Top Audit Guru Alerts: All DeFi is Unsafe, Withdraw Now!

Odaily星球日报Published on 2026-05-28Last updated on 2026-05-28

Abstract

Leading DeFi security auditor and OpenZeppelin founder Manuel Aráoz has issued a stark warning, declaring all DeFi protocols unsafe and advising the withdrawal of funds, even from established platforms like Aave and MakerDAO. This warning stems from the rapidly growing threat posed by AI-powered hacking tools. Aráoz highlights that AI agents can now identify and exploit smart contract vulnerabilities in minutes, a task that previously took expert teams weeks. This creates a critical asymmetry: defenders must patch every flaw, while attackers need only find one. Recent months have seen a surge in high-profile exploits, with billions lost in April and May alone across protocols like Drift Protocol, Kelp DAO, and THORChain. The acceleration is attributed to AI's ability to perform rapid code scanning, generate automated attack scripts, and even orchestrate social engineering and infrastructure attacks faster than human defenders can respond. The article cites Anthropic's powerful new AI model, Mythos, which demonstrated such proficiency in finding zero-day vulnerabilities that its public release was delayed over security concerns. This evolution fundamentally disrupts DeFi's risk-reward calculus. With yields on reliable protocols falling to single digits, users now face the potential of 100% capital loss for minimal returns. Aráoz's conclusion is that for most users, withdrawing funds to secure wallets is the most rational risk-management choice in the current landscape.

Original | Odaily Planet Daily (@OdailyChina)

Author | Azuma (@azuma_eth)

"I believe all DeFi is no longer secure."

This assertion left by Manuel Aráoz, founder of OpenZeppelin, on X yesterday is like a depth charge, once again shaking the already stagnant DeFi market.

Manuel even stated that he has started advising friends and family to withdraw funds from major DeFi protocols, including blue-chip protocols like Aave, MakerDAO, and Compound, which were once considered low-risk.

This is not alarmist talk from an outsider. On the contrary, Manuel himself is one of the core builders of the DeFi security ecosystem, and OpenZeppelin is one of the industry's most mainstream security auditing firms. Its contract libraries, security standards, and auditing frameworks have permeated almost the entire DeFi world.

The reason for Manuel's complete change in attitude lies in AI. Manuel pessimistically believes that the capability of AI Coding Agents to identify and exploit smart contract vulnerabilities is increasing exponentially.

This means that issues which previously took top white-hat teams weeks to discover might now be scanned by AI in minutes; where hackers needed to study protocol logic extensively, AI can now automatically analyze attack paths; where DeFi's "openness and transparency" was once an advantage, it has now become the best training corpus for attackers.

Manuel also mentioned a more fatal problem: smart contract security is essentially an extremely asymmetric game — defenders must patch all vulnerabilities, while attackers only need to find one to steal funds. As AI begins to exponentially enhance attack efficiency, this asymmetry is rapidly tilting out of balance.

The Icy Reality: DeFi Has Become a Hacker's ATM

Looking back at DeFi security incidents over the past few months, you'll find Manuel's concerns are not exaggerated.

April was arguably one of the worst months in DeFi history.

  • On April 1st, April Fool's Day, Drift Protocol suffered a theft of $280 million due to a manager privilege hijacking and multisig execution vulnerability (see April Fool's Joke? Drift Protocol Hacked for Over $280 Million, Possibly Becoming Solana Ecosystem's Second Largest DeFi Heist).
  • Subsequently on April 19th, Kelp DAO lost $292 million due to a breached bridge protocol (see Another $292 Million Stolen from DeFi, Is Even Aave Unsafe Now?). The hacker later escaped via lending protocols like Aave, casting a shadow of bad debts and their ripple effects over the entire DeFi space.

And since entering May, incidents have not decreased but rather further proliferated.

  • On May 15th, THORChain was attacked. A newly added node operator exploited a vulnerability in the GG20 threshold signature scheme (TSS) to reconstruct the vault's private key and directly execute outbound transactions, causing a loss exceeding $10 million.
  • On May 18th, Verus's bridge protocol was attacked. The attacker forged cross-chain import payloads to bypass verification and extract assets from the Ethereum reserves, stealing approximately $11.58 million.
  • On May 19th, Echo Protocol on Monad was attacked due to a private key leak. The attacker minted 1,000 eBTC (worth $76.7 million) and extracted funds via a previously tested attack path through Curvance.
  • On May 24th, StablR, a compliant stablecoin issuer under the MiCA regulatory framework, was attacked. The hacker profited over $2.8 million by minting EURR and USDR, causing EURR and USDR to depeg.
  • On May 25th, the SquidRouter module was attacked, resulting in the theft of approximately $3 million in assets from 86 Gnosis Safe wallets.
  • On May 27th, the StakeDAO deployer's private key was leaked on Arbitrum. The attacker minted about 5.45 trillion vsdCRV and partially exchanged them for 43.7 ETH to escape.

Frequently occurring security incidents have sounded the alarm. From on-chain code to off-chain management, DeFi seems to be losing ground across the board.

AI Has Become the Hacker's Nuclear Weapon

Why has the DeFi offensive-defensive balance suddenly collapsed this summer? Beyond the evolution of traditional hacking techniques, the rapid advancement of AI large language model capabilities is becoming the ultimate factor tipping the scales.

In the past, finding a complex smart contract vulnerability (especially one involving cross-chain interactions, multi-layer nesting, or extremely hidden reentrancy logic) required top-tier hackers weeks or even months of code analysis. However, with the maturation of AI agents possessing ultra-long context, strong logical reasoning, and autonomous tool-calling abilities, this has undergone a qualitative change.

  • Second-level Scanning and Global "Zero-day Vulnerability" Mining: Attackers only need to feed open-source code repositories to new-generation AI reasoning models, and AI can, within seconds, deduce hundreds of extreme interaction scenarios like a seasoned security expert, precisely identifying boundary conditions that human auditors might miss due to fatigue.
  • Automated Attack Script Generation: AI can not only discover vulnerabilities but also automatically write, test, and deploy "hacker smart contracts" designed to extract funds.
  • Perfect Orchestration of Off-chain DevOps and Social Engineering: AI can impersonate a perfect developer for phishing or monitor a DeFi team's GitHub commits 24/7. Once the team uploads code containing sensitive information or unverified fixes, AI can launch an attack within seconds—far faster than any human security personnel can respond.

In this AI-augmented security war, hackers, armed with AI, possess nearly unlimited ammunition and attack speeds measured in seconds. In contrast, DeFi, constrained by slow-paced governance voting, multisig confirmations, and delayed security audits, struggles to mount a corresponding defense.

Last month, Anthropic, the AI development company behind Claude, officially announced its new-generation model, Mythos (see Anthropic Develops the Most Powerful AI Model in History, But Dares Not Release It...). This is the first model in human history to exceed ten trillion parameters (in contrast, current mainstream models range from hundreds of billions to one trillion parameters), with a staggering training cost of $10 billion.

However, due to Mythos's specialized capabilities in cybersecurity (Anthropic disclosed that they identified thousands of zero-day vulnerabilities using Mythos in just a few weeks), the company even dares not release the model publicly directly, fearing malicious use by hacker groups. Instead, they plan to allow leading tech giants to test it first through a "Project Glasswing" to patch potential vulnerabilities in advance.

If the current DeFi security landscape is already this severe, it's hard to imagine what new threats industry security defenses will face once Mythos is publicly released.

The Biggest Problem: The Risk-Reward Ratio Has Long Been Out of Balance

For ordinary DeFi participants, liquidity providers (LPs), and whales, the most important issue now is to sit down and do the math.

For a long time, the reason users chose to deposit funds into DeFi was the pursuit of annualized yields several times higher than those in traditional finance. During bull markets or frenzied periods of liquidity mining, yields of 10%, 20%, or even higher were enough to cover people's psychological expectations for "potential technical risks."

But today, this underlying logic has long been shaken, even overturned. The risk-reward ratio of DeFi is already out of balance. On the reward side, as the market enters a phase of stock game competition and security cushions thicken, the real yields of most mainstream, relatively reliable DeFi protocols have fallen back to single-digit percentages. On the risk side, users' principal is exposed to a black box that could be breached by AI at any moment, emptied by flash loans in an instant. Once a protocol is hacked, token prices plummeting to zero and liquidity pools being drained often happen within minutes, with no legal recourse, insurance, or central bank to cover the losses.

The gamble of risking 100% principal loss for an annualized return of around 5% is clearly not a worthwhile bet.

Manuel's words may be somewhat absolute, but they tear off DeFi's final fig leaf. In the face of the reality where hackers have made AI a conventional weapon and security incidents keep erupting in the industry, if you are not mentally prepared to risk losing 100% of your principal for a certain return, then "withdrawing funds as soon as possible and securing profits" might be the most rational, most risk-control-compliant choice in the current market cycle.

Related Questions

QAccording to the article, who is Manuel Aráoz and why is his warning about DeFi security considered significant?

AManuel Aráoz is the founder of OpenZeppelin, a leading security audit firm in the crypto industry. His warning is significant because he is a core builder of the DeFi security system, and his company's contract libraries, security standards, and audit frameworks are widely used across the DeFi ecosystem. His shift in stance carries substantial weight due to his deep expertise and role in the industry.

QWhat is the primary reason cited by Manuel Aráoz for his belief that all DeFi is now insecure?

AThe primary reason is the exponential improvement in AI (specifically AI Coding Agents) in identifying and exploiting smart contract vulnerabilities. AI can now find issues in minutes that once took top security teams weeks, automate the analysis of attack paths, and leverage the public nature of DeFi code as training data. This massively amplifies the inherent asymmetry in security where attackers need only find one flaw while defenders must patch all of them.

QWhat is the 'Mythos' model mentioned in the article, and why is it considered a potential threat?

AMythos is a new AI model developed by Anthropic, the company behind Claude. It is the first model to surpass 10 trillion parameters, with a training cost of $10 billion. It is considered a potential threat because Anthropic disclosed that in just a few weeks, Mythos identified thousands of zero-day vulnerabilities. Due to its specialized capabilities in cybersecurity, Anthropic is hesitant to release it publicly for fear it could be maliciously used by hackers to exploit vulnerabilities at an unprecedented scale.

QThe article argues that the risk-reward ratio for DeFi participation has become unbalanced. What is the core of this argument?

AThe core argument is that the potential rewards (returns) from mainstream DeFi protocols have fallen to single-digit percentages in the current market, while the risks have skyrocketed. Users now risk losing 100% of their principal in minutes due to AI-enhanced hacks, with no legal recourse, insurance, or central bank backstop. The article frames this as an irrational trade-off: risking total loss for a relatively low annual yield.

QBesides smart contract code, what other aspects of DeFi infrastructure have been targeted in recent hacks according to the article's examples?

ARecent hacks have targeted vulnerabilities beyond just smart contract code. Examples include bridge protocols (Kelp DAO, Verus), management/private key compromises (Drift Protocol, StakeDAO, Echo Protocol), threshold signature schemes (THORChain), and wallet management modules (SquidRouter). This indicates that security weaknesses exist across the entire DeFi stack, from on-chain code to off-chain operational and key management practices.

Related Reads

Consumer Confidence Hits Bottom, Macro Correlations Simultaneously Break Down: How Much Longer Can the U.S. Stock Market's Solo Rally Last?

The U.S. stock market is exhibiting a rare divergence: while consumer confidence hits historic lows and traditional macro asset correlations break down, major indices like the Nasdaq 100 and S&P 500 continue reaching record highs, fueled primarily by AI and semiconductor momentum. The rally is now highly concentrated, with strength rotating from giants like Nvidia to higher-beta plays within semiconductors, particularly memory chips. This surge occurs despite a significant split between pessimistic consumer sentiment and still-resilient actual spending behavior, partially supported by fiscal stimulus. Goldman Sachs traders highlight a critical structural fissure: correlations between the S&P 500 and key macro assets (rates, gold, VIX, oil) have deviated extremely from long-term historical norms. Concurrently, the market is in a negative Gamma regime, making it more sensitive to price moves, and hedge fund positioning in momentum and semiconductors is at crowded levels. The sustainability of this "solo rally" faces three main constraints: 1) Oil price volatility linked to Middle East geopolitical risks, 2) Extreme crowding in semiconductor and momentum trades, increasing vulnerability to disappointments, and 3) The breakdown of traditional macro correlations, suggesting the rally reflects a specific mix of factors rather than broad-based risk appetite. The key question is not if indices can rise further, but which variable—oil, rates, or semiconductor momentum—might trigger a repricing of the current fragile logic.

marsbit30m ago

Consumer Confidence Hits Bottom, Macro Correlations Simultaneously Break Down: How Much Longer Can the U.S. Stock Market's Solo Rally Last?

marsbit30m ago

Will OpenAI Swallow the Application Layer? a16z Says Real Opportunities Lie Outside General Models

As large language models (LLMs) from companies like OpenAI and Anthropic become more powerful, many fear they will dominate the AI application layer, leaving no room for startups. However, this article argues that the real opportunity lies not on the "Yellow Brick Road"—the high-profile, general-purpose tasks like code and text generation that model labs are directly pursuing—but in the "rest of Oz": complex, vertical-specific applications. On the Yellow Brick Road, model companies have inherent advantages: control over the model, better margins, pricing power, and strong distribution. Startups building generic, horizontal "co-pilot" tools for standard tasks are competing directly on this path and are vulnerable. True defensibility and value are found in specialized, vertical applications. These involve deep integration into messy, multi-step business workflows (e.g., sales, insurance, legal), handling legacy systems, data quality issues, compliance, and governance. The "scaffolding" around the model—the specialized tools, automations, workflows, and industry knowledge—becomes more critical than the raw model power itself. Vertical AI companies can build defensible moats through: * **Data & Learning Flywheels:** Capturing unwritten industry practices and specific customer feedback not found in public training data. * **Managing Model Complexity:** Routinely evaluating and routing queries across multiple models (including open-source) to optimize for performance and cost, and absorbing the migration burden of model upgrades for clients. * **Cost Optimization:** Using cheaper, fine-tuned models for specific sub-tasks instead of always calling the most expensive, general-purpose model. * **Governance & Compliance:** Providing the control plane for permissions, auditing, and ensuring compliance with industry-specific regulations (e.g., HIPAA, FINRA). Examples from sales (11x) and insurance (FurtherAI) illustrate that clients pay for systems that drive specific business outcomes (e.g., sales pipeline, policy underwriting), not for generic intelligence. These systems become the "operational memory" of a business, a layer that is hard to replace, even as the underlying LLMs commoditize and improve. To test if a startup is building in the "rest of Oz," it should pass checks like the **Tool & Steps Test** (requires complex, multi-step workflows), the **System Test** (owns the end-to-end workflow, not just a tool on top), and the **Hedge Fund / P&L Test** (measured by client business outcomes, not benchmark scores). Both model labs and vertical application companies will win. The next generation of enterprise software will be built in the specialized, complex, and high-value territory beyond the Yellow Brick Road.

marsbit57m ago

Will OpenAI Swallow the Application Layer? a16z Says Real Opportunities Lie Outside General Models

marsbit57m ago

'ASIC Giant' Marvell Sets Record Quarterly Revenue, Raises Guidance Again, CEO Says Data Center Business Is 'On Fire'

Marvell Technology, a leading player in custom AI chips and data center connectivity, reported record revenue for its fiscal Q1 2027, driven by explosive demand in its data center business. Revenue reached $2.418 billion, slightly surpassing expectations, though GAAP net income fell year-over-year due to acquisition-related costs. Crucially, data center revenue hit $1.83 billion, making up 76% of the total and growing 27% YoY. The company significantly raised its full-year and next-year guidance, citing "exceptionally strong AI-related orders." Revenue is now projected at ~$11.5 billion for FY2027 and ~$16.5 billion for FY2028. CEO Matt Murphy emphasized that growth in the data center segment is accelerating. The AI Interconnect business, now expected to grow over 70% annually, saw its forecast lifted again due to rising network demands in complex AI models. Additionally, Marvell's custom chip (XPU) business is on a steep growth path, with FY2028 revenue anticipated to double and a target of over $10 billion by FY2029. The company also expanded its strategic collaboration with NVIDIA, focusing on silicon photonics, system integration, and AI-RAN solutions. To secure supply for surging demand, Marvell plans about $1 billion in supplier prepayments this fiscal year, highlighting its long-term capacity planning. Despite the strong results, the stock dipped slightly in after-hours trading.

marsbit1h ago

'ASIC Giant' Marvell Sets Record Quarterly Revenue, Raises Guidance Again, CEO Says Data Center Business Is 'On Fire'

marsbit1h ago

Trading

Spot
Futures
活动图片