Stateful Greybox Fuzzing (2204.02545v3)

Abstract: Many protocol implementations are reactive systems, where the protocol process is in continuous interaction with other processes and the environment. If a bug can be exposed only in a certain state, a fuzzer needs to provide a specific sequence of events as inputs that would take protocol into this state before the bug is manifested. We call these bugs as "stateful" bugs. Usually, when we are testing a protocol implementation, we do not have a detailed formal specification of the protocol to rely upon. Without knowledge of the protocol, it is inherently difficult for a fuzzer to discover such stateful bugs. A key challenge then is to cover the state space without an explicit specification of the protocol. In this work, we posit that manual annotations for state identification can be avoided for stateful protocol fuzzing. Specifically, we rely on a programmatic intuition that the state variables used in protocol implementations often appear in enum type variables whose values (the state names) come from named constants. In our analysis of the Top-50 most widely used open-source protocol implementations, we found that every implementation uses state variables that are assigned named constants (with easy to comprehend names such as INIT, READY) to represent the current state. In this work, we propose to automatically identify such state variables and track the sequence of values assigned to them during fuzzing to produce a "map" of the explored state space. Our experiments confirm that our stateful fuzzer discovers stateful bugs twice as fast as the baseline greybox fuzzer that we extended. Starting from the initial state, our fuzzer exercises one order of magnitude more state/transition sequences and covers code two times faster than the baseline fuzzer. Several zero-day bugs in prominent protocol implementations were found by our fuzzer, and 8 CVEs have been assigned.

• The paper proposes a novel fuzzing technique that infers state transitions in protocols to accurately identify stateful bugs.
• It utilizes a state transition tree to dynamically capture protocol state variables, outperforming traditional coverage-based methods.
• Experimental results show SGFuzz explores 33 times more state sequences and discovers new vulnerabilities, enhancing security testing efficacy.

Analysis of "Stateful Greybox Fuzzing"

The paper "Stateful Greybox Fuzzing" addresses a significant challenge in the domain of protocol testing: the identification and detection of stateful bugs within reactive systems without explicit protocol specifications. The authors propose a novel stateful greybox fuzzing approach that harnesses automatically identifiable state variables to map and explore the state space of protocol implementations more efficiently than traditional fuzzing methods.

Identification of Stateful Bugs

Stateful bugs are intrinsic to protocol implementations where specific sequences of state transitions are prerequisites for bug manifestation. Conventional greybox fuzzers, focusing primarily on code coverage, fail to capture the nuanced state transitions within protocols, leading to an incomplete exploration of a program’s behavior. This paper acknowledges this shortcoming and introduces a methodology to infer and navigate the state space effectively, utilizing state variables commonly defined in protocol implementations.

Methodology and Experimental Insights

The authors assert that protocol state variables, often represented as enum types with named constants, form the basis for their state exploration. They develop a state transition tree (STT) data structure that dynamically captures the values of these state variables, enabling the fuzzer to observe and record state transitions during program execution. By automating the identification of these variables, the fuzzer constructs the STT without manual annotations or protocol specifications.

The implementation of this methodology into a tool named SGFuzz, built upon LibFuzzer, demonstrated significant improvements in fuzzing efficiency. In comparative experiments involving several widely-used protocol implementations, SGFuzz outperformed existing state-of-the-art fuzzers such as AFLNet and IJON in terms of discovering stateful bugs and expanding the state transition coverage.

Results and Implications

The experimental results highlight a few key numerical findings: SGFuzz explores 33 times more state transition sequences on average compared to the baseline, LibFuzzer, and covers code branches twice as fast. Notably, SGFuzz identified 12 new vulnerabilities across the evaluated subjects, with a substantial proportion being stateful bugs. These results underscore the efficacy of automatic state space exploration and its potential to unearth vulnerabilities previously unreachable by standard fuzzing techniques.

Theoretical and Practical Implications

The paper proposes a shift in the fuzz testing paradigm towards integrating statefulness into the fuzzing process — a method that could be particularly beneficial for security-critical systems where protocol adherence is vital. The ability to automatically infer state transitions without human intervention or detailed protocol knowledge could revolutionize testing in environments with high protocol variability or inadequate documentation.

Moreover, the paper suggests future pathways for automated fuzzer evolution by integrating tighter feedback loops between the exploration of functional and state coverage, potentially enhancing the detection of dormant vulnerabilities in complex systems.

Conclusion and Outlook

In summary, "Stateful Greybox Fuzzing" presents a compelling approach to address the challenges posed by stateful bugs in protocol implementations. By automating the identification and mapping of state spaces through inferred state variables, this research propels fuzzing strategies towards more comprehensive testing solutions. Future work could focus on refining state inference techniques and expanding this approach to cover broader classes of stateful software systems, further advancing the robustness of software security practices. As SGFuzz continues to be refined and adopted, it stands to fortify the methodologies available for testing stateful systems, promoting greater stability and security in protocol implementation.