1,634 research outputs found
Structural Learning of Attack Vectors for Generating Mutated XSS Attacks
Web applications suffer from cross-site scripting (XSS) attacks that
resulting from incomplete or incorrect input sanitization. Learning the
structure of attack vectors could enrich the variety of manifestations in
generated XSS attacks. In this study, we focus on generating more threatening
XSS attacks for the state-of-the-art detection approaches that can find
potential XSS vulnerabilities in Web applications, and propose a mechanism for
structural learning of attack vectors with the aim of generating mutated XSS
attacks in a fully automatic way. Mutated XSS attack generation depends on the
analysis of attack vectors and the structural learning mechanism. For the
kernel of the learning mechanism, we use a Hidden Markov model (HMM) as the
structure of the attack vector model to capture the implicit manner of the
attack vector, and this manner is benefited from the syntax meanings that are
labeled by the proposed tokenizing mechanism. Bayes theorem is used to
determine the number of hidden states in the model for generalizing the
structure model. The paper has the contributions as following: (1)
automatically learn the structure of attack vectors from practical data
analysis to modeling a structure model of attack vectors, (2) mimic the manners
and the elements of attack vectors to extend the ability of testing tool for
identifying XSS vulnerabilities, (3) be helpful to verify the flaws of
blacklist sanitization procedures of Web applications. We evaluated the
proposed mechanism by Burp Intruder with a dataset collected from public XSS
archives. The results show that mutated XSS attack generation can identify
potential vulnerabilities.Comment: In Proceedings TAV-WEB 2010, arXiv:1009.330
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RUN-TIME ANALYSIS AND SECURITY OF MULTI-LANGUAGE SYSTEMS
The contemporary software development landscape has witnessed a widespread integration of diverse programming languages, leveraging the specific advantages of each, such as the efficiency of C and the programmability of Python. This trend finds notable applications in prominent domains, including the Android operating system and advanced machine learning frameworks like PyTorch. However, adopting this multi-language approach has ushered in aseries of great challenges for developers, necessitating the identification of robust solutions to tackle potential security vulnerabilities.Traditional techniques such as program analysis and fuzzing, initially designed for single-language software, face limitations in effectively uncovering vulnerabilities in multi-language systems. Program analysis grapples with challenges in comprehending the intricate control and data flows across diverse languages, often resulting in incomplete vulnerability detection. Conversely, greybox fuzzing encounters difficulties adapting to the nuances of various languages, leading to incomplete code coverage and complications in reproducing identified vulnerabilities. The intricacies within runtime systems supporting multilingual software exacerbate the security clearance predicament, as these systems are often constructed using multiple languages. This complexity adds an additional layer of difficulty for conventional security techniques, emphasizing the need for more adaptive and comprehensive approachestailored to the unique challenges posed by the multifaceted nature of multi-language systems.Within the scope of my dissertation, I endeavored to tackle the intricate challenges posed by vulnerabilities in multi-language software through a comprehensive and multifaceted approach. This approach entailed conducting extensive empirical investigations into vulnerability susceptibility, facilitating the development of dynamic cross-language information flow analysis. Recognizing the pivotal significance of comprehensive test input coverage, I devisedan integrated greybox fuzzing methodology. This innovative approach integrates sensitivity analysis and comprehensive whole-system coverage measurements, significantly enhancing the efficiency of the fuzzing process and vulnerability identification. Furthermore, I focused on fortifying runtime security by proposing a novel two-level collaborative fuzzing framework tailored explicitly for Python language runtime. This contribution was pivotal in reinforcing the software system’s foundational safeguards, ensuring a robust defense mechanism against potential security threats
Serverification of Molecular Modeling Applications: the Rosetta Online Server that Includes Everyone (ROSIE)
The Rosetta molecular modeling software package provides experimentally
tested and rapidly evolving tools for the 3D structure prediction and
high-resolution design of proteins, nucleic acids, and a growing number of
non-natural polymers. Despite its free availability to academic users and
improving documentation, use of Rosetta has largely remained confined to
developers and their immediate collaborators due to the code's difficulty of
use, the requirement for large computational resources, and the unavailability
of servers for most of the Rosetta applications. Here, we present a unified web
framework for Rosetta applications called ROSIE (Rosetta Online Server that
Includes Everyone). ROSIE provides (a) a common user interface for Rosetta
protocols, (b) a stable application programming interface for developers to add
additional protocols, (c) a flexible back-end to allow leveraging of computer
cluster resources shared by RosettaCommons member institutions, and (d)
centralized administration by the RosettaCommons to ensure continuous
maintenance. This paper describes the ROSIE server infrastructure, a
step-by-step 'serverification' protocol for use by Rosetta developers, and the
deployment of the first nine ROSIE applications by six separate developer
teams: Docking, RNA de novo, ERRASER, Antibody, Sequence Tolerance,
Supercharge, Beta peptide design, NCBB design, and VIP redesign. As illustrated
by the number and diversity of these applications, ROSIE offers a general and
speedy paradigm for serverification of Rosetta applications that incurs
negligible cost to developers and lowers barriers to Rosetta use for the
broader biological community. ROSIE is available at
http://rosie.rosettacommons.org
A Review on Web Application Testing and its Current Research Directions
Testing is an important part of every software development process on which companies devote considerable time and effort. The burgeoning web applications and their proliferating economic significance in the society made the area of web application testing an area of acute importance. The web applications generally tend to take faster and quicker release cycles making their testing very challenging. The main issues in testing are cost efficiency and bug detection efficiency. Coverage-based testing is the process of ensuring exercise of specific program elements. Coverage measurement helps determine the “thoroughness” of testing achieved. An avalanche of tools, techniques, frameworks came into existence to ascertain the quality of web applications. A comparative study of some of the prominent tools, techniques and models for web application testing is presented. This work highlights the current research directions of some of the web application testing techniques
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