Biblio

Smart contracts are usually financial-related, which makes them attractive attack targets. Many static analysis tools have been developed to facilitate the contract audit process, but not all of them take account of two special features of smart contracts: (1) The external variables, like time, are constrained by real-world factors; (2) The internal variables persist between executions. Since these features import implicit constraints into contracts, they significantly affect the performance of static tools, such as causing errors in reachability analysis and resulting in false positives. In this paper, we conduct a systematic study on implicit constraints from three aspects. First, we summarize the implicit constraints in smart contracts. Second, we evaluate the impact of such constraints on the state-of-the-art static tools. Third, we propose a lightweight but effective mitigation method named ConSym to deal with such constraints and integrate it into OSIRIS. The evaluation result shows that ConSym can filter out 96% of false positives and reduce false negatives by two-thirds.

Cyber risk assessments are an essential process for analyzing and prioritizing security issues. Unfortunately, many risk assessment methodologies are marred by human subjectivity, resulting in non-repeatable, inconsistent findings. The absence of repeatable and consistent results can lead to suboptimal decision making with respect to cyber risk reduction. There is a pressing need to reduce cyber risk assessment uncertainty by using tools that use well defined inputs, producing well defined results. This paper presents Automated Vulnerability and Risk Analysis (AVRA), an end-to-end process and tool for identifying and exploiting vulnerabilities, designed for use in cyber risk assessments. The approach presented is more comprehensive than traditional vulnerability scans due to its analysis of an entire network, integrating both host and network information. AVRA automatically generates a detailed model of the network and its individual components, which is used to create an attack graph. Then, AVRA follows individual attack paths, automatically launching exploits to reach a particular objective. AVRA was successfully tested within a virtual environment to demonstrate practicality and usability. The presented approach and resulting system enhances the cyber risk assessment process through rigor, repeatability, and objectivity.

Software has become an integral part of every industry and organization. Due to improvement in technology and lack of expertise in coding techniques, software vulnerabilities are increasing day-by-day in the software development sector. The time gap between the identification of the vulnerabilities and their automated exploit attack is decreasing. This gives rise to the need for detection and prevention of security risks and development of secure software. Earlier the security risk is identified and corrected the better it is. Developers needs a framework which can report the security flaws in their system and reduce the chances of exploitation of these flaws by some malicious user. Common Vector Scoring System (CVSS) is a De facto metrics system used to assess the exploitability of vulnerabilities. CVSS exploitability measures use subjective values based on the views of experts. It considers mainly two factors, Access Vector (AV) and Authentication (AU). CVSS does not specify on what basis the third-factor Access Complexity (AC) is measured, whether or not it considers software properties. Our objective is to come up with a framework that automates the process of identifying vulnerabilities using software structural properties. These properties could be attack entry points, vulnerability locations, presence of dangerous system calls, and reachability analysis. This framework has been tested on two open source softwares - Apache HTTP server and Mozilla Firefox.

In this paper, we study system security against Advanced Persistent Threats (APTs). APTs are stealthy and persistent but APTs interact with system and introduce information flows in the system as data-flow and control-flow commands. Dynamic Information Flow Tracking (DIFT) is a promising detection mechanism against APTs which taints suspicious input sources in the system and performs online security analysis when a tainted information is used in unauthorized manner. Our objective in this paper is to model DIFT that handle data-flow and conditional branches in the program that arise from control-flow commands. We use game theoretic framework and provide the first analytical model of DIFT with data-flow and conditional-branch tracking. Our game model which is an undiscounted infinite-horizon stochastic game captures the interaction between APTs and DIFT and the notion of conditional branching. We prove that the best response of the APT is a maximal reachability probability problem and provide a polynomial-time algorithm to find the best response by solving a linear optimization problem. We formulate the best response of the defense as a linear optimization problem and show that an optimal solution to the linear program returns a deterministic optimal policy for the defense. Since finding Nash equilibrium for infinite-horizon undiscounted stochastic games is computationally difficult, we present a nonlinear programming based polynomial-time algorithm to find an E-Nash equilibrium. Finally, we perform experimental analysis of our algorithm on real-world data for NetRecon attack augmented with conditional branching.

Denial of Service (DoS) attacks have been a serious security concern, as no service is, in principle, protected against them. Although a Dolev-Yao intruder with unlimited resources can trivially render any service unavailable, DoS attacks do not necessarily have to be carried out by such (extremely) powerful intruders. It is useful in practice and more challenging for formal protocol verification to determine whether a service is vulnerable even to resource-bounded intruders that cannot generate or intercept arbitrary large volumes of traffic. This paper proposes a novel, more refined intruder model where the intruder can only consume at most some specified amount of resources in any given time window. Additionally, we propose protocol theories that may contain timeouts and specify service resource usage during protocol execution. In contrast to the existing resource-conscious protocol verification models, our model allows finer and more subtle analysis of DoS problems. We illustrate the power of our approach by representing a number of classes of DoS attacks, such as, Slow, Asymmetric and Amplification DoS attacks, exhausting different types of resources of the target, such as, number of workers, processing power, memory, and network bandwidth. We show that the proposed DoS problem is undecidable in general and is PSPACE-complete for the class of resource-bounded, balanced systems. Finally, we implemented our formal verification model in the rewriting logic tool Maude and analyzed a number of DoS attacks in Maude using Rewriting Modulo SMT in an automated fashion.

Enforcement of hypersafety security policies such as noninterference can be achieved through Secure Multi-Execution (SME). While this is typically very resource-intensive, more efficient solutions such as Demand-Driven Secure Multi-Execution (DDSME) exist. Here, the resource requirements are reduced by restricting multi-execution enforcement to critical sections in the code. However, the current solution requires manual binary analysis. In this paper, we propose a fully automatic critical section analysis. Our analysis extracts a context-sensitive boundary of all nodes that handle information from the reachability relation implied by the control-flow graph. We also provide evaluation results, demonstrating the correctness and acceleration of DDSME with our analysis.

By accurately measuring risk for enterprise networks, attack graphs allow network defenders to understand the most critical threats and select the most effective countermeasures. This paper describes substantial enhancements to the NetSPA attack graph system required to model additional present-day threats (zero-day exploits and client-side attacks) and countermeasures (intrusion prevention systems, proxy firewalls, personal firewalls, and host-based vulnerability scans). Point-to-point reachability algorithms and structures were extensively redesigned to support "reverse" reachability computations and personal firewalls. Host-based vulnerability scans are imported and analyzed. Analysis of an operational network with 84 hosts demonstrates that client-side attacks pose a serious threat. Experiments on larger simulated networks demonstrated that NetSPA's previous excellent scaling is maintained. Less than two minutes are required to completely analyze a four-enclave simulated network with more than 40,000 hosts protected by personal firewalls.

Today, computing on various Android devices is pervasive. However, growing security vulnerabilities and attacks in the Android ecosystem constitute various threats through user apps. Taint analysis is a common technique for defending against these threats, yet it suffers from challenges in attaining practical simultaneous scalability and effectiveness. This paper presents a novel approach to fast and precise taint checking, called incremental taint analysis, by exploiting the evolving nature of Android apps. The analysis narrows down the search space of taint checking from an entire app, as conventionally addressed, to the parts of the program that are different from its previous versions. This technique improves the overall efficiency of checking multiple versions of the app as it evolves. We have implemented the techniques as a tool prototype, EVOTAINT, and evaluated our analysis by applying it to real-world evolving Android apps. Our preliminary results show that the incremental approach largely reduced the cost of taint analysis, by 78.6% on average, yet without sacrificing the analysis effectiveness, relative to a representative precise taint analysis as the baseline.

For LTI control systems, we provide mathematical tools - in terms of Linear Matrix Inequalities - for computing outer ellipsoidal bounds on the reachable sets that attacks can induce in the system when they are subject to the physical limits of the actuators. Next, for a given set of dangerous states, states that (if reached) compromise the integrity or safe operation of the system, we provide tools for designing new artificial limits on the actuators (smaller than their physical bounds) such that the new ellipsoidal bounds (and thus the new reachable sets) are as large as possible (in terms of volume) while guaranteeing that the dangerous states are not reachable. This guarantees that the new bounds cut as little as possible from the original reachable set to minimize the loss of system performance. Computer simulations using a platoon of vehicles are presented to illustrate the performance of our tools.

Data dependency flow have been reformulated as Context Free Grammar (CFG) reachability problem, and the idea was explored in detection of some web vulnerabilities, particularly Cross Site Scripting (XSS) and Access Control. However, reformulation of SQL Injection Vulnerability (SQLIV) detection as grammar reachability problem has not been investigated. In this paper, concepts of data dependency flow was used to reformulate SQLIVs detection as a CFG reachability problem. The paper, consequently defines reachability analysis strategy for SQLIVs detection.

We consider the problem of translating a deterministic \textbackslashemph\simulation model\ (like Matlab-Simunk, Modelica or Ptolemy models) into a \textbackslashemphěrification model\ expressed by a network of hybrid automata. The goal is to verify safety using reachability analysis on the verification model. Simulation models typically use transitions with urgent semantics, which must be taken as soon as possible. Urgent transitions also make it possible to decompose systems that would otherwise need to be modeled with a monolithic hybrid automaton. In this paper, we include urgent transitions in our verification models and propose a suitable adaptation of our reachability algorithm. However, the simulation model, due to its imperfections, may be unsafe even though the corresponding hybrid automata are safe. Conversely, set-based reachability may not be able to show safety of an ideal formal model, since complex dynamics necessarily entail overapproximations. Taken as a whole, the formal modeling and verification process can both falsely claim safety and fail to show safety of the concrete system. We address this inconsistency by relaxing the model as follows. The standard semantics of hybrid automata is a mathematical idealization, where reactions are considered to be instantaneous and physical measurements infinitely precise. We propose semantics that relax these assumptions, where guard conditions are sampled in discrete time and admit measurement errors. The relaxed semantics can be translated to an equivalent relaxed model in standard semantics. The relaxed model is realistic in the sense that it can be implemented on hardware fast and precise enough, and in a way that safety is preserved. Finally, we show that overapproximative reachability analysis can show safety of relaxed models, which is not the case in general.

Operating system kernel is the de facto trusted computing base for most computer systems. To secure the OS kernel, many security mechanisms, e.g., kASLR and StackGuard, have been increasingly deployed to defend against attacks (e.g., code reuse attack). However, the effectiveness of these protections has been proven to be inadequate-there are many information leak vulnerabilities in the kernel to leak the randomized pointer or canary, thus bypassing kASLR and StackGuard. Other sensitive data in the kernel, such as cryptographic keys and file caches, can also be leaked. According to our study, most kernel information leaks are caused by uninitialized data reads. Unfortunately, existing techniques like memory safety enforcements and dynamic access tracking tools are not adequate or efficient enough to mitigate this threat. In this paper, we propose UniSan, a novel, compiler-based approach to eliminate all information leaks caused by uninitialized read in the OS kernel. UniSan achieves this goal using byte-level, flow-sensitive, context-sensitive, and field-sensitive initialization analysis and reachability analysis to check whether an allocation has been fully initialized when it leaves kernel space; if not, it automatically instruments the kernel to initialize this allocation. UniSan's analyses are conservative to avoid false negatives and are robust by preserving the semantics of the OS kernel. We have implemented UniSan as passes in LLVM and applied it to the latest Linux kernel (x86\_64) and Android kernel (AArch64). Our evaluation showed that UniSan can successfully prevent 43 known and many new uninitialized data leak vulnerabilities. Further, 19 new vulnerabilities in the latest kernels have been confirmed by Linux and Google. Our extensive performance evaluation with LMBench, ApacheBench, Android benchmarks, and the SPEC benchmarks also showed that UniSan imposes a negligible performance overhead.