Dynamically validating static memory leak warnings

The static code analysis tools are a fundamental instruments to validate the developed code. They allow detecting bugs (as memory leak, accessing arrays out of bounds, etc.. ), structural errors and preventing entire classes of errors. This work refers to the Parasoft's tool It helps developers prevent and eliminate defects using rules tuned to nd code patterns that lead to reliability, performance, and security problems. To be practical, the static analysis must be seamlessly in- tegrated into the team's work- ow. The `continuous static analysis' allows executing automatically the analysis when the new code is released. In this way is possible to realize a further automation in the software development executing the tool during the night or weekend. The time saved to run the tool can be used to analyze and correct the bugs. When you deal with static analysis, it's important to under- line the problem of false positive. It would be attractive if we could develop a tool that could intercept all defects in a given piece of software with certainty. Not all real errors can always be caught, and not all errors caught can always be real. This work presents a quantitative evaluation on the percentile of false positive generated by C++test.

The past decade has witnessed an explosive increase in the scale, intensity and sophistication of cyber attacks. While software vendors have significantly increased their efforts on security, they are almost always playing catch up. As a result, security-conscious organizations and individuals have come to expect their system administrators to deploy an array of tools and techniques to stay a step ahead of the hackers. While developer-oriented security tools rely mainly on static analysis, runtime analysis and policy enforcement are the mechanisms of choice in administrator-oriented tools. Runtime techniques offer increased automation and precision over static analysis, thus addressing the needs of administrators who don't have the time or resources needed to acquire extensive knowledge about the internals of a software system. In this talk, I will begin by summarizing some of the significant advances that have been achieved in the past few years in the context of software vulnerability mitigation, including buffer overflow defenses, and more recently, the impressive results that have been achieved using dynamic information-flow analysis for blocking the most popular exploits today, including SQL and command injection and cross-site scripting. I will then proceed to describe dynamic analysis and enforcement techniques aimed at another high-profile security problem faced today, namely, malware defense. Our initial target in this regard has been on dynamic analysis techniques for extracting high-level models of program behavior. These models could be used in a variety of applications such as intrusion detection, vulnerability analysis and security policy verification. More recently, interesting advances have been made in the context of security policy development, where a combination of static and dynamic analysis techniques have been developed to synthesize low-level, enforceable policies that achieve a high-level goal such as protecting system integrity. Finally, I will conclude the talk with a discussion of some research opportunities and challenges in software security.

Static and dynamic program analysis techniques are important research areas in software security. Static analysis helps us locate vulnerabilities in a software by looking at the source code. Dynamic analysis helps us reason about the behavior of the software from information gathered at run-time. In this thesis, we are focusing on data flow analysis and heap analysis which are key static and dynamic program analysis techniques respectively.
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\nIn the first part of this thesis, we aim at detecting vulnerabilities in Android applications which have capability leaks. The security of the Android platform relies mainly on sandboxing applications and restricting their capabilities such that no application, by default, can perform any operations that would adversely impact other applications, the operating system, or the user. However, a recent research reported that a genuine but vulnerable application may leak its capabilities. When being leveraged, other applications can gain extra capabilities which they are not granted originally. We present DroidChecker, an Android application analyzing tool which searches for the aforementioned vulnerability in Android applications. DroidChecker uses interprocedural control flow graph searching and static taint checking to detect exploitable data paths in an Android application. We analyzed more than 1100 Android applications using DroidChecker and found 6 previously unknown vulnerable applications including the renowned Adobe Photoshop Express application. We also developed a malicious application that exploits the previously unknown vulnerability found in the Adobe Photoshop Express application. We showed that the malicious application, which is not granted any permissions, can access contacts on the phone with just a few lines of code.
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\nIn the second part of this thesis, we explore the use of heap analysis to extract software birthmarks. There are techniques like code obfuscation and watermarking which can make the source code of a program difficult to understand by humans and prove the ownership of the program. However, code obfuscation cannot avoid the source code being copied and a watermark can be defaced. A birthmark is a group of unique characteristics a program possesses that can be used to identify the program. We propose two novel dynamic birthmark systems based on the run-time heap. A dynamic birthmark is one that is extracted when the program is executing. Since it is based on the run-time behavior of the program, semantics-preserving transformations of the code like obfuscation cannot defeat dynamic birthmarks. In this regard, dynamic birthmarks are more robust compared with static birthmarks.
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\nTo the best of our knowledge, these are the first birthmark systems using heap analysis as the underlying technique. The basic idea is to take snapshots of the heap while the program is running. From the snapshots, heap graphs are constructed to model the referencing structure between objects. After going through some filtering and referencing processes, they become the birthmarks. The two birthmark systems have been devised to extract birthmarks for Java programs and JavaScript programs respectively. While the underlying ideas of the two birthmark systems are similar, the differences in nature of the two programming languages led to different implementation designs.

Malware classification plays an important role in tracing the attack sources of computer security. However, existing static analysis methods are fast in classification, but they are inefficient in some malware using packing and obfuscation techniques; the dynamic analysis methods have better universality for packing and obfuscation, but they will cause excessive classification cost. To overcome these shortcomings, in this paper, we propose a classification system Malscore based on the probability scoring and machine learning, which sets the probability threshold to concatenate static analysis (called Phase 1) and dynamic analysis (called Phase 2). The convolutional neural networks with spatial pyramid pooling were used to analyze the grayscale images (static features) in Phase 1, and the variable n-grams and machine learning were used to analyze the native API call sequences (dynamic features) in Phase 2. Malscore combined static analysis with dynamic analysis not only accelerated the static analysis process by taking advantage of the CNN in image recognition but also appeared to be more resilient to obfuscation by the dynamic analysis. Different from other static and dynamic analysis techniques, when malware is detected, due to the fact that malware will most likely be labeled only by static analysis, we could reduce the overheads by dynamically analyzing a few malware that has less obvious features or greater confusion in static analysis. We performed experiments on 174607 malware samples from 63 malware families. The result showed that Malscore achieved 98.82% accuracy for malware classification. Furthermore, Malscore was compared with the method of using static and dynamic analysis. The preprocessing and test time represented a reduction of 59.58% and 61.70%, respectively.

Static and dynamic analysis techniques for bug detection have significantly improved in the last decades, and are today implemented in industry-strength tools and routinely applied by developers. Nevertheless, it is still difficult to deal with bugs located in OTS software, since developers lack the source code and/or knowledge about their internals to fix these bugs. In this paper, we propose an approach for fixing memory leak bugs in OTS software, that leverages dynamic binary analysis tools to find bugs, and binary code rewriting to patch them. Patching will allow to rejuvenate OTS-based software less frequently, thus further improving the availability of applications using this approach. Future work will implement this approach in a prototype, and validate it on real memory leaks found in complex software.

The use of a managed, type-safe languages such as Java in real-time and embedded systems can offer productivity and, in particular, safety and dependability benefits over the dominating unsafe languages at reasonable costs. A JVM that has dynamic memory-management needs to provide an implicit memory-management strategy, that is, for example, a garbage collector (GC) or stack allocation provided by the escape analysis of the JVM's compiler: Explicit management of dynamically allocated memory (i.e., by use of functions such as C's malloc() and free()) is vulnerable to programming errors such as neglected or false memory release operations causing memory leaks or dangling pointers. Such operations have the potential to break the soundness of the type system and are therefore usually not available for strongly typed languages. Type-safe languages in combination with static analyses -- which respect hardware as well as system-specific information -- can efficiently be employed to provide a runtime system including memory management (MM) that is specifically suited to an embedded application on a particular hardware device. In the context of this paper, we present novel memory-management strategy we implemented in our KESO JVM. It is a latency-aware garbage-collection algorithm called LAGC. Also, we introduce the static analyses that can assist LAGC. The application developers have to ensure that there is enough time for the GCs to run. Hardware characteristics such as soft-error proneness of the hardware or the memory layout can also be taken into consideration as demanded by the system configuration. This is achieved by integrating the GCs in the design process of the whole system just as any other user application, which is the reason why this approach is called cooperative memory management. The suggested strategies require reasonably low overhead.

Sound static analyzers over-approximate the input program behaviour and thus imprecisely report many correct properties as potential errors (false warnings). Manual investigation of these warnings is cost intensive and error prone. To get an insight into the causes and explore the effectiveness of current solutions, we analyzed the code structure associated with warnings reported by sound state of the art static analyzers: Polyspace and TCS Embedded Code Analyzer, over six industrial embedded applications. We observed that most of the warnings were due to variables modified inside loops with large or unknown bounds.While earlier techniques have suggested the use of program slicing, abstraction, Iterative Context Extension (ICE) with Bounded Model Checking (BMC) to eliminate false warnings automatically, more recently an effective approach has been proposed called loop abstraction for BMC (LABMC), aimed specially at proving properties using BMC in the presence of loops with large and unknown bounds. Therefore, we experimentally evaluated a combination of program slicing, ICE and LABMC to enable practitioners to eliminate false warnings automatically. This combination successfully identified more than 70% of the static analysis warnings on the applications as false positives. We share the details of our approach and experimentation in this paper.

A secure software demands effective techniques for vulnerability detection during its development cycle. The practice of detecting security flaws before the deployment phase eliminates the risks that vulnerabilities may impose for the company. Static analysis and dynamic analysis techniques offer two complimentary approaches for checking vulnerabilities. Static analysis involves the scanning of source code or binary eliminating the need of executing it. This approach is fast and has no run time overhead. However, static analysis are quite imprecise and generate huge false positives and false negatives. On the other hand, dynamic analysis involves the running of the software. The problem of false positives and negatives is less in case of dynamic analysis because they analyze by running the test cases. But this approach requires large number of test cases to ensure a certain confidence level in detecting security bugs. This paper describes a methodology which integrates the two approaches in a complimentary manner. It adopts the strengths of the two and eliminates their weaknesses. We are currently dealing with buffer overflow vulnerability with pointer aliasing. However the idea can be extended to other vulnerabilities also for e.g memory related errors, race conditions( Time of Check to Time to Use vulnerability), dangling pointer vulnerability, integer errors etc.

Software forms a critical part of our lives today. Verifying software to avoid violations of safety and security properties is a necessary task. It is also imperative to have an assurance that the verification process was correct. We propose a human-centric approach to software verification. This involves enabling human-machine collaboration to detect vulnerabilities and to prove the correctness of the verification. We discuss two classes of vulnerabilities. The first class is Algorithmic Complexity Vulnerabilities (ACV). ACVs are a class of software security vulnerabilities that cause denial-of-service attacks. The description of an ACV is not known a priori. The problem is equivalent to searching for a needle in the haystack when we don't know what the needle looks like. We present a novel approach to detect ACVs in web applications. We present a case study audit from DARPA's Space/Time Analysis for Cybersecurity (STAC) program to illustrate our approach. The second class of vulnerabilities is Memory Leaks. Although the description of the Memory Leak (ML) problem is known, a proof of the correctness of the verification is needed to establish trust in the results. We present an approach inspired by the works of Alan Perlis to compute evidence of the verification which can be scrutinized by a human to prove the correctness of the verification. We present a novel abstraction, the Evidence Graph, that succinctly captures the verification evidence and show how to compute the evidence. We evaluate our approach against ML instances in the Linux kernel and report improvement over the state-of-the-art results. We also present two case studies to illustrate how the Evidence Graph can be used to prove the correctness of the verification.

Detecting performance issues due to suboptimal code during the development process can be a daunting task, especially when it comes to localizing them after noticing performance degradation after deployment. Static analysis has the potential to provide early feedback on performance problems to developers without having to run profilers with expensive (and often unavailable) performance tests. We develop a VSCode tool that integrates the static performance analysis results from Infer via code annotations and decorations (surfacing complexity analysis results in context) and side panel views showing details and overviews (enabling explainability of the results). Additionally, we design our system for interactivity to allow for more responsiveness to code changes as they happen. We evaluate the efficacy of our tool by measuring the overhead that the static performance analysis integration introduces in the development workflow. Further, we report on a case study that illustrates how our system can be used to reason about software performance in the context of a real performance bug in the ElasticSearch open-source project. Demo video: https://www.youtube.com/watch?v=-GqPb_YZMOs Repository: https://github.com/ipa-lab/vscode-infer-performance

It is well accepted that pointers are a common source of memory anomalies such as loosing references to dynamic records without deallocating them (also known as memory leaks). This paper presents a novel pointer analysis framework that detects memory leaks by statically analyzing the behavior of programs. Our approach is based on symbolic evaluation of programs. Symbolic evaluation is an advanced static symbolic analysis that is centered around symbolic variable values, assumptions about and constraints between variable values, and control flow information (path conditions). As part of symbolic evaluation we introduce a new symbolic heap algebra for modeling heap operations. Predicates — defined over the program's input — are derived which allow to detect memory leaks. Our approach goes beyond previous work in the field of statically detecting memory leaks by considering also path conditions which increases the accuracy of our results, symbolically modeling heap data structures and heap operations. Examples are used to illustrate the effectiveness of our approach.

Some trade-offs in reducing the overhead of assertion run-time checks via static analysis

Memory leaks in web applications are pervasive and difficult to debug. Leaks degrade responsiveness by increasing garbage collection costs and can even lead to browser tab crashes. Previous leak detection approaches designed for conventional applications are ineffective in the browser environment. Tracking down leaks currently requires intensive manual effort by web developers, which is often unsuccessful. This paper introduces BLEAK (Browser Leak debugger), the first system for automatically debugging memory leaks in web applications. BLEAK'S algorithms leverage the observation that in modern web applications, users often repeatedly return to the same (approximate) visual state (e.g., the inbox view in Gmail). Sustained growth between round trips is a strong indicator of a memory leak. To use BLEAK, a developer writes a short script (17-73 LOC on our benchmarks) to drive a web application in round trips to the same visual state. BLEAK then automatically generates a list of leaks found along with their root causes, ranked by return on investment. Guided by BLEAK, we identify and fix over 50 memory leaks in popular libraries and apps including Airbnb, AngularJS, Google Analytics, Google Maps SDK, and jQuery. BLEAK'S median precision is 100%; fixing the leaks it identifies reduces heap growth by an average of 94%, saving from 0.5MB to 8MB per round trip.

The growing demand for productivity in hardware development opens up new opportunities for applying programming language (PL) techniques to hardware description languages (HDLs). Chisel, a leading agile HDL, embraces this shift by leveraging modern PL features to enhance hardware design productivity. However, verification for Chisel remains a major productivity bottleneck, requiring substantial time and manual effort. To address this issue, we advocate the use of static analysis —a technique proven well-suited to agile development workflows in software—for lightweight Chisel verification. This work establishes a theoretical foundation for Chisel static analysis. At its core is λ C , a formal core calculus of ChAIR (a Chisel-specific intermediate representation for analysis). λ C is the first formalism that captures the essence of Chisel while being deliberately minimal to ease rigorous reasoning about static analysis built on λ C . We prove key properties of λ C that reflect real hardware characteristics, which in turn offer a form of retrospective validation for its design. On the basis of λ C , we define and formalize the hardware value flow analysis (HVFA) problem, which underpins our static analyses for critical Chisel verification tasks, including bug detection and security analysis. We then propose a synchronized fixed-point solution to the HVFA problem, featuring hardware-specific treatment of the synchronous behavior of clock-driven hardware registers—the essential feature of Chisel programs. We further prove key theorems establishing the guarantees and limitations of our solution. As a proof of concept, we develop ChiSA (30K+ LoC)—the first Chisel static analyzer that can analyze intricate hardware value flows to enable lightweight analyses for critical Chisel verification tasks such as bug detection and security analysis. To facilitate thorough evaluation of both ChiSA and future work, we provide ChiSABench (11M+ LoC), a comprehensive benchmark for Chisel static analysis. Our evaluation on ChiSABench demonstrates that ChiSA offers an effective and significantly more lightweight approach for critical Chisel verification tasks, especially on large and complex real-world designs. For example, ChiSA identified 69 violable developer-inserted assertions in large-scale Chisel designs (9.7M+ LoC) in under 200 seconds—eight of which were recognized by developers and scheduled for future fixes—and detected all 60 information-leak vulnerabilities in the well-known TrustHub benchmark (1.1M+ LoC) in just one second—outperforming state-of-the-art Chisel approaches like ChiselTest’s bounded model checking and ChiselFlow’s secure type system. These results underscore the high promise of static analysis for lightweight Chisel verification. To encourage continued research and innovation, we will fully open-source ChiSA (30K+ LoC) and ChiSABench (11M+ LoC).

Secure software engineering has emerged in recent decades by encouraging the idea of software security has to be an integral part of all the phases of the software development lifecycle. As a result, each phase of the lifecycle is associated with security-specific best practices such as threat modeling and static code analysis. It was observed that various artifacts (i.e., security requirements, architectural flaws, bug reports, security test cases) generated as a result of security best practices tend to be segregated. This creates a significant barrier to resolve the security issues at the implementation phase since most of them are originated in the design phase. In order to address this issue, this paper presents a knowledge-modeling based approach to semantically infer the associations between architectural level security flaws and code-level security bugs, which is manually tedious. Threat modeling and static analysis are used to identify security flaws and security bugs, respectively. The case study based experimental results revealed that the architectural level security flaws have a significant impact on originating security bugs in the code level. Besides, the evaluation results confirmed the scalability of the proposed approach to large-scale industrial software products.