Never expected that the first tangible application of AI x Crypto is in security auditing

链捕手Publicado em 2026-06-26Última atualização em 2026-06-26

Resumo

Unexpectedly, the initial major application of AI in the Crypto sphere has turned out to be security auditing. In 2026, DeFi has faced significant security challenges, with 121 hacking incidents resulting in approximately $942 million in losses. While AI was expected to first impact areas like quantitative trading, its initial breakthrough has instead transformed security auditing by drastically lowering the cost and skill barrier for finding smart contract vulnerabilities. The traditional audit model is facing obsolescence. Advanced AI models, such as Claude Mythos, enable attackers to scan thousands of contracts and identify vulnerability patterns at scale, compressing the time from discovery to execution to mere minutes. This renders the month-long validity of traditional audit reports ineffective. Notably, attacks now frequently target well-audited, established protocols by exploiting business logic flaws, operational security weaknesses, and even years-old historical contracts, demonstrating that old audit reports offer zero protection. This pressure is forcing a fundamental shift in the industry. In the short term, a wave of defensive re-auditing is occurring, driven by projects seeking to meet new AI-era security standards and regulatory requirements. In the long run, audit firms' business models are diverging. The one-time report delivery model is declining in value, as evidenced by platforms like Code4rena shutting down. Leading firms are now pivoting towards AI-po...

Data shows that as of June, the Total Value Locked (TVL) in DeFi has dropped from approximately $115 billion at the beginning of the year to around $70 billion, a decline of 39%, with losses occurring almost every month.

Meanwhile, security incidents have added another layer of pressure on DeFi. According to statistics, there have been 121 hacker attacks in the DeFi sector since 2026, resulting in cumulative losses of approximately $942 million. In the second quarter alone, 85 incidents occurred, with losses reaching $775 million, making it the quarter with the highest frequency of attack activity during the statistical period.

With the proliferation of a new generation of AI tools, the cost and skill requirements for finding vulnerabilities in smart contracts have significantly decreased,forcing security audit companies to the center of this transformation.

I. The AI-ization of the Attack Side, and the Failure of Old Security Defenses

The Collapse of Old Logic

Whenever the industry discusses the impact of AI on the crypto space, the first thoughts often go to quantitative trading, robo-advisors, and on-chain data analysis. However, the direction of reality has taken everyone by surprise: AI has first broken through what was originally considered the most stable business in this industry—security auditing.

Two or three years ago, security audit firms were seen by investment institutions as conservative assets to participate in the crypto industry's boom. The logic was straightforward: whenever a new protocol launches, it needs auditing; the more prosperous the industry, the stronger the audit demand; high client fees, stable income, independent of token price fluctuations.

Immunefi data shows that losses suffered by DeFi protocols due to hacker attacks once dropped 74% from the 2022 peak of $2.62 billion to approximately $680 million in 2025. The proportion of cross-chain bridge attacks in total DeFi losses plummeted from 73% in 2022 to 3% in 2025. The industry generally believed that the continuous maturation of security audits was playing a role.

However, this assessment is gradually being proven wrong.

On June 9, Anthropic released the new-generation AI model Claude Mythos. A viewpoint subsequently emerged in the market: the abnormal increase in recent attack frequency on top protocols may be related to the continuous leap in capabilities of cutting-edge AI models.

Simon Dedic, founder of Moonrock Capital, pointed out that with the proliferation of new-generation AI tools, the cost and skill requirements for finding smart contract vulnerabilities will drop to essentially zero, unaudited protocols will become targets, and known vulnerabilities will be repeatedly exploited.

Data from Chainalysis corroborates this trend: in the past six months, attacks targeting only contracts with unverified source code have caused approximately $36.7 million in losses. Attackers use AI-assisted decompilation of original bytecode to find vulnerabilities, and large language models can now systematically identify vulnerability patterns, scanning thousands of contracts at scale. Protocols like Truebit, Aperture Finance, and Ekubo were among them.

The entire process from discovery to execution by attackers is being compressed to the minute level. The validity period of traditional audit reports is measured in months. This time gap is the most fatal structural flaw in the old audit model.

Audited, but Still Hacked?

The main targets of hacker attacks are no longer second- or third-tier small protocols. Drift Protocol is a leading perpetual contract platform on Solana, and its smart contracts have undergone multiple rounds of audits by several well-known security firms. However, an investigation by security firm TRM Labs revealed that the attacker, through a six-month-long social engineering attack, gradually infiltrated Drift team members and ultimately obtained privileged admin keys.

The situation with KelpDAO was similar. The attacker exploited a vulnerability in the single validator node configuration of the LayerZero cross-chain bridge, forged deposits, minted unbacked tokens, and stole $293 million within 46 minutes. It was later determined that a multi-validator node configuration scheme had been recommended previously but was not adopted. The contract passed the audit, but the infrastructure configuration had flaws, and the loss still occurred.

In those protocols that passed audits, although code correctness was covered, attackers circumvented them by targeting business logic and operational processes.

On the other hand, AI's scanning scope is not limited to new protocols. Web3 security company GoPlus Security pointed out that attackers are using AI technology to mine vulnerabilities in historical contracts deployed years ago on a large scale. On June 9, an Ethereum contract deployed for 7 years, Token of Power, was attacked, resulting in a loss of about $1.5 million. On May 25, a 3-year-old WUSD.fi contract was attacked, losing about $200,000. An old contract deployed 2 years ago for Aztec Network was attacked twice on June 14 and 18, with total losses exceeding $4 million. This indicates that the protective validity period of old audit reports may have already expired.

Just last month, Manuel Aráoz, co-founder of crypto security company OpenZeppelin, stated that he now believes "all DeFi is insecure" and claimed he had advised friends and family to exit all DeFi positions, including Aave, MakerDAO, and Compound. His reasoning is that the ability of AI programming agents to find vulnerabilities has reached a superhuman level, and the structure of smart contract security is extremely asymmetric—the defense side must patch every vulnerability, while the attacker only needs to find one effective entry point.

OpenZeppelin has provided audit services for Aave, Compound, Uniswap, and Coinbase, making it one of the most important smart contract security infrastructure providers in the crypto industry. This statement, coming from him, carries unusual weight.

However, the market also has its disagreements on this. Marc Zeller, an Aave ecosystem contributor, mentioned that less than 10% of DeFi losses in the past year stemmed from code vulnerabilities, with the rest coming from misconfigured risk parameters, improper collateral management, and weak operational security. Michael Heinrich, CEO of 0G Labs, also pointed out that the security of DeFi lending has improved by about 98% compared to the 2020 baseline.

The problem now is that the scope covered by code audits is becoming increasingly limited, while the attacker's strike surface is continuously expanding. The old security framework can no longer provide a convincing answer.

II. The Response and Restructuring by Project Teams and Audit Firms

Although the old audit standards have shown obvious cracks in the face of AI attacks, this does not mean audit demand will disappear. On the contrary, both project teams and audit companies will adjust according to the new reality.

Short-term: The Concentration of Defensive Audit Demand

Many leading protocols that have previously completed audits are now under pressure to be re-audited according to new security standards in the AI era. Project teams are beginning to realize that in the context of continuously improving AI attack capabilities, the protective cycle of traditional audits is shortening.

The nature of this demand is defensive spending, not a signal of healthy industry growth. Security firm CertiK noted in its 2026 regulatory report that smart contract security audits are evolving from an industry best practice to a regulatory access condition, becoming a necessary threshold for license approvals and token listings.

In the short term, this defensive spending will generate a certain amount of audit demand, but it is more of a passive investment by project teams to mitigate risks.

Long-term: The Fundamental Differentiation of Audit Firm Business Models

Audit firms are also feeling the pressure. As attack-side AI tools continue to evolve, leading companies are accelerating the development of their own detection capabilities. Multiple mainstream audit firms have launched AI-assisted audit systems between 2025 and 2026, improving efficiency through multi-model parallel analysis and automated detection.

While efficiency improves, the traditional model faces pressure. The commercial value of delivering a one-time audit report is declining. In the long run, institutions relying on point-to-point reports face the risk of contracting business volumes.

Analysts at J.P. Morgan explicitly stated that ongoing DeFi security incidents are limiting the entry of major institutional investors. This is not just market sentiment; it's an open questioning of the very value proposition of the entire audit industry.

Code4rena, a smart contract audit platform known for its competitive audit model, recently announced its shutdown, with client and researcher resources transferred to Immunefi. This platform had raised $6 million from Paradigm in 2023 and was once seen as a strong complement to the traditional audit model, shutting down less than two years after acquisition.

Image Source:RooData

After experiencing a hacker attack in October 2024, the DeFi lending protocol Radiant, despite 18 months of effort, was unable to recover the funds and announced its shutdown. Ionic Protocol also announced an immediate halt to all operations due to the expanding impact of a security vulnerability.

However, the change is not unidirectional. AI also demonstrates superhuman-level capabilities on the defense side—the question is who uses it first.

The AI-native audit tool Firepan disclosed that during an independent audit of Curve Finance's new AMM contract in April 2026, it discovered a critical composite vulnerability: looking at any single property, the code appeared normal, but under a specific combination of operations, attackers could bypass the donation protection mechanism and withdraw funds.

Curve had previously undergone multiple rounds of review by six independent audit firms and was considered one of the most heavily audited protocols in DeFi, yet this vulnerability remained hidden in the blind spot of manual audits.

Michael Egorov, founder of Curve Finance, later commented that AI is indeed helpful in smart contract security. However, he also noted that AI's success in detecting vulnerabilities in browsers and the Linux kernel cannot be directly applied to smart contracts—smart contracts typically have only a few thousand lines of code, which both humans and conventional AI can fully reason about. The real risks to be wary of, he said, come more from OpSec-level key leaks and supply chain attacks than from code vulnerabilities themselves.

A similar case appeared in the privacy coin space. Security engineer Taylor Hornby, commissioned by the non-profit organization Shielded Labs, used the Anthropic Opus 4.8 model to audit the Zcash protocol and discovered a critical vulnerability in the Zcash Orchard privacy pool that had gone undetected since 2022. Theoretically, it could allow attackers to infinitely mint counterfeit ZEC that cannot be detected on-chain.

Zcash founder Zooko Wilcox subsequently publicly thanked Anthropic. Hornby also stated that he had added Monero (XMR) to the audit queue and would conduct security reviews on more privacy coin projects in the future.

It is reported that OpenZeppelin has launched its Skills system, providing authoritative knowledge of its audited smart contract library to AI programming agents, moving the defense line forward to the development stage.

This is the new direction traditional audit firms are forced to take—shifting from post-hoc review to full-process embedding, from one-time delivery to continuous monitoring, formal verification, and real-time on-chain risk detection.

Conclusion

Overall, the security audit sector is undergoing a transition from a dividend model to a competitive model. AI accelerates attack efficiency while also driving defensive system upgrades. This process not only affects the business models of audit firms but also requires the entire DeFi ecosystem to rethink its approach to security investment.

For project teams, the era of a one-time audit providing lifelong peace of mind is over. Security is no longer a pre-launch formality but infrastructure requiring continuous investment.

For audit firms, passively following AI is no longer sufficient. Players who can more quickly complete a comprehensive reconstruction from tools to service models are more likely to remain at the table in the next phase.

Criptomoedas em alta

Perguntas relacionadas

QAccording to the article, what is the unexpected first major impact of AI on the crypto field?

AThe article states that the unexpected first major impact of AI on the crypto field has been on security auditing. Contrary to initial expectations focusing on quantitative trading or data analysis, AI has most significantly disrupted the security audit business by drastically lowering the cost and skill barrier for finding vulnerabilities, leading to a surge in attacks.

QWhy does the article argue that traditional security audit models are becoming structurally flawed?

AThe article argues that traditional audit models are structurally flawed because the time from an attacker discovering a vulnerability to executing an exploit has been compressed to minutes, while traditional audit reports have a validity period measured in months. This significant time gap creates a critical vulnerability that the old model cannot address.

QWhat does Manuel Aráoz, co-founder of OpenZeppelin, believe about the current state of DeFi security, and what is his reasoning?

AManuel Aráoz, co-founder of OpenZeppelin, now believes that 'all DeFi is unsafe' and has advised friends and family to exit all DeFi positions. His reasoning is that AI programming agents have achieved superhuman capabilities in finding vulnerabilities, and the security structure of smart contracts is fundamentally asymmetric: defenders must patch every single vulnerability, while attackers only need to find one effective entry point.

QHow is the business model of audit companies expected to fundamentally change in the long term according to the article?

AIn the long term, the business model of audit companies is expected to shift fundamentally from a one-time report delivery service to a model of continuous, embedded security. This involves transitioning from post-deployment review to full-process integration, offering services like continuous monitoring, formal verification, and real-time on-chain risk detection instead of a single-point-in-time assessment.

QWhat are two key examples cited in the article where AI-powered audits found critical vulnerabilities missed by multiple traditional human audits?

AThe article cites two key examples: 1) AI audit tool Firepan discovered a critical combinatorial vulnerability in Curve Finance's new AMM contract that was missed by six independent traditional audit firms. 2) An audit of Zcash using Anthropic's Opus 4.8 model uncovered a critical vulnerability in the Zcash Orchard privacy pool, present since 2022, which could theoretically allow unlimited, undetectable minting of fake ZEC.

Leituras Relacionadas

South Korean Institutions' Crypto Race: Dual Explosion of Stablecoins and RWA

**Summary: South Korea's Institutional Crypto Race: Stablecoins and RWA Take Off** South Korea is undergoing a structural shift in its crypto ecosystem, moving beyond its historical role as a major retail trading hub. Major financial institutions and internet platforms are now building institutional-grade blockchain infrastructure, with stablecoins and Real-World Asset (RWA) tokenization as the primary drivers. The push for a regulated Korean won stablecoin market is a major policy and corporate focus. This is driven partly by an estimated $115 billion outflow into dollar stablecoins like USDC, threatening the domestic financial system. Banks (e.g., KB Financial, Hana), payment giants (e.g., Shinhan Card, BC Card), and internet super-apps (KakaoPay, NAVER Pay) are all conducting pilots. The goal is to anchor future digital finance to the Korean won and local regulations. In RWA, South Korea is advancing rapidly within regulatory sandboxes, focusing on unique domestic assets beyond typical global templates like US Treasuries. Projects involve tokenizing ships (with Hyundai Heavy Industries), defense supply chain assets, and K-pop intellectual property, alongside more conventional assets. A legal framework is set for 2027, and platforms like NXT are preparing for regulated trading. Key opportunities for crypto-native projects lie in providing the underlying technology these traditional institutions lack: global distribution channels for tokenized assets, cross-chain liquidity solutions, and enabling infrastructure tools (e.g., for asset packaging and management). Partnerships, such as Solana with Shinhan Card or LayerZero with the Korea Gold Exchange, exemplify this proactive approach. Crucially, user access is being shaped by consumer platforms. NAVER's planned acquisition of Upbit's operator Dunamu and Kakao's development of a unified wallet aim to seamlessly integrate crypto with everyday payments for tens of millions of users. The race is now about which protocols and projects will become the foundational standards as regulation solidifies and institutional adoption accelerates.

Foresight NewsHá 7m

South Korean Institutions' Crypto Race: Dual Explosion of Stablecoins and RWA

Foresight NewsHá 7m

How to Detect AI-Generated Videos? A Review of Dynamic, Traceable, and Explainable Detection Systems

**How to Detect AI-Generated Videos: A Survey on Dynamic, Traceable, and Explainable Detection Systems** With rapid advances in AI video generation (e.g., Sora, Veo), creating highly realistic, multi-minute videos is now possible, widening the gap with detection research. Current AI video detection, often limited to unreliable binary classifications, is insufficient. This survey, accepted at ACL 2026, reframes the goal as **"factual fidelity verification"**—checking if a video's content (who, when, where, what) aligns with the real world perceptually and cognitively. It categorizes AI-generated videos into three paradigms: **Local Manipulation Videos (LMV**, e.g., face swaps), **Audio-Visual Editing (AVE**, e.g., lip-syncing), and **Generative Video Synthesis (GVS**, fully synthetic videos like Sora's). Detection challenges evolve from visual artifacts in LMV to multi-modal inconsistencies in AVE and higher-level world knowledge violations in GVS. The core proposal is a **Vision-Language Dual-View framework** with four hierarchical layers: 1. **Layer 1 (Intrinsic Visual Cues):** Analyzes low-level signal statistics, noise patterns, and physiological signals. 2. **Layer 2 (Spatiotemporal Consistency):** Checks for temporal coherence in object motion and scene dynamics. 3. **Layer 3 (Cross-Modal Consistency):** Verifies alignment between video, audio, and text within the video. 4. **Layer 4 (Language-Guided World-Level Reasoning):** Uses external knowledge, facts, and physical laws to judge semantic plausibility and factual correctness. The survey traces a shift in detection focus from lower layers (1 & 2) toward higher, language-involved layers (3 & 4). It also reviews evolving evaluation metrics and datasets tailored for each video paradigm. The conclusion advocates for a **dynamic, evidence-first detection system** that moves beyond simple classification. Future trustworthy detection requires combining visual evidence (from CV) with semantic reasoning and explanation (from NLP & multimodal AI), ultimately creating traceable and explainable judgments about a video's adherence to real-world constraints.

marsbitHá 43m

How to Detect AI-Generated Videos? A Review of Dynamic, Traceable, and Explainable Detection Systems

marsbitHá 43m

It Turns Out the First Real-World Application of AI x Crypto is in Security Auditing

The article explores the surprising trend where AI's first major impact on crypto has been in security auditing, not in areas like trading or analytics. It details how AI-powered tools are dramatically lowering the barrier to finding smart contract vulnerabilities, enabling attackers to scan thousands of contracts and execute exploits within minutes. This has rendered traditional, manually-produced audit reports with their month-long validity periods increasingly obsolete, creating a critical "structural crack" in the old security model. Cases like Drift Protocol and KelpDAO show that even extensively audited protocols can be hacked through social engineering, operational flaws, or infrastructure misconfigurations beyond pure code review. Attackers are also using AI to find and exploit vulnerabilities in years-old, deployed contracts. Notably, OpenZeppelin's co-founder has expressed a grim view that "all DeFi is insecure" due to AI's asymmetric advantage. In response, the audit industry is undergoing a fundamental shift. While there's a short-term spike in defensive re-audits, the long-term business model is changing. Firms are developing AI-assisted systems and moving from one-time report deliveries towards embedded, continuous services like real-time monitoring and formal verification. Examples include AI tools uncovering critical, previously missed vulnerabilities in heavily audited protocols like Curve Finance and Zcash. The conclusion is that security must become a continuous investment, not a one-time checkbox, and audit firms must rapidly evolve their tools and service models to survive.

marsbitHá 49m

It Turns Out the First Real-World Application of AI x Crypto is in Security Auditing

marsbitHá 49m

Trading

Spot

Artigos em Destaque

Como comprar F

Bem-vindo à HTX.com!Tornámos a compra de Synfutures (F) simples e conveniente.Segue o nosso guia passo a passo para iniciar a tua jornada no mundo das criptos.Passo 1: cria a tua conta HTXUtiliza o teu e-mail ou número de telefone para te inscreveres numa conta gratuita na HTX.Desfruta de um processo de inscrição sem complicações e desbloqueia todas as funcionalidades.Obter a minha contaPasso 2: vai para Comprar Cripto e escolhe o teu método de pagamentoCartão de crédito/débito: usa o teu visa ou mastercard para comprar Synfutures (F) instantaneamente.Saldo: usa os fundos da tua conta HTX para transacionar sem problemas.Terceiros: adicionamos métodos de pagamento populares, como Google Pay e Apple Pay, para aumentar a conveniência.P2P: transaciona diretamente com outros utilizadores na HTX.Mercado de balcão (OTC): oferecemos serviços personalizados e taxas de câmbio competitivas para os traders.Passo 3: armazena teu Synfutures (F)Depois de comprar o teu Synfutures (F), armazena-o na tua conta HTX.Alternativamente, podes enviá-lo para outro lugar através de transferência blockchain ou usá-lo para transacionar outras criptomoedas.Passo 4: transaciona Synfutures (F)Transaciona facilmente Synfutures (F) no mercado à vista da HTX.Acede simplesmente à tua conta, seleciona o teu par de trading, executa as tuas transações e monitoriza em tempo real.Oferecemos uma experiência de fácil utilização tanto para principiantes como para traders experientes.

270 Visualizações TotaisPublicado em {updateTime}Atualizado em 2026.06.02

Como comprar F

Discussões

Bem-vindo à Comunidade HTX. Aqui, pode manter-se informado sobre os mais recentes desenvolvimentos da plataforma e obter acesso a análises profissionais de mercado. As opiniões dos utilizadores sobre o preço de F (F) são apresentadas abaixo.

活动图片