OS fuzzers primarily test the system call interface between the OS kernel and user-level applications for security vulnerabilities. The effectiveness of evolutionary OS fuzzers depends heavily on the quality and diversity of their seed system call sequences. However, generating good seeds for OS fuzzing is a hard problem as the behavior of each system call depends heavily on the OS kernel state created by the previously executed system calls. Therefore, popular evolutionary OS fuzzers often rely on hand-coded rules for generating valid seed sequences of system calls that can bootstrap the fuzzing process. Unfortunately, this approach severely restricts the diversity of the seed system call sequences and therefore limits the effectiveness of the fuzzers. In this paper, we develop MoonShine, a novel strategy for distilling seeds for OS fuzzers from system call traces of real-world programs while still maintaining the dependencies across the system calls. MoonShine leverages light-weight static analysis for efficiently detecting dependencies across different system calls. We designed and implemented MoonShine as an extension to Syzkaller, a state-of-the-art evolutionary fuzzer for the Linux kernel. Starting from traces containing 2.8 million system calls gathered from 3,220 real-world programs, MoonShine distilled down to just over 14,000 calls while preserving 86% of the original code coverage. Using these distilled seed system call sequences, MoonShine was able to improve Syzkaller’s achieved code coverage for the Linux kernel by 13% on average. MoonShine also found 14 new vulnerabilities in the Linux kernel that were not found by Syzkaller.
Y. Li et al., “V-Fuzz: Vulnerability-Oriented Evolutionary Fuzzing” […we design and implement a vulnerability-oriented evolutionary fuzzing prototype named V-Fuzz, which aims to find bugs efficiently and quickly in a limited time…]https://t.co/J4WuZ7RPTS
V. Manes et al., “Fuzzing: Art, Science, and Engineering” […To help preserve and bring coherence to the vast literature of fuzzing, this paper presents a unified, general-purpose model of fuzzing together with a taxonomy of the current literature…]https://t.co/cw98uPLKjB
Angora: Efficient Fuzzing by Principled Search Peng Chen, Hao Chen (Submitted on 4 Mar 2018 (v1), last revised 27 Mar 2018 (this version, v2))
Fuzzing is a popular technique for finding software bugs. However, the performance of the state-of-the-art fuzzers leaves a lot to be desired. Fuzzers based on symbolic execution produce quality inputs but run slow, while fuzzers based on random mutation run fast but have difficulty producing quality inputs. We propose Angora, a new mutation-based fuzzer that outperforms the state-of-the-art fuzzers by a wide margin. The main goal of Angora is to increase branch coverage by solving path constraints without symbolic execution. To solve path constraints efficiently, we introduce several key techniques: scalable byte-level taint tracking, context-sensitive branch count, search based on gradient descent, and input length exploration. On the LAVA-M data set, Angora found almost all the injected bugs, found more bugs than any other fuzzer that we compared with, and found eight times as many bugs as the second-best fuzzer in the program who. Angora also found 103 bugs that the LAVA authors injected but could not trigger. We also tested Angora on eight popular, mature open source programs. Angora found 6, 52, 29, 40 and 48 new bugs in file, jhead, nm, objdump and size, respectively. We measured the coverage of Angora and evaluated how its key techniques contribute to its impressive performance.
Touching the Untouchables: Dynamic Security Analysis of the LTE Control Plane
Hongil Kim ,Jiho Lee , Eunkyu Lee ,Yongdae Kim
This paper presents our extensive investigation of the security aspects of control plane procedures based on dynamic testing of the control components in operational Long Term Evolution (LTE) networks. For dynamic testing in LTE networks, we implemented a semi-automated testing tool, named LTEFuzz, by using open-source LTE software over which the user has full control. We systematically generated test cases by defining three basic security properties by closely analyzing the standards. Based on the security property, LTEFuzz generates and sends the test cases to a target network, and classifies the problematic behavior by only monitoring the device-side logs. Accordingly, we uncovered 36 vulnerabilities, which have not been disclosed previously. These findings are categorized into five types: Improper handling of (1) unprotected initial procedure, (2) crafted plain requests, (3) messages with invalid integrity protection, (4) replayed messages, and (5) security procedure bypass. We confirmed those vulnerabilities by demonstrating proof-of-concept attacks against operational LTE networks. The impact of the attacks is to either deny LTE services to legitimate users, spoof SMS messages, or eavesdrop/manipulate user data traffic. Precise root cause analysis and potential countermeasures to address these problems are presented as well. Cellular carriers were partially involved to maintain ethical standards as well as verify our findings in commercial LTE networks.
It looks like the code for Neuzz, a very cool new approach to fuzzing that uses deep neural networks to predict the coverage map for an input and then uses its gradients to focus fuzzing effort, has been released! https://t.co/i5QmRApvW1
Fuzzing has become the de facto standard technique for finding software vulnerabilities. However, even state-of-the-art fuzzers are not very efficient at finding hard-to-trigger software bugs. Most popular fuzzers use evolutionary guidance to generate inputs that can trigger different bugs. Such evolutionary algorithms, while fast and simple to implement, often get stuck in fruitless sequences of random mutations. Gradient-guided optimization presents a promising alternative to evolutionary guidance. Gradient-guided techniques have been shown to significantly outperform evolutionary algorithms at solving high-dimensional structured optimization problems in domains like machine learning by efficiently utilizing gradients or higher-order derivatives of the underlying function. However, gradient-guided approaches are not directly applicable to fuzzing as real-world program behaviors contain many discontinuities, plateaus, and ridges where the gradient-based methods often get stuck. We observe that this problem can be addressed by creating a smooth surrogate function approximating the discrete branching behavior of target program. In this paper, we propose a novel program smoothing technique using surrogate neural network models that can incrementally learn smooth approximations of a complex, real-world program’s branching behaviors. We further demonstrate that such neural network models can be used together with gradient-guided input generation schemes to significantly improve the fuzzing efficiency. Our extensive evaluations demonstrate that NEUZZ significantly outperforms 10 state-of-the-art graybox fuzzers on 10 real-world programs both at finding new bugs and achieving higher edge coverage. NEUZZ found 31 unknown bugs that other fuzzers failed to find in 10 real world programs and achieved 3X more edge coverage than all of the tested graybox fuzzers for 24 hours running.
One of the best papers on automated vulnerability discovery I read this year – lots of really clever, practical improvements to hybrid fuzzing/concolic execution. And it's now open source 😀 https://t.co/hP9epyLBeghttps://t.co/U7I4asLa8e
[…]Unicorn Mode works by implementing the block-edge instrumentation that AFL’s QEMU Mode normally does into Unicorn Engine. Basically, AFL will use block coverage information from any emulated code snippet to drive its input generation. The whole idea revolves around proper construction of a Unicorn-based test harness, as shown in the figure below:
fastboot oem vuln: Android Bootloader Vulnerabilities in Vendor Customizations: We discuss the fastboot interface of the Android bootloader, an area of fragmentation in Android devices. We then present a variety of vulnerabilities we have found across multiple Android devices. Most notable ones include Secure Boot & Device Locking bypasses in the Motorola and OnePlus 3/3T bootloaders. Another critical flaw in OnePlus 3/3T enables easy attacks by malicious chargers – the only prerequisite is a powered-off device to be connected. An unexpected attack vector in Nexus 9 is also shown – malicious headphones. Other discovered weaknesses allow for data exfiltration (including a memory dumping of a Nexus 5X device), enablement of hidden functionality such as access to the device’s modem diagnostics and AT interfaces , and attacks against internal System-on-Chips (SoCs) found on the Nexus 9 board.
abootool: Simple fuzzer for discovering hidden fastboot gems. Modus Operandi: Based on static knowledge (strings fetched from available bootloader images), dynamically fuzz for hidden fastboot OEM commands.
s a n d s i f t e r : the x86 processor fuzzer The sandsifter audits x86 processors for hidden instructions and hardware bugs, by systematically generating machine code to search through a processor’s instruction set, and monitoring execution for anomalies. Sandsifter has uncovered secret processor instructions from every major vendor; ubiquitous software bugs in disassemblers, assemblers, and emulators; flaws in enterprise hypervisors; and both benign and security-critical hardware bugs in x86 chips. With the multitude of x86 processors in existence, the goal of the tool is to enable users to check their own systems for hidden instructions and bugs.[…]
NEZHA is an evolutionary-based efficient and domain-independent differential testing framework developed at Columbia University. NEZHA exploits the behavioral asymmetries between multiple test programs to focus on inputs that are more likely to trigger semantic bugs. NEZHA features several runtime diversity-promoting metrics used to generate inputs for multi-app differential testing.[…]
fuzzer-test-suite: This is a set of tests (benchmarks) for fuzzing engines (fuzzers). The goal of this project is to have a set of fuzzing benchmakrs derived from real-life libraries that have interesting bugs, hard-to-find code paths, or other challenges for bug finding tools. The current version supports libFuzzer, in future versions we exect to support AFL and potentially other fuzzers. […]
“KernelFuzzer: This is the core Kernel Fuzzer, with example library calls and Syscalls to start fuzzing Windows. The fuzzer has been tested on Windows 7 / 10, OS X and QNX. […] See our Def Con 24 slides over at MWR Labs which give an explanation of the fuzzer and examples of writing library calls and syscalls for the fuzzer. One of each is provided as an example and more examples are provided in the slides. […]”
“I am happy to announce today the public release of our evolutionary knowledge-based fuzzer, Choronzon. An overview of the architecture of Choronzon was initially presented at the ZeroNights 2015 conference. A recording of the presentation and the slide deck are also available. You can now find the full source code of Choronzon on the official CENSUS GitHub page. We welcome any feedback you may have and pull requests!”
[…] Original AFL code written by Michal Zalewski <firstname.lastname@example.org> Windows fork written and maintained by Ivan Fratric <email@example.com> Copyright 2016 Google Inc. All Rights Reserved. […] Unfortunately, the original AFL does not work on Windows due to very *nix-specific design (e.g. instrumentation, forkserver etc). This project is a fork of AFL that uses different instrumentation approach which works on Windows even for black box binary fuzzing. […] The WinAFL approach: Instead of instrumenting the code at compilation time, WinAFL relies on dynamic instrumentation using DynamoRIO to measure and extract target coverage. This approach has been found to introduce an overhead about 2x compared to the native execution speed, which is comparable to the original AFL in binary instrumentation mode. To improve the process startup time, WinAFL relies heavily on persistant fuzzing mode, that is, executing multiple input samples without restarting the target process. This is accomplished by selecting a target function (that the user wants to fuzz) and instrumenting it so that it runs in a loop. […]
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