arXiv subjects

Philipp Görz

Publications and source records attributed to Philipp Görz.

5 recordsLinked to original sources

arXiv2026

An Empirical Study of Fuzz Harness Degradation

The purpose of continuous fuzzing platforms is to enable fuzzing for software projects via fuzz harnesses -- but as the projects continue to evolve, are these harnesses updated in lockstep, or do they run out of date? If these harnesses remain unmaintained, will they degrade over time in terms of coverage achieved or number of bugs found? This is the subject of our study. We study Google's OSS-Fuzz continuous fuzzing platform containing harnesses for 510 open-source C/C++ projects, many of which are security-critical. A harness is the glue code between the fuzzer and the project, so it needs to adapt to changes in the project. It is often added by a project maintainer or as part of a, sometimes short-lived, testing effort. Our analysis shows a consistent overall fuzzer coverage percentage for projects in OSS-Fuzz and a surprising longevity of the bug-finding capability of harnesses even without explicit updates, as long as they still build. However, we also identify and manually examine individual cases of harness coverage degradation and categorize their root causes. Furthermore, we contribute to OSS-Fuzz and Fuzz Introspector to support metrics to detect harness degradation in OSS-Fuzz projects guided by this research.

cs.SE↗

arXiv2026

A Comparative Study of Fuzzers and Static Analysis Tools for Finding Memory Unsafety in C and C++

Over 70% of security vulnerabilities in critical software systems today result from memory safety violations. To address this challenge, fuzzing and static analysis are widely used automated methods to discover such vulnerabilities. Fuzzing generates random program inputs to identify faults at runtime, while static analysis reasons about the code to detect potential vulnerabilities. Although these techniques share a common goal, they take fundamentally different approaches and have evolved largely independently. In this paper, we present an empirical analysis of five static analyzers and 13 fuzzers, applied to over 100 known security vulnerabilities in C/C++ programs. We measure the detection rate for each tool and vulnerability to evaluate how the approaches differ and complement each other. We find that fuzzers discover a very similar set of bugs, while static analyzers report more diverse sets, and identify clear leaders for each group. Comparing the union of all fuzzers with that of all static analyzers, we observe they are nearly disjoint. In a second step, we manually validate the report-to-bug mapping we developed for the evaluation and discuss more qualitative aspects of limitations, usability, and integration into the development process. We examine how widely these bug finding tools are used in critical open-source projects. We advise developers on choosing tools to harden their software and identify barriers to adoption as well as future research opportunities.

cs.CR↗

arXiv2025

Anota: Identifying Business Logic Vulnerabilities via Annotation-Based Sanitization

Detecting business logic vulnerabilities is a critical challenge in software security. These flaws come from mistakes in an application's design or implementation and allow attackers to trigger unintended application behavior. Traditional fuzzing sanitizers for dynamic analysis excel at finding vulnerabilities related to memory safety violations but largely fail to detect business logic vulnerabilities, as these flaws require understanding application-specific semantic context. Recent attempts to infer this context, due to their reliance on heuristics and non-portable language features, are inherently brittle and incomplete. As business logic vulnerabilities constitute a majority (27/40) of the most dangerous software weaknesses in practice, this is a worrying blind spot of existing tools. In this paper, we tackle this challenge with ANOTA, a novel human-in-the-loop sanitizer framework. ANOTA introduces a lightweight, user-friendly annotation system that enables users to directly encode their domain-specific knowledge as lightweight annotations that define an application's intended behavior. A runtime execution monitor then observes program behavior, comparing it against the policies defined by the annotations, thereby identifying deviations that indicate vulnerabilities. To evaluate the effectiveness of ANOTA, we combine ANOTA with a state-of-the-art fuzzer and compare it against other popular bug finding methods compatible with the same targets. The results show that ANOTA+FUZZER outperforms them in terms of effectiveness. More specifically, ANOTA+FUZZER can successfully reproduce 43 known vulnerabilities, and discovered 22 previously unknown vulnerabilities (17 CVEs assigned) during the evaluation. These results demonstrate that ANOTA provides a practical and effective approach for uncovering complex business logic flaws often missed by traditional security testing techniques.

cs.CR↗

arXiv2023

Systematic Assessment of Fuzzers using Mutation Analysis

Fuzzing is an important method to discover vulnerabilities in programs. Despite considerable progress in this area in the past years, measuring and comparing the effectiveness of fuzzers is still an open research question. In software testing, the gold standard for evaluating test quality is mutation analysis, which evaluates a test's ability to detect synthetic bugs: If a set of tests fails to detect such mutations, it is expected to also fail to detect real bugs. Mutation analysis subsumes various coverage measures and provides a large and diverse set of faults that can be arbitrarily hard to trigger and detect, thus preventing the problems of saturation and overfitting. Unfortunately, the cost of traditional mutation analysis is exorbitant for fuzzing, as mutations need independent evaluation. In this paper, we apply modern mutation analysis techniques that pool multiple mutations and allow us -- for the first time -- to evaluate and compare fuzzers with mutation analysis. We introduce an evaluation bench for fuzzers and apply it to a number of popular fuzzers and subjects. In a comprehensive evaluation, we show how we can use it to assess fuzzer performance and measure the impact of improved techniques. The required CPU time remains manageable: 4.09 CPU years are needed to analyze a fuzzer on seven subjects and a total of 141,278 mutations. We find that today's fuzzers can detect only a small percentage of mutations, which should be seen as a challenge for future research -- notably in improving (1) detecting failures beyond generic crashes (2) triggering mutations (and thus faults).

cs.SE↗

arXiv2022

Mutation Analysis: Answering the Fuzzing Challenge

Fuzzing is one of the fastest growing fields in software testing. The idea behind fuzzing is to check the behavior of software against a large number of randomly generated inputs, trying to cover all interesting parts of the input space, while observing the tested software for anomalous behaviour. One of the biggest challenges facing fuzzer users is how to validate software behavior, and how to improve the quality of oracles used. While mutation analysis is the premier technique for evaluating the quality of software test oracles, mutation score is rarely used as a metric for evaluating fuzzer quality. Unless mutation analysis researchers can solve multiple problems that make applying mutation analysis to fuzzing challenging, mutation analysis may be permanently sidelined in one of the most important areas of testing and security research. This paper attempts to understand the main challenges in applying mutation analysis for evaluating fuzzers, so that researchers can focus on solving these challenges.

cs.SE↗