UniFuzz: Optimizing Distributed Fuzzing via Dynamic Centralized Task Scheduling

Fuzzing is one of the most efficient technology for vulnerability detection. Since the fuzzing process is computing-intensive and the performance improved by algorithm optimization is limited, recent research seeks to improve fuzzing performance by utilizing parallel computing. However, parallel fuzzing has to overcome challenges such as task conflicts, scalability in a distributed environment, synchronization overhead, and workload imbalance. In this paper, we design and implement UniFuzz, a distributed fuzzing optimization based on a dynamic centralized task scheduling. UniFuzz evaluates and distributes seeds in a centralized manner to avoid task conflicts. It uses a "request-response" scheme to dynamically distribute fuzzing tasks, which avoids workload imbalance. Besides, UniFuzz can adaptively switch the role of computing cores between evaluating, and fuzzing, which avoids the potential bottleneck of seed evaluation. To improve synchronization efficiency, UniFuzz shares different fuzzing information in a different way according to their characteristics, and the average overhead of synchronization is only about 0.4\%. We evaluated UniFuzz with real-world programs, and the results show that UniFuzz outperforms state-of-the-art tools, such as AFL, PAFL and EnFuzz. Most importantly, the experiment reveals a counter-intuitive result that parallel fuzzing can achieve a super-linear acceleration to the single-core fuzzing. We made a detailed explanation and proved it with additional experiments. UniFuzz also discovered 16 real-world vulnerabilities.Comment: 14 pages, 4 figure

Targeted Delivery of Immunomodulators to Lymph Nodes

SUMMARY Active-targeted delivery to lymph nodes represents a major advance toward more effective treatment of immune-mediated disease. The MECA79 antibody recognizes peripheral node address in molecules expressed by high endothelial venules of lymph nodes. By mimicking lymphocyte trafficking to the lymph nodes, we have engineered MECA79-coated microparticles containing an immunosuppressive medication, tacrolimus. Following intravenous administration, MECA79-bearing particles showed marked accumulation in the draining lymph nodes of transplanted animals. Using an allograft heart transplant model, we show that targeted lymph node delivery of microparticles containing tacrolimus can prolong heart allograft survival with negligible changes in tacrolimus serum level. Using MECA79 conjugation, we have demonstrated targeted delivery of tacrolimus to the lymph nodes following systemic administration, with the capacity for immune modulation in vivo

FirmHunter: State-Aware and Introspection-Driven Grey-Box Fuzzing towards IoT Firmware

IoT devices are exponentially increasing in all aspects of our lives. Via the web interfaces of IoT devices, attackers can control IoT devices by exploiting their vulnerabilities. In order to guarantee IoT security, testing these IoT devices to detect vulnerabilities is very important. In this work, we present FirmHunter, an automated state-aware and introspection-driven grey-box fuzzer towards Linux-based firmware images on the basis of emulation. It employs a message-state queue to overcome the dependency problem in test cases. Furthermore, it implements a scheduler collecting execution information from system introspection to drive fuzzing towards more interesting test cases, which speeds up vulnerability discovery. We evaluate FirmHunter by emulating and fuzzing eight firmware images including seven routers and one IP camera with a state-of-the-art IoT fuzzer FirmFuzz and a web application scanner ZAP. Our evaluation results show that (1) the message-state queue enables FirmHunter to parse the dependencies in test cases and find real-world vulnerabilities that other fuzzers cannot detect; (2) our scheduler accelerates the discovery of vulnerabilities by an average of 42%; and (3) FirmHunter is able to find unknown vulnerabilities

P-Fuzz: A Parallel Grey-Box Fuzzing Framework

Fuzzing is an effective technology in software testing and security vulnerability detection. Unfortunately, fuzzing is an extremely compute-intensive job, which may cause thousands of computing hours to find a bug. Current novel works generally improve fuzzing efficiency by developing delicate algorithms. In this paper, we propose another direction of improvement in this field, i.e., leveraging parallel computing to improve fuzzing efficiency. In this way, we develop P-fuzz, a parallel fuzzing framework that can utilize massive, distributed computing resources to fuzz. P-fuzz uses a database to share the fuzzing status such as seeds, the coverage information, etc. All fuzzing nodes get tasks from the database and update their fuzzing status to the database. Also, P-fuzz handles some data races and exceptions in parallel fuzzing. We compare P-fuzz with AFL and a parallel fuzzing framework Roving in our experiment. The result shows that P-fuzz can easily speed up AFL about 2.59× and Roving about 1.66× on average by using 4 nodes

BMI Modulates the Effect of Thyroid Hormone on Lipid Profile in Euthyroid Adults

The impacts of thyroid hormones (TH) on lipid profile in euthyroid adults have gained much attention. It is currently unknown whether BMI influences such interaction. In the present study, we investigate the role of BMI in modulating the association between TH and lipid parameters in 1372 euthyroid healthy adults. Our results show that thyroid parameters are differentially associated with lipid profile. FT3 is positively correlated with total cholesterol (β=0.176±0.046, P<0.001) and LDL cholesterol levels (β=0.161±0.040, P<0.001). FT4 is negatively correlated with TG (β=−0.087±0.029, P<0.01) while positively correlated with HDL cholesterol levels (β=0.013±0.005, P<0.01). TSH is positively associated with TG (β=0.145±0.056, P<0.05) and total cholesterol levels (β=0.094±0.030, P<0.01). Importantly, BMI modulates the effect of TH on lipid profile: the interaction of FT4 and BMI and the interaction of FT3 and BMI reach statistical significance in predicting TG and HDL cholesterol levels, respectively. Stratified according to BMI levels, most associations between TH and lipid profile are significant only in normal-weight group. In conclusion, in euthyroid adults, high normal FT3, TSH levels, and low normal FT4 levels are associated with unfavorable lipid profile. BMI mediates the effect of thyroid function on lipid profile in euthyroid adults

SIoTFuzzer: Fuzzing Web Interface in IoT Firmware via Stateful Message Generation

Cyber attacks against the web management interface of Internet of Things (IoT) devices often have serious consequences. Current research uses fuzzing technologies to test the web interfaces of IoT devices. These IoT fuzzers generate messages (a test case sent from the client to the server to test its functionality) without considering their dependency, which is unlikely to bypass the early check of the server. These invalid test cases significantly reduce the efficiency of fuzzing. To overcome this problem, we propose a stateful message generation (SMG) mechanism for IoT web fuzzing. SMG addresses two problems in IoT fuzzing. First, we retrieve the message dependency by using web front-end analysis and status analysis. These dependent messages, which can easily bypass the server check, are used as a valid seed. Second, we adopt a multi-message seed format to preserve the dependency of the messages when mutating the seed to get a valid test case, so that the test case can bypass the state check of the server to make a valid test. Message dependency preservation is implemented by our proposed parameter mutation and structural mutation methods. We implement SMG in our IoT fuzzer, SIoTFuzzer, which applies IoT firmware on the latest Linux-based simulation tool, FirmAE. We test nine IoT devices including a router and an IP camera and adopt a vulnerability detection mechanism. Our evaluation results show that (1) SIoTFuzzer is capable of finding real-world vulnerabilities in IoT devices; (2) our SMG is effective as it enables Boofuzz (a popular protocol fuzzer) to find command injection and cross-site scripting (XSS) vulnerabilities; and (3) compared to FirmFuzz, SIoTFuzzer found all the vulnerabilities in our benchmarks, while FirmFuzz found only four—the efficiency of our tool increased by 20.57% on average

Comprehensive resistome analysis reveals the prevalence of NDM and MCR-1 in Chinese poultry production

By 2030, the global population will be 8.5 billion, placing pressure on international poultry production, of which China is a key producer1. From April 2017, China will implement the withdrawal of colistin as a growth promoter, removing over 8,000 tonnes per year from the Chinese farming sector2. To understand the impact of banning colistin and the epidemiology of multi-drug-resistant (MDR) Escherichia coli (using blaNDM and mcr-1 as marker genes), we sampled poultry, dogs, sewage, wild birds and flies. Here, we show that mcr-1, but not blaNDM, is prevalent in hatcheries, but blaNDM quickly contaminates flocks through dogs, flies and wild birds. We also screened samples directly for resistance genes to understand the true breadth and depth of the environmental and animal resistome. Direct sample testing for blaNDM and mcr-1 in hatcheries, commercial farms, a slaughterhouse and supermarkets revealed considerably higher levels of positive samples than the blaNDM- and mcr-1-positive E. coli, indicating a substantial segment of unseen resistome—a phenomenon we have termed the ‘phantom resistome’. Whole-genome sequencing identified common blaNDM-positive E. coli shared among farms, flies, dogs and farmers, providing direct evidence of carbapenem-resistant E. coli transmission and environmental contamination