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

Combining Static and Dynamic Analysis for Vulnerability Detection

In this paper, we present a hybrid approach for buffer overflow detection in C code. The approach makes use of static and dynamic analysis of the application under investigation. The static part consists in calculating taint dependency sequences (TDS) between user controlled inputs and vulnerable statements. This process is akin to program slice of interest to calculate tainted data- and control-flow path which exhibits the dependence between tainted program inputs and vulnerable statements in the code. The dynamic part consists of executing the program along TDSs to trigger the vulnerability by generating suitable inputs. We use genetic algorithm to generate inputs. We propose a fitness function that approximates the program behavior (control flow) based on the frequencies of the statements along TDSs. This runtime aspect makes the approach faster and accurate. We provide experimental results on the Verisec benchmark to validate our approach.Comment: There are 15 pages with 1 figur

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

IIFA: Modular Inter-app Intent Information Flow Analysis of Android Applications

Android apps cooperate through message passing via intents. However, when apps do not have identical sets of privileges inter-app communication (IAC) can accidentally or maliciously be misused, e.g., to leak sensitive information contrary to users expectations. Recent research considered static program analysis to detect dangerous data leaks due to inter-component communication (ICC) or IAC, but suffers from shortcomings with respect to precision, soundness, and scalability. To solve these issues we propose a novel approach for static ICC/IAC analysis. We perform a fixed-point iteration of ICC/IAC summary information to precisely resolve intent communication with more than two apps involved. We integrate these results with information flows generated by a baseline (i.e. not considering intents) information flow analysis, and resolve if sensitive data is flowing (transitively) through components/apps in order to be ultimately leaked. Our main contribution is the first fully automatic sound and precise ICC/IAC information flow analysis that is scalable for realistic apps due to modularity, avoiding combinatorial explosion: Our approach determines communicating apps using short summaries rather than inlining intent calls, which often requires simultaneously analyzing all tuples of apps. We evaluated our tool IIFA in terms of scalability, precision, and recall. Using benchmarks we establish that precision and recall of our algorithm are considerably better than prominent state-of-the-art analyses for IAC. But foremost, applied to the 90 most popular applications from the Google Playstore, IIFA demonstrated its scalability to a large corpus of real-world apps. IIFA reports 62 problematic ICC-/IAC-related information flows via two or more apps/components

Autonomic computing meets SCADA security

© 2017 IEEE. National assets such as transportation networks, large manufacturing, business and health facilities, power generation, and distribution networks are critical infrastructures. The cyber threats to these infrastructures have increasingly become more sophisticated, extensive and numerous. Cyber security conventional measures have proved useful in the past but increasing sophistication of attacks dictates the need for newer measures. The autonomic computing paradigm mimics the autonomic nervous system and is promising to meet the latest challenges in the cyber threat landscape. This paper provides a brief review of autonomic computing applications for SCADA systems and proposes architecture for cyber security

The approaches to quantify web application security scanners quality: A review

The web application security scanner is a computer program that assessed web application security with penetration testing technique. The benefit of automated web application penetration testing is huge, which web application security scanner not only reduced the time, cost, and resource required for web application penetration testing but also eliminate test engineer reliance on human knowledge. Nevertheless, web application security scanners are possessing weaknesses of low test coverage, and the scanners are generating inaccurate test results. Consequently, experimentations are frequently held to quantitatively quantify web application security scanner's quality to investigate the web application security scanner's strengths and limitations. However, there is a discovery that neither a standard methodology nor criterion is available for quantifying the web application security scanner's quality. Hence, in this paper systematic review is conducted and analysed the methodology and criterion used for quantifying web application security scanners' quality. In this survey, the experiment methodologies and criterions that had been used to quantify web application security scanner's quality is classified and review using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) protocol. The objectives are to provide practitioners with the understanding of methodologies and criterions that available for measuring web application security scanners' test coverage, attack coverage, and vulnerability detection rate, while provides the critical hint for development of the next testing framework, model, methodology, or criterions, to measure web application security scanner quality