Defying the Odds: Solana's Unexpected Resilience in Spite of the Security Challenges Faced by Developers
- URL: http://arxiv.org/abs/2406.13599v1
- Date: Wed, 19 Jun 2024 14:42:33 GMT
- Title: Defying the Odds: Solana's Unexpected Resilience in Spite of the Security Challenges Faced by Developers
- Abstract summary: We conducted the first comprehensive study on the Solana platform consisting of a 90-minute smart contract code review task.
Our study shows, quite alarmingly, that none of the participants could detect all important security vulnerabilities in a code review task.
In spite of these challenges, our automated analysis on currently deployed Solana smart contracts surprisingly suggests that the prevalence of vulnerabilities is below 0.3%.
- Score: 7.29491274634436
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Solana gained considerable attention as one of the most popular blockchain platforms for deploying decentralized applications. Compared to Ethereum, however, we observe a lack of research on how Solana smart contract developers handle security, what challenges they encounter, and how this affects the overall security of the ecosystem. To address this, we conducted the first comprehensive study on the Solana platform consisting of a 90-minute Solana smart contract code review task with 35 participants followed by interviews with a subset of seven participants. Our study shows, quite alarmingly, that none of the participants could detect all important security vulnerabilities in a code review task and that 83% of the participants are likely to release vulnerable smart contracts. Our study also sheds light on the root causes of developers' challenges with Solana smart contract development, suggesting the need for better security guidance and resources. In spite of these challenges, our automated analysis on currently deployed Solana smart contracts surprisingly suggests that the prevalence of vulnerabilities - especially those pointed out as the most challenging in our developer study - is below 0.3%. We explore the causes of this counter-intuitive resilience and show that frameworks, such as Anchor, are aiding Solana developers in deploying secure contracts.
Related papers
- Steering LLM Viewpoints through Fabricated Evidence Injection [61.22850755490993]
We introduce a two-phase attack framework that first repackages misleading statements with fabricated rationales, then instruct target LLMs to incorporate these viewpoints when responding to relevant queries.<n>Experiments on BBQ, ToxiGen, and our specialized dataset reveal that commercial LLMs without external safety classifiers remain highly vulnerable.<n>Building on this, we explore multiple defense strategies, among which a tailored safety policy enablesgpt-oss-safeguard to achieve 81% detection rate.
arXiv Detail & Related papers (2026-06-04T14:48:23Z) - Credential Leakage in LLM Agent Skills: A Large-Scale Empirical Study [51.717224133855886]
Third-party skills extend LLM agents with powerful capabilities but often handle sensitive credentials in privileged environments.<n>We analyze 17,022 skills (sampled from 170,226 on SkillsMP) using static analysis, sandbox testing, and manual inspection.<n>We identify 520 vulnerable skills with 1,708 issues and derive a taxonomy of 10 leakage patterns (4 accidental and 6 adversarial)
arXiv Detail & Related papers (2026-04-03T14:50:16Z) - SseRex: Practical Symbolic Execution of Solana Smart Contracts [8.403608370743356]
We propose SseRex, the first symbolic execution vulnerability detection approach for finding Solana-specific bugs.<n>Our evaluation of 8,714 bytecode-only contracts shows that our approach outperforms existing approaches.
arXiv Detail & Related papers (2026-03-17T10:33:11Z) - Where Do Smart Contract Security Analyzers Fall Short? [1.6058099298620423]
We evaluate six widely used analyzers on 653 real-world smart contracts.<n>We then survey 150 professional developers and auditors to understand how they use and perceive these tools.<n>Our findings reveal that excessive false positives, vague explanations, and long analysis times are the main barriers to trust and adoption in practice.
arXiv Detail & Related papers (2026-03-01T03:27:05Z) - How Brittle is Agent Safety? Rethinking Agent Risk under Intent Concealment and Task Complexity [55.441602598245744]
Current safety evaluations for LLM-driven agents primarily focus on atomic harms, failing to address sophisticated threats where malicious intent is concealed or diluted within complex tasks.<n>We address this gap with a two-dimensional analysis of agent safety brittleness under the pressures of intent concealment and task complexity.<n>Our findings reveal two critical phenomena: safety alignment degrades sharply and predictably as intent becomes obscured, and a "Complexity Paradox" emerges, where agents seem safer on harder tasks only due to capability limitations.
arXiv Detail & Related papers (2025-11-11T17:27:27Z) - The Alignment Waltz: Jointly Training Agents to Collaborate for Safety [80.16102254128518]
WaltzRL is a novel multi-agent reinforcement learning framework that formulates safety alignment as a collaborative, positive-sum game.<n>At the core of WaltzRL is a Dynamic Improvement Reward (DIR) that evolves over time based on how well the conversation agent incorporates feedback.<n>Our experiments, conducted across five diverse datasets, demonstrate that WaltzRL significantly reduces both unsafe responses and overrefusals.
arXiv Detail & Related papers (2025-10-09T14:03:05Z) - UltraHorizon: Benchmarking Agent Capabilities in Ultra Long-Horizon Scenarios [63.67884284105684]
We introduce textbfUltraHorizon, a novel benchmark that measures the foundational capabilities essential for complex real-world challenges.<n>Agents are designed in long-horizon discovery tasks where they must iteratively uncover hidden rules.<n>Our experiments reveal that LLM-agents consistently underperform in these settings, whereas human participants achieve higher scores.
arXiv Detail & Related papers (2025-09-26T02:04:00Z) - Security Challenges in AI Agent Deployment: Insights from a Large Scale Public Competition [101.86739402748995]
We run the largest public red-teaming competition to date, targeting 22 frontier AI agents across 44 realistic deployment scenarios.<n>We build the Agent Red Teaming benchmark and evaluate it across 19 state-of-the-art models.<n>Our findings highlight critical and persistent vulnerabilities in today's AI agents.
arXiv Detail & Related papers (2025-07-28T05:13:04Z) - A Comprehensive Study of Exploitable Patterns in Smart Contracts: From Vulnerability to Defense [1.1138859624936408]
Vulnerabilities within smart contracts not only undermine the security of individual applications but also pose significant risks to the broader blockchain ecosystem.
This paper provides a comprehensive analysis of key security risks in smart contracts, specifically those written in Solidity and executed on the Virtual Machine.
We focus on two prevalent and critical types (reentrancy and integer overflow) by examining their underlying mechanisms, replicating attack scenarios, and assessing effective countermeasures.
arXiv Detail & Related papers (2025-04-30T10:00:36Z) - Exploring Vulnerabilities and Concerns in Solana Smart Contracts [0.8225825738565354]
This paper studies the current status of security analysis of Solana by researching Solana smart contract security analysis tools.
The data of Solana smart contract security analysis tools are collected and compared with, and the differences are analyzed and some tools are selected for practical testing.
arXiv Detail & Related papers (2025-04-10T03:25:20Z) - Reasoning-Augmented Conversation for Multi-Turn Jailbreak Attacks on Large Language Models [53.580928907886324]
Reasoning-Augmented Conversation is a novel multi-turn jailbreak framework.
It reformulates harmful queries into benign reasoning tasks.
We show that RACE achieves state-of-the-art attack effectiveness in complex conversational scenarios.
arXiv Detail & Related papers (2025-02-16T09:27:44Z) - Jigsaw Puzzles: Splitting Harmful Questions to Jailbreak Large Language Models [50.89022445197919]
Large language models (LLMs) have exhibited outstanding performance in engaging with humans.
LLMs are vulnerable to jailbreak attacks, leading to the generation of harmful responses.
We propose Jigsaw Puzzles (JSP), a straightforward yet effective multi-turn jailbreak strategy against the advanced LLMs.
arXiv Detail & Related papers (2024-10-15T10:07:15Z) - Attack Atlas: A Practitioner's Perspective on Challenges and Pitfalls in Red Teaming GenAI [52.138044013005]
generative AI, particularly large language models (LLMs), become increasingly integrated into production applications.
New attack surfaces and vulnerabilities emerge and put a focus on adversarial threats in natural language and multi-modal systems.
Red-teaming has gained importance in proactively identifying weaknesses in these systems, while blue-teaming works to protect against such adversarial attacks.
This work aims to bridge the gap between academic insights and practical security measures for the protection of generative AI systems.
arXiv Detail & Related papers (2024-09-23T10:18:10Z) - ContractTinker: LLM-Empowered Vulnerability Repair for Real-World Smart Contracts [8.756175353426304]
Smart contracts are susceptible to being exploited by attackers, especially when facing real-world vulnerabilities.
To mitigate this risk, developers often rely on third-party audit services to identify potential vulnerabilities before project deployment.
Existing pattern-based repair tools mostly fail to address real-world vulnerabilities due to their lack of high-level semantic understanding.
arXiv Detail & Related papers (2024-09-15T08:24:01Z) - SUPER: Evaluating Agents on Setting Up and Executing Tasks from Research Repositories [55.161075901665946]
Super aims to capture the realistic challenges faced by researchers working with Machine Learning (ML) and Natural Language Processing (NLP) research repositories.
Our benchmark comprises three distinct problem sets: 45 end-to-end problems with annotated expert solutions, 152 sub problems derived from the expert set that focus on specific challenges, and 602 automatically generated problems for larger-scale development.
We show that state-of-the-art approaches struggle to solve these problems with the best model (GPT-4o) solving only 16.3% of the end-to-end set, and 46.1% of the scenarios.
arXiv Detail & Related papers (2024-09-11T17:37:48Z) - Identifying Smart Contract Security Issues in Code Snippets from Stack Overflow [34.79673982473015]
We introduce SOChecker, a tool to identify potential vulnerabilities in incomplete SO smart contract code snippets.
Results show that SOChecker achieves an F1 score of 68.2%, greatly surpassing GPT-3.5 and GPT-4.
Our findings underscore the need to improve the security of code snippets from Q&A websites.
arXiv Detail & Related papers (2024-07-18T08:25:16Z) - Vulnerabilities of smart contracts and mitigation schemes: A Comprehensive Survey [0.6554326244334866]
This paper presents a literature review combined with an experimental report that aims to assist developers in developing secure smarts.
It provides a list of frequent vulnerabilities and corresponding mitigation solutions.
It evaluates the community most widely used tools by executing and testing them on sample smart contracts.
arXiv Detail & Related papers (2024-03-28T19:36:53Z) - Competition-Level Problems are Effective LLM Evaluators [121.15880285283116]
This paper aims to evaluate the reasoning capacities of large language models (LLMs) in solving recent programming problems in Codeforces.
We first provide a comprehensive evaluation of GPT-4's peiceived zero-shot performance on this task, considering various aspects such as problems' release time, difficulties, and types of errors encountered.
Surprisingly, theThoughtived performance of GPT-4 has experienced a cliff like decline in problems after September 2021 consistently across all the difficulties and types of problems.
arXiv Detail & Related papers (2023-12-04T18:58:57Z) - Fuzz on the Beach: Fuzzing Solana Smart Contracts [3.6618081664084583]
We propose FuzzDelSol, the first binary-only coverage-guided fuzzing architecture for Solana smart contracts.
FuzzDelSol faithfully models runtime specifics such as smart contract interactions.
Our evaluation on 6049 smart contracts shows that FuzzDelSol's bug oracles find bugs with a high precision and recall.
arXiv Detail & Related papers (2023-09-06T13:54:07Z) - Empirical Review of Smart Contract and DeFi Security: Vulnerability
Detection and Automated Repair [36.46679501556185]
Decentralized Finance (DeFi) is emerging as a peer-to-peer financial ecosystem.
smart contracts hold a massive amount of value, making them an attractive target for attacks.
This paper reviews the progress made in the field of smart contract and DeFi security from the perspective of both vulnerability detection and automated repair.
arXiv Detail & Related papers (2023-09-05T17:00:42Z) - An Exploratory Study on the Evidence of Hackathons' Role in Solving OSS
Newcomers' Challenges [54.56931759953522]
We aim to understand and discuss the challenges newcomers face when joining an OSS project.
We collect evidence on how hackathons were used to address those challenges.
arXiv Detail & Related papers (2023-05-16T15:40:19Z) - ESCORT: Ethereum Smart COntRacTs Vulnerability Detection using Deep
Neural Network and Transfer Learning [80.85273827468063]
Existing machine learning-based vulnerability detection methods are limited and only inspect whether the smart contract is vulnerable.
We propose ESCORT, the first Deep Neural Network (DNN)-based vulnerability detection framework for smart contracts.
We show that ESCORT achieves an average F1-score of 95% on six vulnerability types and the detection time is 0.02 seconds per contract.
arXiv Detail & Related papers (2021-03-23T15:04:44Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.