In this paper we present MAYHEM, a new system for automatically finding exploitable bugs in binary (i.e., executable) programs. Every bug reported by MAYHEM is accompanied by a working shell-spawning exploit. The working exploits ensure soundness and that each bug report is securitycritical and actionable. MAYHEM works on raw binary code without debugging information. To make exploit generation possible at the binary-level, MAYHEM addresses two major technical challenges: actively managing execution paths without exhausting memory, and reasoning about symbolic memory indices, where a load or a store address depends on user input. To this end, we propose two novel techniques: 1) hybrid symbolic execution for combining online and offline (concolic) execution to maximize the benefits of both techniques, and 2) index-based memory modeling, a technique that allows MAYHEM to efficiently reason about symbolic memory at the binary level. We used MAYHEM to find and demonstrate 29 exploitable vulnerabilities in both Linux and Windows programs, 2 of which were previously undocumented.
The automatic exploit generation challenge is given a program, automatically find vulnerabilities and generate exploits for them. In this paper we present AEG, the first end-to-end system for fully automatic exploit generation. We used AEG to analyze 14 open-source projects and successfully generated 16 control flow hijacking exploits. Two of the generated exploits (expect-5.43 and htget-0.93) are zero-day exploits against unknown vulnerabilities. Our contributions are: 1) we show how exploit generation for control flow hijack attacks can be modeled as a formal verification problem, 2) we propose preconditioned symbolic execution, a novel technique for targeting symbolic execution, 3) we present a general approach for generating working exploits once a bug is found, and 4) we build the first end-to-end system that automatically finds vulnerabilities and generates exploits that produce a shell.
Black-box mutational fuzzing is a simple yet effective technique to find bugs in software. Given a set of program-seed pairs, we ask how to schedule the fuzzings of these pairs in order to maximize the number of unique bugs found at any point in time. We develop an analytic framework using a mathematical model of black-box mutational fuzzing and use it to evaluate 26 existing and new randomized online scheduling algorithms. Our experiments show that one of our new scheduling algorithms outperforms the multi-armed bandit algorithm in the current version of the CERT Basic Fuzzing Framework (BFF) by finding 1.5× more unique bugs in the same amount of time.
We present the design of an algorithm to maximize the number of bugs found for black-box mutational fuzzing given a program and a seed input. The major intuition is to leverage white-box symbolic analysis on an execution trace for a given program-seed pair to detect dependencies among the bit positions of an input, and then use this dependency relation to compute a probabilistically optimal mutation ratio for this program-seed pair. Our result is promising: we found an average of 38.6% more bugs than three previous fuzzers over 8 applications using the same amount of fuzzing time.
JavaScript engines are an attractive target for attackers due to their popularity and flexibility in building exploits. Current state-of-the-art fuzzers for finding JavaScript engine vulnerabilities focus mainly on generating syntactically correct test cases based on either a predefined context-free grammar or a trained probabilistic language model. Unfortunately, syntactically correct JavaScript sentences are often semantically invalid at runtime. Furthermore, statically analyzing the semantics of JavaScript code is challenging due to its dynamic nature: JavaScript code is generated at runtime, and JavaScript expressions are dynamically-typed. To address this challenge, we propose a novel test case generation algorithm that we call semantics-aware assembly, and implement it in a fuzz testing tool termed CodeAlchemist. Our tool can generate arbitrary JavaScript code snippets that are both semantically and syntactically correct, and it effectively yields test cases that can crash JavaScript engines. We found numerous vulnerabilities of the latest JavaScript engines with CodeAlchemist and reported them to the vendors.
We present the design and implementation of a novel anti-malware system called SplitScreen. SplitScreen performs an additional screening step prior to the signature matching phase found in existing approaches. The screening step filters out most non-infected files (90%) and also identifies malware signatures that are not of interest (99%). The screening step significantly improves end-to-end performance because safe files are quickly identified and are not processed further, and malware files can subsequently be scanned using only the signatures that are necessary. Our approach naturally leads to a network-based anti-malware solution in which clients only receive signatures they needed, not every malware signature ever created as with current approaches. We have implemented SplitScreen as an extension to Cla-mAV [13], the most popular open source anti-malware software. For the current number of signatures, our implementation is 2× faster and requires 2× less memory than the original ClamAV. These gaps widen as the number of signatures grows.
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