In recent years, the growth of mobile advertising has been driven by in-app programmatic advertising and technologies like Real-Time Bidding (RTB). However, this growth has also led to an increase in ad fraud, such as click injection, background ad activity, etc. While existing studies have primarily concentrated on ad fraud within individual apps or devices, this paper introduces a new form of collusion-based ad fraud, named ad attribution laundering fraud (ALF). ALF involves multiple apps collaborating to deceive advertisers by misrepresenting the app where ads are displayed. The collusion-based approach allows lower-quality apps to exploit the reputable identities of seemingly legitimate apps. This deceives advertisers or ad networks into believing that the advertisements they place are reaching potentially valid end-users on the legitimate app. The seemingly legitimate ad events and ad attribution procedures employed by individual apps in such attacks can evade detection by existing tools.

Starting with a random initial seed, fuzzers search for inputs that trigger bugs or vulnerabilities. However, fuzzers often fail to generate inputs for program paths guarded by restrictive branch conditions. In this paper, we show that by first identifying rare-paths in programs (i.e., program paths with path constraints that are unlikely to be satisfied by random input generation), and then, generating inputs/seeds that trigger rare-paths, one can improve the coverage of fuzzing tools. In particular, we present techniques 1) that identify rare paths using quantitative symbolic analysis, and 2) generate inputs that can explore these rare paths using path-guided concolic execution. We provide these inputs as initial seed sets to three state of the art fuzzers. Our experimental evaluation on a set of programs shows that the fuzzers achieve better coverage with the rare-path based seed set compared to a random initial seed.

Smart contracts are critical financial instruments, and their security is of utmost importance. However, smart contract programs are difficult to fuzz due to the persistent blockchain state behind all transactions. Mutating sequences of transactions are complex and often lead to a suboptimal exploration for both input and program spaces. In this paper, we introduce a novel snapshot-based fuzzer ItyFuzz for testing smart contracts. In ItyFuzz, instead of storing sequences of transactions and mutating from them, we snapshot states and singleton transactions. To explore interesting states, ItyFuzz introduces a dataflow waypoint mechanism to identify states with more potential momentum. ItyFuzz also incorporates comparison waypoints to prune the space of states. By maintaining snapshots of the states, ItyFuzz can synthesize concrete exploits like reentrancy attacks quickly. Because ItyFuzz has second-level response time to test a smart contract, it can be used for on-chain testing, which has many benefits compared to local development testing. Finally, we evaluate ItyFuzz on real-world smart contracts and some hacked on-chain DeFi projects. ItyFuzz outperforms existing fuzzers in terms of instructional coverage and can find and generate realistic exploits for on-chain projects quickly.

Browsers use security policies to block malicious behaviors. Cross-Origin Read Blocking (CORB) is a browser security policy for preventing side-channel attacks such as Spectre. We propose a web browser security policy fuzzer called CorbFuzz for checking CORB and similar policies. In implementing a security policy, the browser only has access to HTTP requests and responses, and takes policy actions based solely on those interactions. In checking the browser security policies, CorbFuzz uses a policy oracle that tracks the web application behavior and infers the desired policy action based on the web application state. By comparing the policy oracle with the browser behavior, CorbFuzz detects weaknesses in browser security policies. CorbFuzz checks the web browser policy by fuzzing a set of web applications where the state-related queries are symbolically evaluated for increased coverage and automation. CorbFuzz collects type information from database queries and branch conditions in order to prevent the generation of inconsistent data values during fuzzing. We evaluated CorbFuzz on CORB implementations of Chromium and Webkit, and Opaque Response Blocking (ORB) policy implementation of Firefox using web applications collected from GitHub. We found three classes of weaknesses in Chromium’s implementation of CORB.

This paper proposes a state-aware fuzzing framework for testing software-defined network applications. It leverages a property graph to store fuzzing results. Application developers can easily express oracles with the graph query language to test their applications. The graph representation also allows the oracles to analyze the fuzzing result efficiently.