Matching Items (2)
Description
Web applications are an incredibly important aspect of our modern lives. Organizations
and developers use automated vulnerability analysis tools, also known as
scanners, to automatically find vulnerabilities in their web applications during development.
Scanners have traditionally fallen into two types of approaches: black-box
and white-box. In the black-box approaches, the scanner does not have access to the
source code of the web application whereas a white-box approach has access to the
source code. Today’s state-of-the-art black-box vulnerability scanners employ various
methods to fuzz and detect vulnerabilities in a web application. However, these
scanners attempt to fuzz the web application with a number of known payloads and
to try to trigger a vulnerability. This technique is simple but does not understand
the web application that it is testing. This thesis, presents a new approach to vulnerability
analysis. The vulnerability analysis module presented uses a novel approach
of Inductive Reverse Engineering (IRE) to understand and model the web application.
IRE first attempts to understand the behavior of the web application by giving
certain number of input/output pairs to the web application. Then, the IRE module
hypothesizes a set of programs (in a limited language specific to web applications,
called AWL) that satisfy the input/output pairs. These hypotheses takes the form of
a directed acyclic graph (DAG). AWL vulnerability analysis module can then attempt
to detect vulnerabilities in this DAG. Further, it generates the payload based on the
DAG, and therefore this payload will be a precise payload to trigger the potential vulnerability
(based on our understanding of the program). It then tests this potential
vulnerability using the generated payload on the actual web application, and creates
a verification procedure to see if the potential vulnerability is actually vulnerable,
based on the web application’s response.
and developers use automated vulnerability analysis tools, also known as
scanners, to automatically find vulnerabilities in their web applications during development.
Scanners have traditionally fallen into two types of approaches: black-box
and white-box. In the black-box approaches, the scanner does not have access to the
source code of the web application whereas a white-box approach has access to the
source code. Today’s state-of-the-art black-box vulnerability scanners employ various
methods to fuzz and detect vulnerabilities in a web application. However, these
scanners attempt to fuzz the web application with a number of known payloads and
to try to trigger a vulnerability. This technique is simple but does not understand
the web application that it is testing. This thesis, presents a new approach to vulnerability
analysis. The vulnerability analysis module presented uses a novel approach
of Inductive Reverse Engineering (IRE) to understand and model the web application.
IRE first attempts to understand the behavior of the web application by giving
certain number of input/output pairs to the web application. Then, the IRE module
hypothesizes a set of programs (in a limited language specific to web applications,
called AWL) that satisfy the input/output pairs. These hypotheses takes the form of
a directed acyclic graph (DAG). AWL vulnerability analysis module can then attempt
to detect vulnerabilities in this DAG. Further, it generates the payload based on the
DAG, and therefore this payload will be a precise payload to trigger the potential vulnerability
(based on our understanding of the program). It then tests this potential
vulnerability using the generated payload on the actual web application, and creates
a verification procedure to see if the potential vulnerability is actually vulnerable,
based on the web application’s response.
ContributorsKhairnar, Tejas (Author) / Doupe, Adam (Thesis advisor) / Ahn, Gail-Joon (Committee member) / Zhao, Ziming (Committee member) / Arizona State University (Publisher)
Created2017
Description
Reverse engineering is critical to reasoning about how a system behaves. While complete access to a system inherently allows for perfect analysis, partial access is inherently uncertain. This is the case foran individual agent in a distributed system. Inductive Reverse Engineering (IRE) enables analysis under
such circumstances. IRE does this by producing program spaces consistent with individual input-output examples for a given domain-specific language. Then, IRE intersects those program spaces to produce a generalized program consistent with all examples. IRE, an easy to use framework, allows this domain-specific language to be specified in the form of Theorist s, which produce Theory s, a succinct way of representing the program space.
Programs are often much more complex than simple string transformations. One of the ways in which they are more complex is in the way that they follow a conversation-like behavior, potentially following some underlying protocol. As a result, IRE represents program interactions as Conversations in order to
more correctly model a distributed system. This, for instance, enables IRE to model dynamically captured inputs received from other agents in the distributed system.
While domain-specific knowledge provided by a user is extremely valuable, such information is not always possible. IRE mitigates this by automatically inferring program grammars, allowing it to still perform efficient searches of the program space. It does this by intersecting conversations prior to synthesis in order to understand what portions of conversations are constant.
IRE exists to be a tool that can aid in automatic reverse engineering across numerous domains. Further, IRE aspires to be a centralized location and interface for implementing program synthesis and automatic black box analysis techniques.
such circumstances. IRE does this by producing program spaces consistent with individual input-output examples for a given domain-specific language. Then, IRE intersects those program spaces to produce a generalized program consistent with all examples. IRE, an easy to use framework, allows this domain-specific language to be specified in the form of Theorist s, which produce Theory s, a succinct way of representing the program space.
Programs are often much more complex than simple string transformations. One of the ways in which they are more complex is in the way that they follow a conversation-like behavior, potentially following some underlying protocol. As a result, IRE represents program interactions as Conversations in order to
more correctly model a distributed system. This, for instance, enables IRE to model dynamically captured inputs received from other agents in the distributed system.
While domain-specific knowledge provided by a user is extremely valuable, such information is not always possible. IRE mitigates this by automatically inferring program grammars, allowing it to still perform efficient searches of the program space. It does this by intersecting conversations prior to synthesis in order to understand what portions of conversations are constant.
IRE exists to be a tool that can aid in automatic reverse engineering across numerous domains. Further, IRE aspires to be a centralized location and interface for implementing program synthesis and automatic black box analysis techniques.
ContributorsNelson, Connor David (Author) / Doupe, Adam (Thesis advisor) / Shoshitaishvili, Yan (Committee member) / Wang, Ruoyu (Committee member) / Arizona State University (Publisher)
Created2019