Search for: All records

Fuzzing reliably and efficiently finds bugs in software, including operating system kernels. In general, higher code coverage leads to the discovery of more bugs. This is why most existing kernel fuzzers adopt strategies to generate a series of inputs that attempt to greedily maximize the amount of code that they exercise. However, simply executing code may not be sufficient to reveal bugs that require specific sequences of actions. Synthesizing inputs to trigger such bugs depends on two aspects: (i) the actions the executed code takes, and (ii) the order in which those actions are taken. An action is a high-level operation, such as a heap allocation, that is performed by the executed code and has a specific semantic meaning. ACTOR, our action-guided kernel fuzzing framework, deviates from traditional methods. Instead of focusing on code coverage optimization, our approach generates fuzzer programs (inputs) that leverage our understanding of triggered actions and their temporal relationships. Specifically, we first capture actions that potentially operate on shared data structures at different times. Then, we synthesize programs using those actions as building blocks, guided by bug templates expressed in our domain-specific language. We evaluated ACTOR on four different versions of the Linux kernel, including two well-tested and frequently updated long-term (5.4.206, 5.10.131) versions, a stable (5.19), and the latest (6.2-rc5) release. Our evaluation revealed a total of 41 previously unknown bugs, of which 9 have already been fixed. Interestingly, 15 (36.59%) of them were discovered in less than a day. 

Human analysts must reverse engineer binary programs as a prerequisite for a number of security tasks, such as vulnerability analysis, malware detection, and firmware re-hosting. Existing studies of human reversers and the processes they follow are limited in size and often use qualitative metrics that require subjective evaluation. In this paper, we reframe the problem of reverse engineering binaries as the problem of perfect decompilation, which is the process of recovering, from a binary program, source code that, when compiled, produces binary code that is identical to the original binary. This gives us a quantitative measure of understanding, and lets us examine the reversing process programmatically. We developed a tool, called Decomperson, that supported a group of reverse engineers during a large-scale security competition designed to collect information about the participants' reverse engineering process, with the well-defined goal of achieving perfect decompilation. Over 150 people participated, and we collected more than 35,000 code submissions, the largest manual reverse engineering dataset to date. This includes snapshots of over 300 successful perfect decompilation attempts. In this paper, we show how perfect decompilation allows programmatic analysis of such large datasets, providing new insights into the reverse engineering process. 

Regular expressions (regexps) are a convenient way for programmers to express complex string searching logic. Sev- eral popular programming languages expose an interface to a regexp matching subsystem, either by language-level primi- tives or through standard libraries. The implementations be- hind these matching systems vary greatly in their capabilities and running-time characteristics. In particular, backtracking matchers may exhibit worst-case running-time that is either linear, polynomial, or exponential in the length of the string being searched. Such super-linear worst-case regexps expose applications to Regular Expression Denial-of-Service (Re- DoS) when inputs can be controlled by an adversarial attacker. In this work, we investigate the impact of ReDoS in back- tracking engines, a popular type of engine used by most programming languages. We evaluate several existing tools against a dataset of broadly collected regexps, and find that despite extensive theoretical work in this field, none are able to achieve both high precision and high recall. To address this gap in existing work, we develop REGULATOR, a novel dy- namic, fuzzer-based analysis system for identifying regexps vulnerable to ReDoS. We implement this system by directly instrumenting a popular backtracking regexp engine, which increases the scope of supported regexp syntax and features over prior work. Finally, we evaluate this system against three common regexp datasets, and demonstrate a seven-fold in- crease in true positives discovered when comparing against existing tools. 

This paper presents SAILFISH, a scalable system for automatically finding state-inconsistency bugs in smart contracts. To make the analysis tractable, we introduce a hybrid approach that includes (i) a light-weight exploration phase that dramatically reduces the number of instructions to analyze, and (ii) a precise refinement phase based on symbolic evaluation guided by our novel value-summary analysis, which generates extra constraints to over-approximate the side effects of whole-program execution, thereby ensuring the precision of the symbolic evaluation. We developed a prototype of SAILFISH and evaluated its ability to detect two state-inconsistency flaws, viz., reentrancy and transaction order dependence (TOD) in Ethereum smart contracts. Our experiments demonstrate the efficiency of our hybrid approach as well as the benefit of the value summary analysis. In particular, we show that SAILFISH outperforms five state-of the-art smart contract analyzers (SECURIFY, MYTHRIL, OYENTE, SEREUM and VANDAL) in terms of performance, and precision. In total, SAILFISH discovered 47 previously unknown vulnerable smart contracts out of 89,853 smart contracts from ETHERSCAN. 

Security monitoring systems detect potentially malicious activities in IT infrastructures, by either looking for known signatures or for anomalous behaviors. Security operators investigate these events to determine whether they pose a threat to their organization. In many cases, a single event may be insufficient to determine whether certain activity is indeed malicious. Therefore, a security operator frequently needs to correlate multiple events to identify if they pose a real threat. Unfortunately, the vast number of events that need to be correlated often overload security operators, forcing them to ignore some events and, thereby, potentially miss attacks. This work studies how to automatically correlate security events and, thus, automate parts of the security operator workload. We design and evaluate DEEPCASE, a system that leverages the context around events to determine which events require further inspection. This approach reduces the number of events that need to be inspected. In addition, the context provides valuable insights into why certain events are classified as malicious. We show that our approach automatically filters 86.72% of the events and reduces the manual workload of security operators by 90.53%, while underestimating the risk of potential threats in less than 0.001% of cases. 

Malicious software (malware) poses a significant threat to the security of our networks and users. In the ever-evolving malware landscape, Excel 4.0 Office macros (XL4) have recently become an important attack vector. These macros are often hidden within apparently legitimate documents and under several layers of obfuscation. As such, they are difficult to analyze using static analysis techniques. Moreover, the analysis in a dynamic analysis environment (a sandbox) is challenging because the macros execute correctly only under specific environmental conditions that are not always easy to create. This paper presents SYMBEXCEL, a novel solution that leverages symbolic execution to deobfuscate and analyze Excel 4.0 macros automatically. Our approach proceeds in three stages: (1) The malicious document is parsed and loaded in memory; (2) Our symbolic execution engine executes the XL4 formulas; and (3) Our Engine concretizes any symbolic values encountered during the symbolic exploration, therefore evaluating the execution of each macro under a broad range of (meaningful) environment configurations. SYMBEXCEL significantly outperforms existing deobfuscation tools, allowing us to reliably extract Indicators of Compromise (IoCs) and other critical forensics information. Our experiments demonstrate the effectiveness of our approach, especially in deobfuscating novel malicious documents that make heavy use of environment variables and are often not identified by commercial anti-virus software. 

Exploring many execution paths in a binary program is essential to discover new vulnerabilities. Dynamic Symbolic Execution (DSE) is useful to trigger complex input conditions and enables an accurate exploration of a program while providing extensive crash replayability and semantic insights. However, scaling this type of analysis to complex binaries is difficult. Current methods suffer from the path explosion problem, despite many attempts to mitigate this challenge (e.g., by merging paths when appropriate). Still, in general, this challenge is not yet surmounted, and most bugs discovered through such techniques are shallow. We propose a novel approach to address the path explosion problem: A smart triaging system that leverages supervised machine learning techniques to replicate human expertise, leading to vulnerable path discovery. Our approach monitors the execution traces in vulnerable programs and extracts relevant features—register and memory accesses, function complexity, system calls—to guide the symbolic exploration. We train models to learn the patterns of vulnerable paths from the extracted features, and we leverage their predictions to discover interesting execution paths in new programs. We implement our approach in a tool called SyML, and we evaluate it on the Cyber Grand Challenge (CGC) dataset—a well-known dataset of vulnerable programs—and on 3 real-world Linux binaries. We show that the knowledge collected from the analysis of vulnerable paths, without any explicit prior knowledge about vulnerability patterns, is transferrable to unseen binaries, and leads to outperforming prior work in path prioritization by triggering more, and different, unique vulnerabilities. 

Given the increasing ubiquity of online embedded devices, analyzing their firmware is important to security, privacy, and safety. The tight coupling between hardware and firmware and the diversity found in embedded systems makes it hard to perform dynamic analysis on firmware. However, firmware developers regularly develop code using abstractions, such as Hardware Abstraction Layers (HALs), to simplify their job. We leverage such abstractions as the basis for the re-hosting and analysis of firmware. By providing high-level replacements for HAL functions (a process termed High-Level Emulation – HLE), we decouple the hardware from the firmware. This approach works by first locating the library functions in a firmware sample, through binary analysis, and then providing generic implementations of these functions in a full-system emulator. We present these ideas in a prototype system, HALucinator, able to re-host firmware, and allow the virtual device to be used normally. First, we introduce extensions to existing library matching techniques that are needed to identify library functions in binary firmware, to reduce collisions, and for inferring additional function names. Next, we demonstrate the re-hosting process, through the use of simplified handlers and peripheral models, which make the process fast, flexible, and portable between firmware samples and chip vendors. Finally, we demonstrate the practicality of HLE for security analysis, by supplementing HALucinator with the American Fuzzy Lop fuzzer, to locate multiple previously-unknown vulnerabilities in firmware middleware libraries. 

Machine learning techniques are widely used in addition to signatures and heuristics to increase the detection rate of anti-malware software, as they automate the creation of detection models, making it possible to handle an ever-increasing number of new malware samples. In order to foil the analysis of anti-malware systems and evade detection, malware uses packing and other forms of obfuscation. However, few realize that benign applications use packing and obfuscation as well, to protect intellectual property and prevent license abuse. In this paper, we study how machine learning based on static analysis features operates on packed samples. Malware researchers have often assumed that packing would prevent machine learning techniques from building effective classifiers. However, both industry and academia have published results that show that machine-learning-based classifiers can achieve good detection rates, leading many experts to think that classifiers are simply detecting the fact that a sample is packed, as packing is more prevalent in malicious samples. We show that, different from what is commonly assumed, packers do preserve some information when packing programs that is “useful” for malware classification. However, this information does not necessarily capture the sample’s behavior. We demonstrate that the signals extracted from packed executables are not rich enough for machine-learning-based models to (1) generalize their knowledge to operate on unseen packers, and (2) be robust against adversarial examples. We also show that a na¨ıve application of machine learning techniques results in a substantial number of false positives, which, in turn, might have resulted 

Malware detection plays a vital role in computer security. Modern machine learning approaches have been centered around domain knowledge for extracting malicious features. However, many potential features can be used, and it is time consuming and difficult to manually identify the best features, especially given the diverse nature of malware. In this paper, we propose Neurlux, a neural network for malware detection. Neurlux does not rely on any feature engineering, rather it learns automatically from dynamic analysis reports that detail behavioral information. Our model borrows ideas from the field of document classification, using word sequences present in the reports to predict if a report is from a malicious binary or not. We investigate the learned features of our model and show which components of the reports it tends to give the highest importance. Then, we evaluate our approach on two different datasets and report formats, showing that Neurlux improves on the state of the art and can effectively learn from the dynamic analysis reports. Furthermore, we show that our approach is portable to other malware analysis environments and generalizes to different datasets.