Smarter Fuzzing: Reusing Past Tests to Find Future Processor Bugs

Author: Denis Avetisyan

A new framework intelligently repurposes existing processor test cases to significantly improve the efficiency of hardware vulnerability detection.

ReFuzz introduces a novel fuzzing framework that accelerates vulnerability discovery in processor-under-tests (PUTs) by strategically reusing effective test cases from prior analyses, thereby improving fuzzing efficiency and increasing the identification of $Vuls$ (vulnerabilities).

ReFuzz leverages contextual bandits to reuse and mutate tests, enhancing coverage and identifying cross-generational vulnerabilities in processor designs.

Processor design increasingly relies on reusing established architectures, yet this practice inadvertently propagates vulnerabilities across generations. Addressing this challenge, we present ReFuzz: Reusing Tests for Processor Fuzzing with Contextual Bandits, a novel framework that leverages contextual bandits to intelligently repurpose effective tests from prior designs when fuzzing a processor-under-test. This adaptive approach not only accelerates the discovery of similar and new vulnerabilities-uncovering three security flaws and two functional bugs in our evaluation-but also achieves a 511.23x coverage speedup compared to existing fuzzers. Can this cross-generational test reuse become a standard practice in hardware verification, proactively mitigating vulnerabilities before they manifest in deployed systems?

The Escalating Complexity of Modern Processor Validation

Contemporary processors, exemplified by the burgeoning RISC-V architecture, present a verification hurdle due to escalating design complexity. The sheer number of transistors and intricate interactions within a modern central processing unit-now routinely exceeding billions of components-creates a state-space explosion. This means the number of possible execution paths and potential error conditions grows exponentially with each added feature. Consequently, achieving comprehensive testing – verifying that every conceivable scenario functions correctly – becomes practically impossible using traditional methods. While functional correctness is paramount, the difficulty lies in systematically exploring this vast design space to identify even the most elusive bugs before deployment, demanding innovative verification techniques and substantial computational resources.

Conventional processor verification techniques, relying heavily on simulation and formal methods, are increasingly hampered by the sheer scale and intricacy of modern designs. While effective at detecting obvious functional errors, these approaches often fall short in achieving comprehensive code coverage – the extent to which every line of processor code is exercised during testing. This limitation leaves room for subtle vulnerabilities, particularly those triggered by rare instruction sequences or corner-case conditions, to remain hidden. The problem is exacerbated by the growing use of complex features like out-of-order execution, branch prediction, and multi-level caches, which dramatically increase the state space that needs to be explored. Consequently, even with significant computational resources, traditional methods struggle to guarantee the complete absence of exploitable flaws, prompting a search for more effective and scalable verification strategies.

ReFuzz training leverages a coverage context set (C) alongside vulnerability (V) and coverage (Cov) lists to iteratively refine testing strategies.

The Importance of Reference Models in Vulnerability Detection

Differential testing is a vulnerability detection technique that operates by executing the same inputs on both a system under test and a known-good reference model – often referred to as a ‘golden’ or baseline implementation, with Spike being a common example. By comparing the outputs of these two executions, discrepancies can be identified, indicating potential bugs or vulnerabilities in the system under test. This approach is particularly effective because it isolates the differences, reducing the need to manually analyze complex system behavior. The accuracy of this method depends on the fidelity of the golden reference and the comprehensiveness of the test input suite.

The efficacy of differential testing is directly correlated with the quality and breadth of the test inputs used. To effectively expose discrepancies between a system under test and a golden reference, inputs must be diverse, covering a wide range of valid and invalid conditions, edge cases, and boundary values. Representative inputs should accurately reflect real-world usage patterns and anticipated operational scenarios. A limited or biased input set will likely fail to uncover subtle vulnerabilities or performance issues, rendering the differential testing process incomplete and potentially misleading. Input generation techniques, including fuzzing, combinatorial testing, and model-based testing, are often employed to achieve sufficient input diversity and coverage.

ReFuzz: An Intelligent Approach to Processor Fuzzing

ReFuzz utilizes Contextual Bandit (CB) algorithms to improve the efficiency of processor fuzzing by intelligently selecting and reusing previously generated test cases. Traditional fuzzing often operates on a principle of random mutation, leading to redundant testing of ineffective input variations. In contrast, ReFuzz models the fuzzing process as a multi-armed bandit problem, where each “arm” represents a specific input region or mutation strategy. The CB algorithm learns to associate input characteristics – the “context” – with the effectiveness of prior test cases, measured by factors like code coverage or bug discovery. This allows ReFuzz to prioritize test case generation towards promising input spaces, effectively reusing successful strategies and minimizing exploration of unproductive areas, thereby accelerating the identification of processor vulnerabilities.

ReFuzz employs Contextual Bandit (CB) algorithms to dynamically prioritize input test case selection based on feedback from prior execution. This approach moves beyond uniform or random testing by associating input characteristics – representing the ‘context’ for the CB – with observed outcomes, specifically whether a test case triggered a fault or not. The CB then learns a policy that predicts the likelihood of finding vulnerabilities in new inputs based on their characteristics, effectively focusing fuzzing efforts on regions of the input space that demonstrate a higher probability of success. This adaptive prioritization minimizes redundant testing of unproductive inputs and maximizes the rate of vulnerability discovery by strategically exploring promising areas.

ReFuzz implements Hardware Design Reuse by identifying and leveraging common sub-structures within the target processor’s architecture during fuzzing. This is achieved through the extraction and categorization of Register Transfer Level (RTL) components, enabling the system to recognize instances where previously tested input patterns can be effectively re-applied to similar, but not identical, hardware blocks. By avoiding redundant testing of functionally equivalent structures, ReFuzz significantly reduces the overall testing effort and increases the efficiency of vulnerability discovery, as the same testing resources can be applied to a broader range of unique hardware configurations. This approach minimizes input space duplication and accelerates the fuzzing process without sacrificing coverage.

ReFuzz employs a novel framework integrating fuzzing with reinforcement learning to efficiently discover vulnerabilities.

Demonstrated Performance Gains with Industry-Standard Tools

ReFuzz integration with industry-standard simulation and verification platforms, specifically VCS and Chipyard, was critical to obtaining reliable performance metrics. VCS, a commercial cycle-based simulator, provided accurate timing and functional modeling of the target processor. Chipyard, an open-source, highly configurable platform for processor generation and testing, enabled comprehensive evaluation across a range of architectural configurations. Utilizing these established tools ensured that the observed performance improvements – including the reported 511.23x coverage speedup and 1.89% total coverage gain – were not artifacts of a simplified or unrealistic testing environment, but reflected practical gains achievable in real-world processor development workflows.

Evaluation of ReFuzz, when benchmarked against baseline fuzzing techniques, yielded an average coverage speedup of 511.23x. This indicates ReFuzz can achieve the same level of code coverage as traditional methods in a significantly reduced timeframe. Additionally, ReFuzz demonstrated an average increase of 1.89% in total coverage achieved, suggesting its ability to identify a greater number of previously undetected vulnerabilities or edge cases within the tested processor designs. These performance metrics were consistently observed across various simulations and verification runs conducted using industry-standard tools.

Post-fuzzing test minimization applied to the generated test suite resulted in a 98.76% reduction in size. This indicates that ReFuzz produces a significantly streamlined set of tests while maintaining coverage. The substantial reduction in test suite size offers practical benefits including reduced storage requirements, faster test execution times, and simplified debugging processes, thereby demonstrating the efficiency of ReFuzz in generating concise and effective tests.

Evaluation of ReFuzz using industry-standard simulation and verification tools, including VCS and Chipyard, demonstrates its practical efficacy in improving processor security. Quantitative results indicate an average coverage speedup of 511.23x and an increase of 1.89% in total coverage when compared to baseline fuzzing techniques. Furthermore, the application of a test minimizer to the generated test suites resulted in a 98.76% reduction in test suite size, confirming ReFuzz’s ability to efficiently identify critical vulnerabilities and generate concise, focused test cases for security validation.

Branch coverage analysis demonstrates the effectiveness of TheHuzz, Cascade, and ReFuzz in thoroughly testing software code.

Towards a Future of Resilient and Secure Computing Systems

Processor verification, the process of ensuring a chip functions correctly under all conditions, traditionally demands immense computational resources and time. Recent advancements demonstrate that intelligently reusing past test cases – rather than generating entirely new ones with each design iteration – dramatically improves efficiency. This approach leverages the knowledge gained from previous tests to focus exploration on areas most likely to reveal vulnerabilities. By prioritizing test cases based on their potential to expose errors, verification processes can pinpoint critical flaws with fewer attempts, significantly reducing both the time and computational power needed to certify a processor’s reliability. This streamlined process is especially vital given the increasing complexity of modern processors and the urgent need for secure computing systems.

The accelerating pace of innovation in processor design demands verification techniques that can keep up. Traditional methods, often exhaustive and time-consuming, struggle to validate increasingly complex architectures before they reach deployment. This creates a critical need for efficient verification strategies, as delays in testing directly impact time-to-market and potentially introduce security vulnerabilities. Furthermore, the proliferation of connected devices and the increasing reliance on processors for sensitive applications – from financial transactions to critical infrastructure – necessitate robust security measures embedded from the design phase. Improved verification efficiency isn’t simply about speed; it’s about enabling the rapid deployment of secure computing systems capable of safeguarding data and maintaining the integrity of vital operations in an ever-evolving technological landscape.

ReFuzz signifies a considerable advancement in the development of dependable computing systems, addressing the escalating need for robust processor security. This innovative approach to processor verification moves beyond traditional, exhaustive testing by intelligently prioritizing test cases and reusing successful explorations – a methodology that dramatically reduces the time and computational resources required to identify vulnerabilities. By proactively discovering and mitigating weaknesses, ReFuzz directly contributes to the safeguarding of critical infrastructure, protecting sensitive data from malicious attacks and ensuring the reliable operation of essential services. The system’s ability to adapt to rapidly evolving processor designs promises a future where security is not an afterthought, but an inherent characteristic of computing hardware, fostering greater trust and resilience in an increasingly interconnected world.

Training ReFuzz’s coverage-based model demonstrates performance gains with increased training steps.

The pursuit of robust hardware verification, as exemplified by ReFuzz, demands a methodology rooted in provable efficacy. The framework’s intelligent test reuse, guided by contextual bandit algorithms, isn’t simply about achieving higher coverage; it’s about systematically exploring the design space with a mathematically justifiable approach. This resonates deeply with the assertion of John von Neumann: “The sciences do not try to explain why we exist, but how we exist.” ReFuzz, similarly, doesn’t concern itself with if vulnerabilities exist, but rather how to efficiently locate them through rigorous, algorithmically-driven exploration of processor designs. The core idea of leveraging past knowledge-reusing tests from prior processors-is a testament to the power of structured reasoning in uncovering cross-generational vulnerabilities.

What’s Next?

The promise of ReFuzz – the leveraging of past design iterations to inform present vulnerability discovery – skirts a fundamental truth of hardware verification: test suites, even reused ones, remain approximations. While contextual bandits offer a pragmatic means of navigating the test space, they are, at their core, heuristics. The framework’s efficacy hinges on the assumption that vulnerabilities exhibit lineage – that flaws in prior generations presage similar weaknesses in subsequent designs. Should this assumption prove consistently inaccurate, the benefits of test reuse diminish, and the computational overhead of the bandit algorithm becomes purely detrimental.

A critical, largely unexplored area lies in formally characterizing the ‘distance’ between processor generations. The current approach implicitly treats test suite similarity as a proxy for architectural resemblance. A more rigorous investigation into quantifiable metrics of design divergence – beyond simple instruction set compatibility – is warranted. Such metrics could enable a more principled approach to test selection, moving beyond empirical observation toward provable guarantees of coverage transfer.

Ultimately, ReFuzz, like all automated fuzzing techniques, addresses symptoms, not causes. The persistent discovery of cross-generational vulnerabilities suggests a systemic failure in the initial design process. A truly elegant solution would not rely on reactive testing, but on formal methods capable of proving the absence of vulnerabilities during the design phase – a pursuit that remains, stubbornly, the ultimate benchmark.

Original article: https://arxiv.org/pdf/2512.04436.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/