Enhancing Smart Contract Security: Front-running Flash Loan DeFi Attacks and Safeguarding Smart Contracts against Oracle Deviations
Bibliographic record
Abstract
This thesis introduces two novel frameworks to defend against malicious attacks in real-time andsecure interactions between smart contracts and oracles in a blockchain system. This thesis proposes FrontDef, a security system to detect malicious transactions and perform front-running to mitigate financial loss. FrontDef monitors each transaction in the pending transaction pool, detecting po- tential attacks. For suspicious transactions, it analyzes the bytecode of the target contract and assembles mimic transactions to replicate attack strategies. Using these assembled transactions, FrontDef preemptively front-runs suspicious attack transactions, preventing financial losses. Empir- ical results demonstrate that FrontDef successfully detects and assembles mimic transactions for all 24 benchmark cases, including 21 historical attacks that occurred on Ethereum and Binance Smart Chain (BSC). We also confirm that FrontDef introduces negligible overhead and does not affect the throughput of Ethereum and BSC clients. In addition, this thesis presents OVer, a framework designed to automatically analyze the behav- ior of decentralized finance (DeFi) protocols encoded in smart contracts when exposed to “skewed” oracle inputs. OVer begins by performing symbolic analysis and constructing a constraints model based on the contract source code. Leveraging an SMT solver, it identifies parameters that ensure secure operation. Additionally, OVer generates guard statements for smart contracts utilizing or- acle values, effectively preventing oracle manipulation attacks. Empirical results show OVer can analyze all ten diverse benchmarks successfully. Current control parameters in most benchmarks prove inadequate when faced with significant oracle deviations. Existing ad-hoc mechanisms, such as introducing delays, often fall short in real-world DeFi protection. In addition, We examine the security attributes of different pricing algorithms and simulate the impact of applying smoothing filters. Drawing insights from the outcomes, we delve into the design considerations for central bank digital currency (CBDC) oracle systems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".