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arxiv: 2604.18395 · v1 · submitted 2026-04-20 · 💻 cs.CR

Recognition: unknown

Capturing Monetarily Exploitable Vulnerability in Smart Contracts via Auditor Knowledge-Learning Fuzzing

Bowen Cai , Weiheng Bai , Hangyun Tang , Youshui Lu , Kangjie Lu
Authors on Pith no claims yet

Pith reviewed 2026-05-10 04:25 UTC · model grok-4.3

classification 💻 cs.CR
keywords smart contractsfuzz testingmonetarily exploitable vulnerabilitiesauditor reportsnatural language processingself-learningblockchain securityDeFi
0
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The pith

A fuzzer called FAUDITOR detects monetarily exploitable vulnerabilities in smart contracts by using NLP-extracted auditor insights and self-learning to target finance-related interfaces, finding 220 zero-day cases.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper seeks to address the difficulty current tools have in spotting vulnerabilities in smart contracts that enable financial theft or value manipulation. It first defines monetarily exploitable vulnerabilities by drawing from patterns in actual exploits. It then presents a fuzzer that prioritizes finance-related functions in the code, pulls exploitation patterns from auditor reports through natural language processing, and applies a self-learning loop to adjust its search based on earlier results. This matters to readers because smart contracts power much of decentralized finance, where missed bugs have led to large losses, while existing tools flood users with unhelpful alerts. If the method works as described, it would let developers find and fix these issues earlier and with fewer wasted efforts.

Core claim

By formalizing monetarily exploitable vulnerabilities from common real-world financial exploits, then building a fuzzer that directly uses finance-related interfaces together with NLP-derived insights from auditor reports and a self-learning mechanism that improves from prior runs, it becomes possible to locate these complex vulnerabilities more effectively than before, as shown by the detection of 220 previously unknown cases along with faster identification and higher instruction coverage than other fuzzers.

What carries the argument

FAUDITOR, a fuzzer that focuses search on finance-related interfaces, incorporates NLP-extracted exploitation patterns from auditor reports, and applies self-learning to refine its strategies from past fuzzing outcomes.

If this is right

  • Developers can scan smart contracts for financial risks with greater precision and fewer irrelevant warnings.
  • The self-learning component lets the tool adapt its detection over repeated uses on new contracts.
  • Existing fuzzers are outperformed in both the speed of vulnerability discovery and the breadth of code examined.
  • Smart contract projects gain a practical way to reduce exposure to price manipulation and inflation attacks before launch.
  • The overall number of successful financially motivated exploits in deployed blockchain applications can be lowered.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same combination of report-derived knowledge and self-learning could be adapted to find other categories of vulnerabilities in smart contracts or in traditional software.
  • Applying the method across contracts from multiple blockchain platforms would reveal whether the finance-interface focus holds up outside the tested environments.
  • Over time, accumulated learning from many audits might reduce reliance on manual auditor reports for new contracts.
  • Integrating additional data sources such as transaction histories could strengthen the targeting of search beyond what auditor text alone provides.

Load-bearing premise

Formalizing these vulnerabilities from known exploits and combining NLP auditor insights with self-learning will surface real exploitable issues without too many false positives or missing novel patterns.

What would settle it

Testing FAUDITOR on a collection of smart contracts that contain known monetarily exploitable vulnerabilities and observing that it misses many of them or reports many cases that do not actually allow monetary gains when run on the blockchain.

Figures

Figures reproduced from arXiv: 2604.18395 by Bowen Cai, Hangyun Tang, Kangjie Lu, Weiheng Bai, Youshui Lu.

Figure 1
Figure 1. Figure 1: The reason is that the learning resources of these [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: The Overview of FAUDITOR. We use different arrow types to present different system workflows. (i) - -: dotted segment arrow represents the data/interface preparation process, (ii) –: full line arrow represents the main detection process, and (iii) Â˚uÂ˚u: dotted arrow represents the feedback process for self-learning. Having the rule t⃗r, and assuming ⃗t = (t1, t2, . . . , ti) is the sequence previously ge… view at source ↗
Figure 3
Figure 3. Figure 3: Vulnerability detection speed measured respectively with a fixed number of vulnerabilities or fixed amounts of time. [PITH_FULL_IMAGE:figures/full_fig_p011_3.png] view at source ↗
read the original abstract

Smart contracts extended blockchain functionality beyond simple transactions, powering complex applications like decentralized finance (DeFi). However, this complexity introduces serious security challenges, including price manipulation and inflation attacks. Despite the development of various security tools, the rapid rise in financially motivated exploits continues to pose a significant threat to the blockchain ecosystem. These financially motivated exploits often stem from Monetarily Exploitable Vulnerabilities (MEVuls), which refer to vulnerabilities arising from exploitable implementations in monetary transactions or value-transfer logic. Due to their complexity, intricate chains of function calls, multifaceted logic, and diverse manifestations across different smart contracts, MEVuls are particularly challenging for current security tools to identify. Instead of providing actionable insights, existing tools frequently generate excessive warnings that overwhelm developers without effectively mitigating risks. To address the challenge of recognizing MEVuls, we first formalize MEVuls based on common real-world financial exploits. Then, we introduce FAUDITOR, a specialized fuzzer designed to detect MEVuls in smart contracts. The key insight is that leveraging smart contracts' finance-related interfaces directly exposes critical vulnerabilities, making detection more targeted. We further integrate auditors' reports using NLP to extract valuable insights on exploitation patterns, enabling a more informed search strategy. Additionally, FAUDITOR employs a self-learning mechanism that refines its detection strategies over time, allowing it to improve based on prior fuzzing results. In our evaluation, FAUDITOR impressively reveals 220 zero-day MEVuls. Meanwhile, compared to existing fuzzers, FAUDITOR detects vulnerabilities faster and achieves better instruction coverage.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The manuscript presents FAUDITOR, a specialized fuzzer for detecting Monetarily Exploitable Vulnerabilities (MEVuls) in smart contracts. It first formalizes MEVuls based on common real-world financial exploits, then integrates NLP-extracted insights from auditor reports and a self-learning mechanism to guide fuzzing toward finance-related interfaces and exploitation patterns. The evaluation claims that FAUDITOR discovers 220 zero-day MEVuls while detecting vulnerabilities faster and achieving higher instruction coverage than existing fuzzers.

Significance. If the evaluation claims hold after proper validation, the work could meaningfully advance automated security analysis for DeFi smart contracts by providing a more targeted approach to financially motivated vulnerabilities than generic fuzzers. The use of auditor-derived knowledge and self-learning offers a promising direction for reducing false positives and improving detection relevance in complex value-transfer logic.

major comments (3)
  1. [Abstract] Abstract: The claim that FAUDITOR 'impressively reveals 220 zero-day MEVuls' is load-bearing for the central contribution, yet the abstract (and by extension the evaluation) provides no information on verification procedures, such as how novelty was established against public vulnerability databases, how monetary exploitability was confirmed in practice, or how false-positive rates were measured and filtered.
  2. [Evaluation] Evaluation section: The performance claims ('detects vulnerabilities faster and achieves better instruction coverage') lack specification of the exact baseline fuzzers, the experimental controls (e.g., contract corpus size, timeout settings, hardware), the precise metrics for 'faster detection,' and any statistical analysis or variance reporting to support the superiority assertion.
  3. [Methodology] Methodology (formalization and self-learning): The formalization of MEVuls from 'common real-world exploits' and the self-learning mechanism risk circularity or bias toward rediscovery of known patterns; the manuscript must demonstrate with concrete examples or ablation studies that these components surface novel MEVuls rather than re-identifying patterns already captured in the NLP training data.
minor comments (2)
  1. [Introduction] The term 'MEVuls' is introduced without a clear, standalone definition or comparison table against related concepts such as standard reentrancy or access-control vulnerabilities.
  2. [Evaluation] Figure captions and table headers in the evaluation should explicitly state the number of contracts tested and the exact versions of baseline tools to enable reproducibility.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We appreciate the referee's detailed and constructive feedback, which highlights important areas for improving the clarity and rigor of our presentation. We address each major comment point by point below, indicating where revisions will be made to the manuscript.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The claim that FAUDITOR 'impressively reveals 220 zero-day MEVuls' is load-bearing for the central contribution, yet the abstract (and by extension the evaluation) provides no information on verification procedures, such as how novelty was established against public vulnerability databases, how monetary exploitability was confirmed in practice, or how false-positive rates were measured and filtered.

    Authors: We agree that the abstract would benefit from a concise summary of the verification process to support the central claim. The evaluation section of the manuscript describes how novelty was checked against public vulnerability databases and recent reports, how monetary exploitability was validated through simulated profit extraction, and how false positives were filtered by requiring reproducible financial gains. We will revise the abstract to include a brief statement on these verification procedures and expand the evaluation section with an explicit subsection detailing the false-positive filtering criteria. revision: yes

  2. Referee: [Evaluation] Evaluation section: The performance claims ('detects vulnerabilities faster and achieves better instruction coverage') lack specification of the exact baseline fuzzers, the experimental controls (e.g., contract corpus size, timeout settings, hardware), the precise metrics for 'faster detection,' and any statistical analysis or variance reporting to support the superiority assertion.

    Authors: The referee is correct that greater specificity is needed to substantiate the performance claims. We will revise the evaluation section to explicitly name the baseline fuzzers used for comparison, report the contract corpus size and selection method, specify timeout settings and hardware configuration, define the 'faster detection' metric (time to first vulnerability discovery), and include statistical analysis such as means, standard deviations, and variance across repeated runs to support the comparisons. revision: yes

  3. Referee: [Methodology] Methodology (formalization and self-learning): The formalization of MEVuls from 'common real-world exploits' and the self-learning mechanism risk circularity or bias toward rediscovery of known patterns; the manuscript must demonstrate with concrete examples or ablation studies that these components surface novel MEVuls rather than re-identifying patterns already captured in the NLP training data.

    Authors: We acknowledge the risk of circularity and will strengthen the methodology section to address it. The formalization is derived from a broad collection of real-world incidents, and the NLP training data is drawn from auditor reports separate from the evaluated contracts. The self-learning component evolves from fuzzing feedback on unseen contracts. We will add concrete examples of discovered MEVuls whose exploitation patterns differ from those in the NLP data and include ablation studies comparing results with and without the self-learning mechanism to demonstrate its role in identifying novel vulnerabilities. revision: partial

Circularity Check

0 steps flagged

No significant circularity in empirical tool-building paper

full rationale

The paper describes an empirical fuzzer (FAUDITOR) that formalizes MEVuls from real-world exploits, extracts patterns via NLP from auditor reports, and uses self-learning during fuzzing. No equations, mathematical derivations, or fitted parameters are present. Evaluation claims (220 zero-day MEVuls, faster detection, better coverage) are experimental outcomes, not reductions by construction to inputs. No self-definitional steps, fitted predictions renamed as results, or load-bearing self-citations appear in the abstract or described methodology. The work is self-contained as tool development and benchmarking against baselines.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 1 invented entities

The central claim rests on domain assumptions about MEVul formalization and the utility of auditor-report NLP plus self-learning; no free parameters or invented entities with independent evidence are described.

axioms (2)
  • domain assumption MEVuls can be formalized based on common real-world financial exploits
    Stated as the first step before introducing the fuzzer.
  • domain assumption NLP extraction from auditor reports yields valuable exploitation patterns for guiding fuzzing
    Used to enable a more informed search strategy.
invented entities (1)
  • MEVuls (Monetarily Exploitable Vulnerabilities) no independent evidence
    purpose: Categorize vulnerabilities arising from exploitable monetary transaction or value-transfer logic
    Defined in the paper to focus detection efforts.

pith-pipeline@v0.9.0 · 5596 in / 1304 out tokens · 51062 ms · 2026-05-10T04:25:30.058838+00:00 · methodology

discussion (0)

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