[Paper] SSR: Safeguarding Staking Rewards by Defining and Detecting Logical Defects in DeFi Staking

Source: arXiv - 2601.05827v1

Overview

The paper “SSR: Safeguarding Staking Rewards by Defining and Detecting Logical Defects in DeFi Staking” tackles a surprisingly common but under‑explored class of bugs in DeFi staking contracts—logic errors that let attackers siphon or inflate rewards. By systematically cataloguing these defects and building a static‑analysis tool powered by a large language model (LLM), the authors demonstrate that a sizable fraction of real‑world staking contracts are vulnerable, and they provide a practical way for developers to catch them early.

Key Contributions

• Empirical taxonomy of six distinct logical defect patterns in DeFi staking, derived from 64 real security incidents and 144 audit reports.
• SSR tool: a novel static analysis pipeline that (1) uses an LLM to extract staking‑specific semantics, (2) builds an abstract “staking model,” and (3) checks the model for the defined defect patterns.
• Ground‑truth evaluation on a curated dataset, achieving 92.3 % precision, 87.9 % recall, and an F1‑score of 88.9 %.
• Large‑scale prevalence study on 15,992 publicly deployed staking contracts, revealing that 22 % contain at least one logical defect.
• Open‑source release (or at least reproducible artifact description) enabling the community to run SSR on their own contracts.

Methodology

1. Data Collection – The authors mined public repositories, blockchain explorers, and audit platforms to gather (a) documented incidents where staking rewards were abused, and (b) audit reports that flagged staking‑related logic flaws.
2. Defect Taxonomy – Each incident was manually dissected to identify the root cause, resulting in six defect categories (e.g., reward‑amount manipulation, re‑entrancy‑free repeated claim, improper epoch handling, etc.). Code snippets illustrate the minimal patterns.
3. Model Construction – For a target contract, an LLM (e.g., GPT‑4‑style) parses the Solidity source, extracts key state variables (stake balances, reward rates, timestamps) and functions (deposit, withdraw, claim). These elements are assembled into a lightweight abstract model that captures the flow of tokens and time.
4. Static Analysis Engine – The engine encodes the six defect rules as logical constraints over the abstract model. It then performs symbolic checks (e.g., “can a user increase rewardPerToken without depositing more stake?”).
5. Evaluation – A labeled ground‑truth set (known vulnerable vs. safe contracts) is used to compute precision/recall. The authors also run SSR on the full 15,992‑contract corpus to estimate real‑world exposure.

Results & Findings

The high precision indicates that false positives are rare—developers can trust the warnings. Recall, while not perfect, is strong enough to catch the majority of known logical flaws. The prevalence scan shows that logical defects are far from niche; roughly one in five staking contracts on Ethereum (and compatible chains) suffers from a design flaw that could be exploited for free rewards.

Practical Implications

• For Smart‑Contract Engineers – Integrating SSR into CI pipelines can automatically flag staking‑logic bugs before deployment, reducing audit costs and post‑mortem patches.
• For Auditors – The defect taxonomy serves as a checklist, and SSR can surface hidden patterns that manual review might miss, especially in large codebases.
• For DeFi Platforms – Deploying SSR‑validated staking contracts can improve user trust and lower insurance premiums, as the risk of reward‑drain attacks is demonstrably mitigated.
• For Tooling Ecosystem – SSR demonstrates a viable hybrid approach: LLM‑driven semantic extraction + rule‑based verification. This pattern can be extended to other DeFi primitives (e.g., lending, AMM) where logical correctness is critical.
• For Governance & Risk Teams – The prevalence numbers give a data‑driven baseline for risk assessments; contracts flagged by SSR can be prioritized for deeper review or on‑chain monitoring.

Limitations & Future Work

• LLM Dependency – The accuracy of the abstract model hinges on the LLM’s ability to correctly interpret complex Solidity patterns; edge‑case syntactic tricks may lead to missed defects.
• Static‑Only View – SSR does not consider runtime state (e.g., price oracle updates) that could affect reward calculations, so some dynamic attacks remain out of scope.
• Coverage of Defect Set – The six categories capture known incidents but may not be exhaustive; novel logical attacks could bypass the current rules.
• Cross‑Chain Variants – The study focuses on Ethereum‑compatible contracts; adapting SSR to other EVM‑like or non‑EVM chains may require additional language support.

Future research directions include enriching the model with dynamic analysis, expanding the defect taxonomy through continuous incident mining, and exploring LLM‑fine‑tuning specifically for Solidity semantics to boost extraction reliability.

Authors

• Zewei Lin
• Jiachi Chen
• Jingwen Zhang
• Zexu Wang
• Yuming Feng
• Weizhe Zhang
• Zibin Zheng

Paper Information

• arXiv ID: 2601.05827v1
• Categories: cs.SE
• Published: January 9, 2026
• PDF: Download PDF