Continuous Fuzzing Techniques

• Continuous fuzzing is an automated testing paradigm that integrates fuzzing into CI/CD pipelines to continuously discover bugs and monitor regressions.
• It employs adaptive mutation and feedback-guided strategies, including reinforcement learning and multi-armed bandit techniques, to enhance code coverage and crash discovery.
• The approach optimizes performance through efficient instrumentation, dynamic seed management, and regression-aware scheduling, significantly reducing resource overhead.

Continuous fuzzing is an automated and iterative software testing paradigm that integrates fuzzing into the ongoing, routine processes of software construction, such as continuous integration (CI) and deployment pipelines. Unlike traditional one-off fuzz campaigns, continuous fuzzing operates as a persistent quality and security assurance mechanism, running systematically on new code changes, monitoring regression, adapting to coverage feedback, and generating cumulative results and actionable artifacts such as bug reports and crash clusters.

1. Foundational Principles and Unified Models

Continuous fuzzing is grounded in a modular, iterative pipeline model where the fuzzer systematically processes and evolves test configurations as long as resources or time budgets allow (Manes et al., 2018). The canonical pipeline consists of phases such as:

• Preprocessing and Instrumentation: Instrument the program-under-test for feedback collection (e.g., coverage, bug oracles).
• Seed Pool Management: Maintain and update a dynamic pool of test seeds, incrementally adding those that discover new program behaviors or faults.
• Fuzz Input Generation and Execution: Create test inputs through mutation or generation, applying learning-based or stochastic selection of inputs and mutation sites.
• Feedback-driven Scheduling: Adaptively select seeds and configurations for further mutation using metrics (distance to uncovered code, rate of new coverage, etc.).
• Configuration Update and Corpus Minimization: Refine the set of seeds/configurations to favor minimality (minset covering all discovered program states) and efficiency, pruning redundant or ineffective seeds.

This core loop, represented formally as a repeated process:

$$(\text{Preprocess} \to \text{Schedule} \to \text{InputGen} \to \text{InputEval} \to \text{ConfUpdate})^*$$

is inherently extensible to continuous fuzzing; its effectiveness grows with campaign duration as feedback and genome diversity accumulate (Manes et al., 2018).

2. Adaptive Mutation and Feedback-guided Strategies

A defining feature of continuous fuzzing systems is their ability to refine testing strategies through constant feedback. Traditional fuzzing often applies random or fixed-probability mutations, but advanced continuous fuzzers dynamically adapt mutation choices to maximize code exploration and crash discovery.

Reinforcement and Bandit-based Adaptation

Frameworks such as FuzzerGym integrate reinforcement learning (RL), formalizing mutation operator selection as a Markov decision process with rewards equating to incremental code coverage ($R_t = \text{cov}_t - \text{cov}_{t-1}$) (Drozd et al., 2018). A double deep Q-learning agent learns a policy over mutation actions by maximizing the expected sum of coverage gains. This is further extended with deep neural networks (including LSTM layers to address partial observability), yielding policies that outperform manual or static schedules.

In parallel, multi-armed bandit techniques such as Thompson Sampling treat each mutation operator as a statistical "arm," drawing on empirical reward rates to bias future mutation choices (Karamcheti et al., 2018). Operators whose application led to coverage gains are favored adaptively, maintaining a balance of exploration (trying underused operators) and exploitation (rewarding successful strategies). These methods robustly outperform uniform random selection, showing 1.1–1.7x improvements in unique crash or path discovery in benchmarks.

Evolutionary and Optimization Schedulers

Continuous fuzzing also employs evolutionary strategies for mutation scheduling. Systems like DARWIN adapt a probability vector over available mutation operators, using population-based search (e.g., single-solution evolution strategy) to maximize the number of new unique paths discovered per round. Feedback (e.g., edge coverage increases) is used as a fitness measure:

$\text{fitness} = \#\, \text{Unique\_Paths}$

Resulting in measurable (1.7×) gains over traditional havoc schedulers (Jauernig et al., 2022).

3. Performance, Scalability, and System Integration

Optimizing for the realities of long-running, resource-constrained deployment is crucial in continuous fuzzing (Klooster et al., 2022). High-throughput execution is achieved by:

• Minimizing Tracing Overhead: Coverage-guided tracing restricts the cost-intensive coverage measurement to only those test cases that newly increase coverage. For example, UnTracer encodes the current coverage frontier via software interrupts so only “interesting” test cases trigger full tracing, lowering the average overhead to <1% compared to 36%–612% for AFL-based tracers (Nagy et al., 2018).
• CI/CD Pipeline Compatibility: Continuous fuzzing is deeply integrated into software pipelines by employing regression fuzzing (testing only code changes with new compiled artifacts), checksum or code-diff–aware test selection, and ensemble fuzzing (multiple fuzzers running in parallel with corpus sharing and minimization). These strategies enable reductions of ~63% in average fuzzing effort in empirical studies, without sacrificing detection of important bugs (Klooster et al., 2022).
• Short, Iterative Campaigns: Empirical analysis of ~1.12 million OSS-Fuzz sessions demonstrates that code coverage and bug discovery improve linearly with ongoing fuzzing, especially in early stages, with detection rates stabilizing at 2–5% per session beyond the 25th run (Shirai et al., 18 Oct 2025). Campaign duration is a critical parameter: short runs (15 minutes) effectively catch regressions, while periodic long runs catch deeper bugs.

4. Advanced Feedback Models and Specialized Applications

Hybrid and Intelligent Fuzzing

Continuous fuzzing frameworks are increasingly leveraging hybrid approaches, combining traditional mutation with symbolic execution and deep learning (Vishnyakov et al., 2022, Zhu et al., 2021, She et al., 2020). These methods:

• Integrate dynamic symbolic execution (DSE) alongside coverage-guided fuzzing, enabling systematic exploration of hard-to-reach paths and inversion of complex branch constraints.
• Employ attention-based and multi-task deep neural architectures to focus mutations on input bytes most likely to control program branching or trigger target behaviors. Saliency-based or gradient-based mutation selection more efficiently covers complex code (She et al., 2020, Zhu et al., 2021, Wang et al., 2023).
• Support continuous improvement cycles, with models periodically retrained on new feedback (coverage, heatmaps) to break through coverage bottlenecks and avoid “plateauing” (Zhu et al., 2021).

State Coverage and Protocol/Smart Contract Fuzzing

Protocols and stateful systems pose unique challenges due to complex internal state machines. Stateful continuous fuzzing instruments code to automatically identify and track state transitions (particularly enums representing protocol states), building a State Transition Tree (STT) to guide exploration toward unvisited state paths (Ba et al., 2022). Similarly, in smart contract fuzzing, invocation order analysis and branch rarity–driven energy allocation allow deeper state coverage and higher precision in vulnerability discovery (Liu et al., 2023).

Domain-specific Extensions

For network applications, techniques such as synchronous communication transformation and smart snapshotting (deferred forkservers, in-memory filesystems) boost input throughput by 8–60× compared to previous asynchronous fuzzers (Andronidis et al., 2022). For cyber-physical and configuration-fuzzing use cases, general-purpose fuzzers operate by iteratively applying configuration changes (parameterized transforms) and collecting test results into a risk model, supporting digital twin construction and path-based risk assessment (Hance et al., 2023).

5. Empirical Impact, Continuous Monitoring, and Crash Management

Large-scale empirical studies provide quantitative validation of continuous fuzzing efficacy (Shirai et al., 18 Oct 2025, Ruohonen et al., 2019). Key findings include:

• A substantial fraction of pre-existing bugs are detected in the very first fuzzing sessions following integration—36/878 projects in the initial session, with a steadily linear increase in aggregate coverage and cumulative bug detection over millions of sessions (Shirai et al., 18 Oct 2025).
• Adaptive coverage changes (both increases and decreases) during the course of continuous fuzzing strongly predict bug discovery events, supporting a need for diff-aware and regression-focused fuzzing strategies.
• In kernel fuzzing, automated pipelines such as syzbot produce large volumes of crash reports (800+ unresolved crashes across kernels), and statistical analysis reveals only weak predictors for bug fix times (e.g., code churn), further underscoring the necessity for crash deduplication and triage in high-volume settings (Ruohonen et al., 2019).

Crash accumulation and triage in continuous fuzzing leverage incremental trace clustering algorithms. Each new crash is assigned to duplicate, inner, outer, or out-of-threshold groups relative to existing clusters. Soft and hierarchical clustering strategies balance cluster quality (as measured by silhouette scores) with the need to maintain stable error assignment over time (Yegorov et al., 28 May 2024).

6. Tooling, Automation, and Future Directions

Continuous fuzzing at scale is heavily dependent on automation infrastructure:

• Automated harness generation for APIs and protocol parsers enables continual onboarding of new code paths as projects evolve, supporting seamless integration with frameworks such as OSS-Fuzz (Rahalkar, 2023).
• Hybrid orchestrators (e.g., Sydr-Fuzz) coordinate DSE, classic fuzzers, predicate-based security checks, and advanced crash triage across interconnected utilities, maximizing both code coverage and bug yield while minimizing redundant computational effort (Vishnyakov et al., 2022).
• Open-sourcing components such as crash triage tools (CASR) promotes reproducibility, extensibility, and community improvement.

Current empirical evidence advocates for the adoption of adaptive, diff-aware, and regression-focused continuous fuzzing schedules, ensemble strategies, and incorporating feedback-driven learning at all stages of the fuzzing lifecycle. These practices collectively deliver ongoing vulnerability detection, elevated coverage, and robust regression assurance in evolving software systems.