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Mon 14 - Fri 18 November 2022 Singapore

Browser APIs are essential to the modern web experience. Due to their large number and complexity, they vastly expand the attack surface of browsers. To detect vulnerabilities in these APIs, fuzzers generate test cases with a large amount of random API invocations. However, the massive search space formed by arbitrary API combinations hinders their effectiveness: since randomly-picked API invocations unlikely interfere with each other (i.e., compute on partially shared data), few interesting API interactions are explored. Consequently, reducing the search space by revealing inter-API relations is a major challenge in browser fuzzing.

We propose Minerva, an efficient browser fuzzer for browser API bug detection. The key idea is to leverage API interference relations to reduce redundancy and improve coverage. Minerva consists of two modules: dynamic mod-ref analysis and guided code generation. Before fuzzing starts, the dynamic mod-ref analysis module builds an API interference graph. It first automatically identifies individual browser APIs from the browser's code base. Next, it instruments the browser to dynamically collect mod-ref relations between APIs. During fuzzing, the guided code generation module synthesizes highly-relevant API invocations guided by the mod-ref relations. We evaluate Minerva on three mainstream browsers, i.e. Safari, FireFox, and Chromium. Compared to state-of-the-art fuzzers, Minerva improves edge coverage by $19.63%$ to $229.62%$ and finds 2x to 3x more unique bugs. Besides, Minerva has discovered 35 previously-unknown bugs out of which 20 have been fixed with 5 CVEs assigned and acknowledged by browser vendors.