33,899 research outputs found
Ghera: A Repository of Android App Vulnerability Benchmarks
Security of mobile apps affects the security of their users. This has fueled
the development of techniques to automatically detect vulnerabilities in mobile
apps and help developers secure their apps; specifically, in the context of
Android platform due to openness and ubiquitousness of the platform. Despite a
slew of research efforts in this space, there is no comprehensive repository of
up-to-date and lean benchmarks that contain most of the known Android app
vulnerabilities and, consequently, can be used to rigorously evaluate both
existing and new vulnerability detection techniques and help developers learn
about Android app vulnerabilities. In this paper, we describe Ghera, an open
source repository of benchmarks that capture 25 known vulnerabilities in
Android apps (as pairs of exploited/benign and exploiting/malicious apps). We
also present desirable characteristics of vulnerability benchmarks and
repositories that we uncovered while creating Ghera.Comment: 10 pages. Accepted at PROMISE'1
CONFLLVM: A Compiler for Enforcing Data Confidentiality in Low-Level Code
We present an instrumenting compiler for enforcing data confidentiality in
low-level applications (e.g. those written in C) in the presence of an active
adversary. In our approach, the programmer marks secret data by writing
lightweight annotations on top-level definitions in the source code. The
compiler then uses a static flow analysis coupled with efficient runtime
instrumentation, a custom memory layout, and custom control-flow integrity
checks to prevent data leaks even in the presence of low-level attacks. We have
implemented our scheme as part of the LLVM compiler. We evaluate it on the SPEC
micro-benchmarks for performance, and on larger, real-world applications
(including OpenLDAP, which is around 300KLoC) for programmer overhead required
to restructure the application when protecting the sensitive data such as
passwords. We find that performance overheads introduced by our instrumentation
are moderate (average 12% on SPEC), and the programmer effort to port OpenLDAP
is only about 160 LoC.Comment: Technical report for CONFLLVM: A Compiler for Enforcing Data
Confidentiality in Low-Level Code, appearing at EuroSys 201
SlowFuzz: Automated Domain-Independent Detection of Algorithmic Complexity Vulnerabilities
Algorithmic complexity vulnerabilities occur when the worst-case time/space
complexity of an application is significantly higher than the respective
average case for particular user-controlled inputs. When such conditions are
met, an attacker can launch Denial-of-Service attacks against a vulnerable
application by providing inputs that trigger the worst-case behavior. Such
attacks have been known to have serious effects on production systems, take
down entire websites, or lead to bypasses of Web Application Firewalls.
Unfortunately, existing detection mechanisms for algorithmic complexity
vulnerabilities are domain-specific and often require significant manual
effort. In this paper, we design, implement, and evaluate SlowFuzz, a
domain-independent framework for automatically finding algorithmic complexity
vulnerabilities. SlowFuzz automatically finds inputs that trigger worst-case
algorithmic behavior in the tested binary. SlowFuzz uses resource-usage-guided
evolutionary search techniques to automatically find inputs that maximize
computational resource utilization for a given application.Comment: ACM CCS '17, October 30-November 3, 2017, Dallas, TX, US
Automated Dynamic Firmware Analysis at Scale: A Case Study on Embedded Web Interfaces
Embedded devices are becoming more widespread, interconnected, and
web-enabled than ever. However, recent studies showed that these devices are
far from being secure. Moreover, many embedded systems rely on web interfaces
for user interaction or administration. Unfortunately, web security is known to
be difficult, and therefore the web interfaces of embedded systems represent a
considerable attack surface.
In this paper, we present the first fully automated framework that applies
dynamic firmware analysis techniques to achieve, in a scalable manner,
automated vulnerability discovery within embedded firmware images. We apply our
framework to study the security of embedded web interfaces running in
Commercial Off-The-Shelf (COTS) embedded devices, such as routers, DSL/cable
modems, VoIP phones, IP/CCTV cameras. We introduce a methodology and implement
a scalable framework for discovery of vulnerabilities in embedded web
interfaces regardless of the vendor, device, or architecture. To achieve this
goal, our framework performs full system emulation to achieve the execution of
firmware images in a software-only environment, i.e., without involving any
physical embedded devices. Then, we analyze the web interfaces within the
firmware using both static and dynamic tools. We also present some interesting
case-studies, and discuss the main challenges associated with the dynamic
analysis of firmware images and their web interfaces and network services. The
observations we make in this paper shed light on an important aspect of
embedded devices which was not previously studied at a large scale.
We validate our framework by testing it on 1925 firmware images from 54
different vendors. We discover important vulnerabilities in 185 firmware
images, affecting nearly a quarter of vendors in our dataset. These
experimental results demonstrate the effectiveness of our approach
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