When AI Solves Security, Will DeFi Return to Its Golden Age?

marsbitОпубліковано о 2026-04-03Востаннє оновлено о 2026-04-03

Анотація

The article discusses how AI is revolutionizing DeFi security, potentially ushering in a new era of innovation reminiscent of the 2020 DeFi Summer. Previously, high security costs and lengthy audit processes stifled experimentation, as developers avoided unproven ideas due to financial and time constraints. However, AI tools like Nemesis are now drastically reducing these barriers by efficiently detecting complex vulnerabilities, such as reentrancy and economic attacks, with deep contextual understanding and low false positives. These tools, combined with platforms like Battlechain, enable a streamlined workflow: code is written, AI-audited within minutes, deployed on a test chain for real-world attack simulations, and quickly refined. This process compresses development cycles from months to hours at minimal cost. Future advancements may include wallet-integrated AI audits, allowing users to scan contracts in real-time before signing transactions. AI is set to transform DeFi security across development, chain, and user layers, reopening the door to rapid experimentation and innovation. This shift could empower individual developers to create groundbreaking protocols quickly and safely, much like the early pioneers of DeFi.

Written by: nour

Compiled by: Chopper, Foresight News

During the DeFi Summer of 2020, Andre Cronje was launching new protocols almost every week, with Yearn, Solidly, and a host of other experimental projects emerging. Unfortunately, many of these projects fell victim to contract vulnerabilities and economic attacks, resulting in losses. But the ones that survived have become some of the most important protocols today.

The problem is, that era left a psychological scar on the entire industry. The industry's focus shifted dramatically, pouring vast resources into security. Multiple audits, audit competitions, months of review for each version, all just to validate a new idea with no market fit. I think most people don't realize how much this stifled the spirit of experimentation. No one will spend $500,000 and wait 6 months for an audit for an unproven idea. So everyone just clones verified designs and calls it innovation. DeFi innovation hasn't died; it's just that the incentives are killing it.

And this is changing because AI is driving down security costs at an astonishing rate.

AI audits used to be laughably shallow, basically only flagging surface-level issues like reentrancy and precision loss that any competent auditor could spot. But the new generation of tools is completely different. Tools like Nemesis can already detect complex execution flow vulnerabilities and economic attacks, demonstrating a stunning depth of contextual understanding of the protocol and its operating environment. One particularly standout feature of Nemesis is how it handles false positives: it uses multiple agents to detect issues with different methods, then has another independent agent judge the results, filtering out false positives based on contextual understanding of the protocol's logic and goals. It truly understands nuances, like in which scenarios reentrancy is acceptable and when it's truly dangerous—something even experienced human auditors often get wrong.

Nemesis is also incredibly simple, requiring just three Markdown files added as a skill to Claude Code. Other tools go even further, some integrating symbolic execution and static analysis, others even automatically writing formal verification specifications and verifying the code. Formal verification is becoming accessible to everyone.

But these are just first-generation tools. The models themselves are continuously evolving. Anthropic's upcoming Mythos is expected to far surpass the capabilities of Opus 4.6. You don't need to make any changes; just run Nemesis on Mythos and immediately get stronger results.

Combined with Cyfrin's Battlechain, the entire security workflow is completely restructured: write code → audit with AI tools → deploy to Battlechain → live attack and defense testing → redeploy to mainnet.

The beauty of Battlechain is that it eliminates the implied 'safety expectation' of the Ethereum mainnet. All users bridging in are fully aware of the risks they are taking. It also provides a natural focal point for AI auditors, no longer needing to search for a needle in the mainnet haystack. Its safe harbor framework stipulates that 10% of stolen funds can be a legitimate bounty, creating an economic incentive that drives the development of more powerful attack tools. Essentially, this is competition similar to MEV, but happening in the security realm. AI agents will probe every new deployment at top speed, racing to find vulnerabilities.

The future process for DeFi protocol development will be:

  1. Write the protocol
  2. Complete an AI audit in minutes
  3. Deploy to Battlechain with a small amount of capital
  4. Get automatically targeted by competing AI agents
  5. Get attacked within minutes
  6. Recover 90% of funds
  7. Fix the vulnerability
  8. Redeploy

The entire cycle, from writing code to being battle-tested and deployed on the mainnet, is compressed from months to potentially just hours, with costs almost negligible compared to traditional audits.

The ultimate line of defense will be wallet-level AI auditing. User wallets can integrate the same AI audit tools at the transaction signing stage. Before signing each transaction, the AI will audit the target contract code, read state variables to link all relevant contracts, map out the protocol topology, understand the context, audit both the contract and the user's transaction input, and provide recommendations in the confirmation pop-up. Every user will ultimately run their own professional-grade audit agent, protecting themselves from rugs, team negligence, or malicious frontends.

Agents will protect DeFi protocols from all angles: development, public chain, and user layers. This reopens the entire experimental design space. Ideas that were once economically unviable due to high security costs can finally be tested. One person in their bedroom can iterate quickly and create billion-dollar protocols, just as Andre and others did in 2020. The era of live testing is back.

Пов'язані питання

QWhat was the main factor that stifled innovation in DeFi after the 2020 DeFi Summer, according to the article?

AThe article states that the industry's shift towards pouring massive resources into security, such as requiring multiple audits, audit competitions, and months of review for each new version, significantly stifled the experimental spirit. The high cost (e.g., $500,000) and long wait times (e.g., 6 months) for auditing unproven ideas killed innovation, leading to mostly forking of verified designs.

QHow is AI currently changing the security cost and process for DeFi protocol development?

AAI is drastically reducing security costs and speeding up the process. New-generation tools like Nemesis can find complex execution flow vulnerabilities and economic attacks with deep contextual understanding. The future development process involves writing code, getting an AI audit in minutes, deploying to a test environment like Battlechain for real-world attacks, and then deploying to mainnet, compressing a months-long cycle into potentially just hours.

QWhat is the specific role of a tool like 'Battlechain' in the new security workflow described?

ABattlechain, specifically mentioned as Cyfrin's Battlechain, is a test environment that restructures the security workflow. It eliminates the implicit 'safety expectation' of the Ethereum mainnet, provides a natural focal point for AI auditors, and has a 'safe harbor' framework where 10% of stolen funds are a legal bounty. This creates economic incentives for powerful attack tools to emerge, allowing protocols to be tested and attacked by competing AI agents quickly before mainnet deployment.

QWhat is the envisioned final line of defense for user security in DeFi ecosystem according to the article?

AThe final line of defense is wallet-level AI auditing. User wallets would integrate the same AI audit tools to audit the target contract's code, read state variables, understand the protocol's topology and context, and audit both the contract and the user's transaction input *before* the user signs the transaction. A recommendation would then be shown in the confirmation pop-up, allowing each user to run their own professional-grade audit agent for protection.

QHow does the article suggest tools like 'Nemesis' handle false positives in smart contract auditing?

ANemesis handles false positives by having multiple AI agents check for issues using different methods. Then, a separate, independent AI agent acts as a judge to filter out the false positives based on its contextual understanding of the protocol's logic and goals. This allows it to understand nuances, such as when reentrancy might be acceptable versus when it is truly dangerous, a task that even experienced human auditors often get wrong.

Пов'язані матеріали

Microsoft is Afraid of Being Marginalized by AI Giants

Microsoft, once the defining force of the PC era, now faces a familiar challenge in the AI age: the risk of being relegated to a profitable but invisible infrastructure provider. This anxiety was laid bare at Build 2026, where CEO Satya Nadella unveiled a major strategic pivot. The catalyst was a quiet April agreement that dissolved Microsoft's exclusive licensing and cloud-hosting deal with OpenAI, its once-vital partner. This erased Microsoft's key AI moat. With OpenAI and Anthropic defining AI applications and gaining enterprise traction—even within Microsoft's own ranks—Nadella had to answer: without exclusivity, what is Microsoft's role? The answer was a suite of seven in-house AI models, a developer-focused AI workstation (Surface RTX Spark Dev Box), and, most crucially, the Agent 365 platform for enterprise AI governance. The models, notably targeting Anthropic's strengths in coding and enterprise, signal a defensive move. However, the broader strategy is to make the models themselves less decisive. Financially, Microsoft's AI revenue is strong, driven largely by Azure running others' models. Yet its user-facing products like Copilot show weak penetration and engagement. Microsoft earns infrastructure money but lacks direct user mindshare. Nadella's core fear is being "hollowed out." As OpenAI and Anthropic prepare for IPOs and gain financial independence, they may build their own infrastructure, threatening Azure's lucrative AI revenue stream. Microsoft's window is to entrench itself deeper: not as the model creator, but as the indispensable platform for securely deploying, managing, and governing all AI models within the enterprise through Agent 365. Build 2026 revealed Microsoft's bet: in the AI era, the ultimate power lies not in any single model, but in the enterprise "operating system" that controls them. Nadella is determined to ensure Microsoft is the driver of this new era, not just a passenger.

marsbit12 хв тому

Microsoft is Afraid of Being Marginalized by AI Giants

marsbit12 хв тому

CPU, Quietly Returning to the Center of the AI Computing Power Stage

Over the past three years, AI computing power narratives have been dominated by GPUs. However, starting in 2026, this story began to shift. While training large models remains GPU-intensive, the rapid growth of inference and AI agent workloads, which require high levels of task orchestration, concurrency, and data flow management, has highlighted a renewed critical role for CPUs. These are tasks GPUs are not designed to handle. Intel's recent launch of the Xeon 6+ processor, built on its Intel 18A process and featuring up to 288 efficiency cores (E-cores), exemplifies this strategic pivot. It is positioned not as a mere companion to GPUs but as the essential "control plane" for AI infrastructure, optimized for high-density, energy-efficient, and high-throughput workloads characteristic of AI agents and inference. This "CPU resurgence" is not about CPUs outperforming GPUs in raw computation. It reflects a systemic bottleneck: as AI scales from training single models to deploying countless intelligent agents, the demand for coordination and data handling surges. Major cloud providers are also developing their own high-density ARM-based server CPUs for similar workloads. However, Intel's success with this strategy faces significant challenges. Competition includes NVIDIA's integrated CPU-GPU solutions, the expanding adoption of cloud vendors' in-house ARM CPUs, and the crucial market test of Intel's 18A manufacturing process against rivals like TSMC's N2. In conclusion, CPUs are indeed reclaiming a central, though redefined, role in AI compute—managing the complex orchestration that enables massive-scale AI deployment. While the trend is clear, which company will ultimately lead this CPU resurgence remains an open question to be decided in the data centers of 2027 and beyond.

marsbit33 хв тому

CPU, Quietly Returning to the Center of the AI Computing Power Stage

marsbit33 хв тому

Торгівля

Спот
Ф'ючерси
活动图片