identification of reliable prognostic biomarkers and construction of an accurate prognostic model are crucial. Methods: In this study, mRNA expression data and clinical data of HNSCC patients from The Cancer Genome Atlas were used. Overlapping candidate genes (OCGs) were identified by intersecting differentially expressed genes and prognosis-related genes. Best prognostic genes were selected using the least absolute shrinkage and selection operator Cox regression based on OCGs, and a risk score was developed using the Cox coefficient of each gene. The prognostic power of the risk score was assessed using Kaplan-Meier survival analysis and time-dependent receiver operating characteristic analysis. Univariate and multivariate Cox regression were performed to identify independent prognostic parameters, which were used to construct a nomogram. The predictive accuracy of the nomogram was evaluated using calibration plots. Functional enrichment analysis of risk score related genes was performed to explore the potential biological functions and pathways. External validation was conducted using data from the Gene Expression Omnibus and ArrayExpress databases. Results: FADS3, TNFRSF12A, TJP3, and FUT6 were screened to be significantly related to prognosis in HNSCC patients. The risk score effectively stratified patients into high-risk group with poor overall survival (OS) and low-risk group with better OS. Risk score, age, clinical M stage and clinical N stage were regarded as independent prognostic parameters by Cox regression analysis and used to construct a nomogram. The nomogram performed well in 1-, 2-, 3-, 5- and 10-year survival predictions. Functional enrichment analysis suggested that tight junction was closely related to the cancer. In addition, the prognostic power of the risk score was validated by external datasets. Conclusions: This study constructed a gene-based model integrating clinical prognostic parameters to accurately predict prognosis in HNSCC patients

A novel method to synthesize pure-phase Si2N2O powders in a fluidized bed reactor

Si2N2O ceramic, an emerging functional and structural material, has a wide range of applications. However, the preparation of pure-phase Si2N2O powder remains challenging due to the mass transfer resistance and unde-sirable side reactions in the conventional methods. Herein, a novel molecular approach combined with the decomposition process has been developed to synthesize pure-phase Si2N2O powders. The hydrated Si(NH)2 precursors were synthesized through the chemical vapor deposition (CVD) of SiCl4, NH3, and humidified N2 in a fluidized bed reactor (FBR) in two steps. Then, the hydrated Si(NH)2 precursors were decomposed into amor-phous and subsequently transformed into crystalline powders under different temperatures and time. It was found that the molar ratio of N/O of the hydrolyzed Si(NH)2 can be controlled by N2 ventilation time and played an important role in synthesizing high pure Si2N2O powder. When it varied from 2.5:1 to 2:1, pure-phase Si2N2O powder was obtained after heat treatment at 1300-1500 degrees C, which features a big tolerance for N/O ratios. This newly developed method offered a chance for the preparation of high-quality Si2N2O powder with high efficiency and low cost

A novel method to synthesize pure-phase Si2N2O powders in a fluidized bed reactor

Si2N2O ceramic, an emerging functional and structural material, has a wide range of applications. However, the preparation of pure-phase Si2N2O powder remains challenging due to the mass transfer resistance and unde-sirable side reactions in the conventional methods. Herein, a novel molecular approach combined with the decomposition process has been developed to synthesize pure-phase Si2N2O powders. The hydrated Si(NH)2 precursors were synthesized through the chemical vapor deposition (CVD) of SiCl4, NH3, and humidified N2 in a fluidized bed reactor (FBR) in two steps. Then, the hydrated Si(NH)2 precursors were decomposed into amor-phous and subsequently transformed into crystalline powders under different temperatures and time. It was found that the molar ratio of N/O of the hydrolyzed Si(NH)2 can be controlled by N2 ventilation time and played an important role in synthesizing high pure Si2N2O powder. When it varied from 2.5:1 to 2:1, pure-phase Si2N2O powder was obtained after heat treatment at 1300-1500 degrees C, which features a big tolerance for N/O ratios. This newly developed method offered a chance for the preparation of high-quality Si2N2O powder with high efficiency and low cost

Analysis and Optimization of a Microgripper Driven by Linear Ultrasonic Motors

This paper presents the vibration response analysis and optimal structural design of a microgripper driven by linear ultrasonic motors (LUMs) dedicated to improving end-point positioning accuracy. Based on structural vibration theory, a parametric vibration response model of the microgripper finger was established, and the relative sensitivities of the structural and material parameters that affect the vibration amplitude of the fingertip were calculated within the structural and material constraints. Then, according to the sensitivity calculation results, a multidimensional constrained nonlinear optimization model was constructed to suppress the vibration of the end-effector. The improved internal penalty function method combined with Newton iteration was adopted to obtain the optimal structural parameters. Finally, the vibration experimental results show that the vibration amplitude of the initial microgripper fingertip is 16.31 μm, and the value measured after optimization was 2.49 μm, exhibiting a reduction of 84.7%. Therefore, the proposed optimal design method can effectively restrain the vibration of the microgripper end-effector and improve manipulation stability

A Novel Reactive Power Optimization in Distribution Network Based on Typical Scenarios Partitioning and Load Distribution Matching Method

This paper proposed an entropy weight optimum seeking method (EWOSM) based on the typical scenarios partitioning and load distribution matching, to solve the reactive power optimization problem in distribution network under the background of big data. Firstly, the mathematic model of reactive power optimization is provided to analyze the relationship between the data source and the optimization schemes in distribution network, which illustrate the feasibility of using large amount of historical data to solve reactive power optimization. Then, the typical scenarios partitioning method and load distribution matching method are presented, which can select out some loads that have the same or similar distributions with the load to be optimized from historical database rapidly, and the corresponding historical optimization schemes are used as the alternatives. As the reactive power optimization is a multi-objective problem, the multi-attribute decision making method based on entropy weight method is used to select out the optimal scheme from the alternatives. The objective weights of evaluation indexes are determined by entropy weight method, and then the multi-attribute decision making problem is transformed to a single attribute decision making problem. Finally, the proposed method is tested on several systems with different scales and compared with existing methods to prove the validity and superiority