StateAFL: Greybox fuzzing for stateful network servers

Fuzzing network servers is a technical challenge, since the behavior of the target server depends on its state over a sequence of multiple messages. Existing solutions are costly and difficult to use, as they rely on manually-customized artifacts such as protocol models, protocol parsers, and learning frameworks. The aim of this work is to develop a greybox fuzzer for network servers that only relies on lightweight analysis of the target program, with no manual customization, in a similar way to what the AFL fuzzer achieved for stateless programs. The proposed fuzzer instruments the target server at compile-time, to insert probes on memory allocations and network I/O operations. At run-time, it infers the current protocol state of the target by analyzing snapshots of long-lived memory areas, and incrementally builds a protocol state machine for guiding fuzzing. The experimental results show that the fuzzer can be applied with no manual customization on a large set of network servers for popular protocols, and that it can achieve comparable, or even better code coverage than customized fuzzing. Moreover, our qualitative analysis shows that states inferred from memory better reflect the server behavior than only using response codes from messages.Comment: The tool is available at https://github.com/stateafl/stateaf

Dependability Evaluation of Middleware Technology for Large-scale Distributed Caching

Distributed caching systems (e.g., Memcached) are widely used by service providers to satisfy accesses by millions of concurrent clients. Given their large-scale, modern distributed systems rely on a middleware layer to manage caching nodes, to make applications easier to develop, and to apply load balancing and replication strategies. In this work, we performed a dependability evaluation of three popular middleware platforms, namely Twemproxy by Twitter, Mcrouter by Facebook, and Dynomite by Netflix, to assess availability and performance under faults, including failures of Memcached nodes and congestion due to unbalanced workloads and network link bandwidth bottlenecks. We point out the different availability and performance trade-offs achieved by the three platforms, and scenarios in which few faulty components cause cascading failures of the whole distributed system.Comment: 2020 IEEE 31st International Symposium on Software Reliability Engineering (ISSRE 2020

Monitoring of Aging Software Systems Affected by Integer Overflows

Numerical aging-related bugs, which can manifest themselves as the accumulation of floating-point errors and the overflow of integers, represent a known but relatively neglected issue in the field of software aging and rejuvenation. Unfortunately, it is very difficult to avoid and to fix these bugs, since the rules of computer arithmetic and programming languages are often misunderstood or disregarded by programmers. Even though software rejuvenation can potentially mitigate these problems, its adoption is prevented by the lack of approaches for forecasting numerical software aging failures: in order to efficiently plan rejuvenation, the rate of numerical errors has to be known, or at least estimated. In this paper, we focus on software aging phenomena related to integer overflows. We present some examples of integer overflow issues of the MySQL open-source DBMS, and an approach for identifying symptoms of potential integer overflows by on-line monitoring

Fault Injection Analytics: A Novel Approach to Discover Failure Modes in Cloud-Computing Systems

Cloud computing systems fail in complex and unexpected ways due to unexpected combinations of events and interactions between hardware and software components. Fault injection is an effective means to bring out these failures in a controlled environment. However, fault injection experiments produce massive amounts of data, and manually analyzing these data is inefficient and error-prone, as the analyst can miss severe failure modes that are yet unknown. This paper introduces a new paradigm (fault injection analytics) that applies unsupervised machine learning on execution traces of the injected system, to ease the discovery and interpretation of failure modes. We evaluated the proposed approach in the context of fault injection experiments on the OpenStack cloud computing platform, where we show that the approach can accurately identify failure modes with a low computational cost.Comment: IEEE Transactions on Dependable and Secure Computing; 16 pages. arXiv admin note: text overlap with arXiv:1908.1164

Olive polyphenol effects in a mouse model of chronic ethanol addiction

Objectives Alcohol addiction elicits oxidative imbalance and it is well known that polyphenols possess antioxidant properties. We investigated whether or not polyphenols could confer a protective potential against alcohol-induced oxidative stress. Methods We administered (per os) for two months 20 mg/kg of olive polyphenols containing mostly hydroxytyrosol in alcoholic adult male mice. Hydroxytyrosol metabolites as hydroxytyrosol sulfate 1 and hydroxytyrosol sulfate 2 were found in the serum of mice administered with polyphenols with the highest amount in animals treated with both polyphenols and alcohol. Oxidative stress was evaluated by FORT (free oxygen radical test) and FORD (free oxygen radical defense) tests. Results Alcoholic mice showed a worse oxidative status than nonalcoholic mice (higher FORT and lower FORD) but polyphenol supplementation partially counteracted the alcohol pro-oxidant effects, as evidenced by FORT. Conclusions A better understanding of the antioxidant protection provided by polyphenols might be of primary interest for drug discovery and dietary-based prevention of the damage associated with chronic alcohol abus

Automating the Correctness Assessment of AI-generated Code for Security Contexts

In this paper, we propose a fully automated method, named ACCA, to evaluate the correctness of AI-generated code for security purposes. The method uses symbolic execution to assess whether the AI-generated code behaves as a reference implementation. We use ACCA to assess four state-of-the-art models trained to generate security-oriented assembly code and compare the results of the evaluation with different baseline solutions, including output similarity metrics, widely used in the field, and the well-known ChatGPT, the AI-powered language model developed by OpenAI. Our experiments show that our method outperforms the baseline solutions and assesses the correctness of the AI-generated code similar to the human-based evaluation, which is considered the ground truth for the assessment in the field. Moreover, ACCA has a very strong correlation with human evaluation (Pearson's correlation coefficient r=0.84 on average). Finally, since it is a fully automated solution that does not require any human intervention, the proposed method performs the assessment of every code snippet in ~0.17s on average, which is definitely lower than the average time required by human analysts to manually inspect the code, based on our experience

Who Evaluates the Evaluators? On Automatic Metrics for Assessing AI-based Offensive Code Generators

AI-based code generators are an emerging solution for automatically writing programs starting from descriptions in natural language, by using deep neural networks (Neural Machine Translation, NMT). In particular, code generators have been used for ethical hacking and offensive security testing by generating proof-of-concept attacks. Unfortunately, the evaluation of code generators still faces several issues. The current practice uses automatic metrics, which compute the textual similarity of generated code with ground-truth references. However, it is not clear what metric to use, and which metric is most suitable for specific contexts. This practical experience report analyzes a large set of output similarity metrics on offensive code generators. We apply the metrics on two state-of-the-art NMT models using two datasets containing offensive assembly and Python code with their descriptions in the English language. We compare the estimates from the automatic metrics with human evaluation and provide practical insights into their strengths and limitations

Enhancing Robustness of AI Offensive Code Generators via Data Augmentation

In this work, we present a method to add perturbations to the code descriptions, i.e., new inputs in natural language (NL) from well-intentioned developers, in the context of security-oriented code, and analyze how and to what extent perturbations affect the performance of AI offensive code generators. Our experiments show that the performance of the code generators is highly affected by perturbations in the NL descriptions. To enhance the robustness of the code generators, we use the method to perform data augmentation, i.e., to increase the variability and diversity of the training data, proving its effectiveness against both perturbed and non-perturbed code descriptions

A Latency-driven Availability Assessment for Multi-Tenant Service Chains

Nowadays, most telecommunication services adhere to the Service Function Chain (SFC) paradigm, where network functions are implemented via software. In particular, container virtualization is becoming a popular approach to deploy network functions and to enable resource slicing among several tenants. The resulting infrastructure is a complex system composed by a huge amount of containers implementing different SFC functionalities, along with different tenants sharing the same chain. The complexity of such a scenario lead us to evaluate two critical metrics: the steady-state availability (the probability that a system is functioning in long runs) and the latency (the time between a service request and the pertinent response). Consequently, we propose a latency-driven availability assessment for multi-tenant service chains implemented via Containerized Network Functions (CNFs). We adopt a multi-state system to model single CNFs and the queueing formalism to characterize the service latency. To efficiently compute the availability, we develop a modified version of the Multidimensional Universal Generating Function (MUGF) technique. Finally, we solve an optimization problem to minimize the SFC cost under an availability constraint. As a relevant example of SFC, we consider a containerized version of IP Multimedia Subsystem, whose parameters have been estimated through fault injection techniques and load tests