Sign In

Fuzzing Program Environments to Discover Zero-Day Bugs in Real-World Applications

Core Concepts
Fuzzing the full program environment, including files, sockets, configurations, and other inputs, can uncover previously unknown bugs and vulnerabilities in real-world applications.
The paper proposes a new greybox fuzzing methodology called Efuzz that can capture the effect of complex program environments without the need for manual environment modelling. Key highlights: Efuzz first records all observed environmental interactions between the target program and its environment at the kernel/user-mode boundary in the form of system calls. During the fuzzing phase, Efuzz replays the recorded interactions, but selectively mutates the inputs to generate the effect of different program environments. Efuzz introduces the notion of "relaxed replay" to handle program behavior divergence after mutation, allowing it to progress the execution and explore novel program behaviors. Efuzz was evaluated on 20 real-world network protocol implementations and GUI applications under Linux. It discovered 33 previously unknown bugs, including 14 new CVEs, demonstrating the effectiveness of fuzzing the full program environment.
Efuzz found 33 previously unknown bugs in well-known real-world applications. 24 of the bugs were confirmed by developers. 14 CVE IDs were assigned for the discovered vulnerabilities. 16 of the bugs have been fixed by the developers.
"Fuzzing should be executed under different execution environments to comprehensively explore diverse program behaviours." "Our approach extends the scope of fuzzing to include all environmental inputs, meaning that any input is considered a fuzz target, regardless of source." "Efuzz found 33 previously unknown bugs (24 bugs confirmed by developers, which include 14 new CVEs) in real-world and well-known applications."

Key Insights Distilled From

by Ruijie Meng,... at 04-23-2024
Program Environment Fuzzing

Deeper Inquiries

How can the techniques used in Efuzz be extended to handle more complex program environments, such as those involving distributed systems or cloud-based services?

Efuzz's approach of recording and replaying program interactions at the system call level can be extended to handle more complex program environments by incorporating additional layers of abstraction and emulation. For distributed systems, where multiple nodes interact over a network, Efuzz can simulate network communication by intercepting and emulating network packets exchanged between nodes. By recording the network interactions and replaying them with mutations, Efuzz can explore different network configurations and behaviors that may lead to bugs or vulnerabilities. In the case of cloud-based services, where programs interact with cloud infrastructure and services, Efuzz can emulate cloud APIs and services to capture the effect of the cloud environment on program behavior. By recording interactions with cloud services and replaying them with variations, Efuzz can uncover bugs related to cloud resource management, security configurations, and data handling in cloud environments. To handle more complex program environments, Efuzz can also integrate with existing tools and frameworks for distributed systems testing and cloud service emulation. By leveraging these tools, Efuzz can simulate realistic distributed and cloud environments, enabling comprehensive testing of programs in these complex settings.

What are the potential limitations or challenges in applying Efuzz to programs that heavily rely on non-deterministic inputs or interactions with external services?

One potential limitation in applying Efuzz to programs with non-deterministic inputs or interactions with external services is the difficulty in reproducing and controlling the non-deterministic behavior during fuzzing. Non-deterministic inputs, such as random number generation or external user interactions, can lead to unpredictable program behaviors that may not be easily captured and replayed by Efuzz. Another challenge is handling interactions with external services that are not easily emulated or controlled during fuzzing. Programs that rely on external APIs, databases, or web services may exhibit complex behaviors that are influenced by the state of the external services, making it challenging to simulate these interactions accurately. Additionally, programs with non-deterministic inputs or interactions may require specialized handling and monitoring during fuzzing to ensure that the fuzzing process remains effective and efficient. Efuzz may need to incorporate techniques for capturing and replaying non-deterministic events, as well as mechanisms for controlling and synchronizing interactions with external services. Overall, the key challenge in applying Efuzz to programs with non-deterministic inputs or interactions with external services lies in effectively capturing, reproducing, and controlling the complex behaviors that arise from these factors during the fuzzing process.

Could the ideas behind Efuzz be adapted to improve the state-of-the-art in other program analysis techniques, such as symbolic execution or model checking, to better handle complex program environments?

The ideas behind Efuzz, specifically the approach of recording and replaying program interactions to capture the effect of program environments, can be adapted to enhance other program analysis techniques such as symbolic execution and model checking. In symbolic execution, Efuzz's technique of recording system-level interactions can be used to augment symbolic execution by providing concrete inputs and environment states for symbolic analysis. By incorporating recorded interactions into symbolic execution, analysts can explore a wider range of program behaviors and paths, leading to more comprehensive bug detection and vulnerability analysis. Similarly, in model checking, Efuzz's methodology of capturing program environments can improve the accuracy and coverage of environment modeling in model checking processes. By using recorded interactions to refine environment models, model checking tools can better simulate real-world conditions and interactions, leading to more effective verification of program properties and behaviors. Overall, adapting Efuzz's ideas to enhance symbolic execution and model checking can help these program analysis techniques better handle complex program environments, leading to more robust and thorough analysis of software systems.