How to Stay Safe in a DeFi Era

BitcoinistPublicado em 2022-02-16Última atualização em 2022-02-16

Resumo

Decentralized finance (DeFi) came into existence a few years ago but only recently rose to fame.

Decentralized finance (DeFi) came into existence a few years ago but only recently rose to fame. Like cryptocurrencies, this emerging financial technology runs on distributed ledgers and replaces intermediaries with smart contracts, making the entire asset management process automatic. With a $127.06B total market cap and $78.92B worth of crypto assets locked in P2P protocols, DeFi rightly holds the trophy of the fastest growing sector in the crypto space.

However, decentralized finance is not immune to common problems. The sector suffers from the same old disease that has been plaguing the crypto industry for a decade – scams.

The amount of money lost in DeFi is overwhelming. According to London-based firm Elliptic, users suffered losses totalling $12B, out of which scams were responsible for $10.5B. The figures are horrifying yet don’t condemn the sector. DeFi has a lot of potential – projects like Aave do give hope for the future. To avoid scams and find truly robust DeFi solutions, it’s vital to do your own research.

Product Reviews

Reviews help us learn about any underwater stones that the service or product have from former or current users. The feedback has a great value – it saves time and energy and, in some cases, money.

However, things don’t look simple when it comes to the crypto industry. After all, countries don’t have proper regulation mechanisms, and information can’t be easily verified. Yet, emerging review sources like TrustPilot started to pop up recently, e.g. RugDoc. The site has a big community and encompasses reviews on 2,600 crypto projects – yield farms, exchanges, OHM forks, play-to-earn games, and NFTs – spanning across 24 networks, including Solana, Binance, Polygon, Avalanche, and Cardano. RugDoc also monitors new projects with Launch Calendar.

At the moment, the crypto review niche is in its early development, but a few more projects are expected to emerge soon.

“Rugs” Under Rugs

Most scams are plain simple rug pulls. In the first phase, fraudsters add liquidity (usually in the chain native currency) to their tokens and attract investors. Then, they remove liquidity and run off with funds, pushing the token’s value to near zero and leaving holders with nothing.

There are other ways to fool the crowd, and Squid Game Token attributed to the show of the same name is a good example. Fraudsters simply disabled the ability to withdraw tokens for investors, leaving them with an unsellable asset. Thus, it’s vital to assess the projects from a technical standpoint.

Some companies have already implemented a strict audit and a special rating system. For example, RugDoc ranks projects from least risk to highest risk through a technical review of the project’s smart contracts- a controlling program deployed to the blockchain. The company also has a KYC Program that uses the process known as “doxxing” to verify the team members’ identities to hold them accountable in case of malicious events.

DeFi Monitor Tools

There are many DeFi tools that monitor crypto projects. Their sole purpose is to identify blind spots not visible to the general public and allow users to invest safely and efficiently in decentralized protocols. Some offer customized audits (e.g., DeFi Audits). In contrast, others have open-source tools (e.g., RugDoc), such as honeypot checker to verify if smart contract vetoes token sales, emergency withdrawal to let users remove funds from the project, and LP Breaker to separate LP tokens.

DeFi Knowledge

DeFi is a rapidly evolving industry with new projects popping up every single day. However, it’s essential to dive deep into details. That way, users can avoid scams and invest with certainty. That’s why many crypto projects have launched their own wiki hubs. In these hubs, users can find all the necessary info about decentralized finance and learn more about the product functionality. Projects like PolkaDot or Ethereum all have wiki hubs.

Furthermore, it gives users confidence that the project is up and running. However, not all have a portal of that kind, which is a reason to become suspicious. Wiki hub, just like White Paper, is essential for community development and trust.

In some cases, the project simply doesn’t have enough resources to create an entire separate wiki hub. In this case, users can turn to publicly available wiki libraries, like RugDoc Wiki, which covers tutorials and information on all Ethereum virtual machine compatible chains. Public knowledge sources are created for the community by the community. These places unite both crypto pros and investors where they share up-to-date information.

Wrapping Up

DeFi is one of the fastest-growing sectors in the finance industry. It offers great potential in transforming the financial industry and equally as many threats due to its decentralized nature. Thus, it’s vital investors adopt a security routine to analyze risk and access sources with additional information on the product or educational articles. That way, any user will be empowered to stay safe in the era of decentralized finance.

Leituras Relacionadas

The Revelation from the Raydium Theft Incident: New DeFi Vulnerabilities Lurking in Forgotten Old Contracts

**Raydium Exploit Reveals DeFi's Hidden Risk: Forgotten "Zombie" Contracts** A recent attack on Raydium's deprecated V3 AMM pools resulted in a loss of approximately $1.34 million. The hacker exploited pools that were no longer supported by Raydium's current UI or SDK but remained fully functional and accessible on-chain. This incident highlights a critical, often overlooked category of risk in DeFi: inactive or legacy smart contracts that projects fail to properly decommission. Since March 2025, there have been at least 8 publicly reported attacks targeting such abandoned contracts, with total losses around $10.8 million. Including older pools and deprecated features, the count rises to 10 incidents with roughly $22.5 million in losses. These "zombie contracts" represent a lifecycle management failure rather than a code vulnerability, yet they are typically misclassified under general "code bug" categories in security reports, masking the true scale of the problem. The root cause is that projects often merely document a contract as "deprecated" without taking essential technical steps to secure it: withdrawing remaining assets, disabling external call functions, and implementing ongoing monitoring. These forgotten, under-monitored components become prime targets for attackers. To address this, the industry needs to recognize "zombie contracts" as a distinct risk category and establish standardized decommissioning protocols. Essential steps should include: 1) a formal retirement announcement, 2) removal of all front-end integrations, 3) withdrawal of locked assets, 4) disabling key contract functions, 5) ongoing security monitoring, 6) clear user communication, and 7) a post-mortem analysis. The value of a DeFi project lies not only in its current TVL but also in the security of its historical codebase, which has now become a new attack surface.

Foresight NewsHá 1h

The Revelation from the Raydium Theft Incident: New DeFi Vulnerabilities Lurking in Forgotten Old Contracts

Foresight NewsHá 1h

Robots Begin to 'Consume Data': The Hidden Production Chain from Indian Data Factories to Billion-Dollar Humanoid Robots

Robots have started to 'consume data,' driving the formation of a new industrial supply chain focused on producing training data for embodied AI. Unlike large language models, which are trained on vast internet text corpora, embodied AI models face a 'data desert' in the physical world. This has created a massive demand for first-person perspective video data (Ego Data), captured by workers wearing cameras in places like Indian garment factories. Companies like Neocambrian AI are establishing 'data factories' where workers perform standardized tasks (e.g., sorting clothes, kitchen organization) to generate thousands of hours of video. Research, such as NVIDIA's EgoScale, demonstrates that scaling this human demonstration data predictably improves robot performance, particularly for dexterous manipulation. This has validated a training path combining large-scale human data for pre-training with smaller amounts of robot-specific data for fine-tuning. The value of different data types varies significantly, forming a 'data pyramid.' The base consists of low-cost, large-scale internet and Ego Data. Higher layers include more expensive motion-capture data (e.g., from data gloves), simulation/synthetic data, and the most costly and scarce layer: real robot teleoperation data. This demand has spawned a layered ecosystem of data suppliers: low-cost data factories, motion capture and alignment specialists, robot-native teleoperation service providers, simulation data companies, and platforms aiming for data standardization. Robot companies themselves are adopting a 'layered procurement' strategy: outsourcing generic Ego Data while building in-house capabilities for robot-specific adaptation data and the critical deployment/failure data generated in real-world applications. The industry is shifting focus from hardware and basic mobility to the data pipelines required for general-purpose capability. While parallels exist to data labeling companies like Scale AI in the LLM boom, the physical complexity of robot data—involving action success ambiguity and sim-to-real gaps—requires more integrated solutions for data collection, annotation, and a continuous feedback loop. The race is on to build the data engines that will teach robots to operate reliably in the unstructured real world.

marsbitHá 4h

Robots Begin to 'Consume Data': The Hidden Production Chain from Indian Data Factories to Billion-Dollar Humanoid Robots

marsbitHá 4h

Spicy Commentary | Michael Saylor's 'Player Talk'; 60-Year-Old Aunt Liquidated After 'Scamming a Young Man'

**"Spicy Commentary": Three Tales of Crypto's Wild Week** This week's "Spicy Commentary" column highlights three dramatic stories from the cryptocurrency world. First, **MicroStrategy's Michael Saylor** addressed the controversy over his company potentially selling Bitcoin. At the BTC Prague event, he clarified, "I never said the company can't sell Bitcoin. I told *you* never to sell *your* Bitcoin." This "do as I say, not as I do" stance was criticized by netizens as peak linguistic gymnastics, noting a history of him previously stating the company would "never" sell. Second, a **bizarre fraud case** emerged from Beijing. A 60-year-old woman, obsessed with getting rich from crypto but unwilling to risk her own savings, posed online as the 20-something "god-daughter" of a high-ranking official. She catfished a young man, convincing him to give her over 200,000 yuan for fabricated emergencies. She then invested all the stolen money into cryptocurrency with 10x leverage, only to lose everything in a market crash. The woman was sentenced to four years in prison for fraud. Finally, a **sobering trader's tale** surfaced on Reddit. A user posted "Tale of a crypto trader," confessing their net worth had plummeted from a peak of $45 million to roughly $17,200, primarily due to holding meme coins too long. The post, described as a crypto "book of confessions," sparked reactions ranging from sympathy to critique about greed, poor risk management, and the perils of treating meme coins as long-term investments instead of taking profits. The column concludes that this week featured masterful rhetoric, elaborate scams, and extreme financial volatility, stitching together another chapter in crypto's unpredictable theater.

Foresight NewsHá 4h

Spicy Commentary | Michael Saylor's 'Player Talk'; 60-Year-Old Aunt Liquidated After 'Scamming a Young Man'

Foresight NewsHá 4h

Tremble Humans, AI Continues Its Accelerated Sprint

Trembling, Humans: AI Continues Its Accelerated Sprint Yes, AI is still rapidly accelerating. While deep learning seemed to stall quickly in its early years, large models after years of development show no sign of hitting their ceiling. At the Zhiyuan Conference 2026, the focus is on enabling AI to move from the digital world into the physical world. Scaling Law remains effective, continuing to drive advancements in both large language models and multimodal models. The industry is now entering a phase of pursuing World Models, though unresolved technical paths and data issues mean this exploration may take 3-5 more years. Concurrently, breakthroughs in Agents are accelerating AI's real-world application in fields like healthcare and meetings. Making Agents truly useful requires key hardware-software co-design, evident from the strong presence of chip vendors at the conference. We stand at a new historical threshold where AI is becoming a foundational force reshaping the world. The first day of the conference highlighted AI's evolution from "knowing how to chat" to "knowing how to work." Scaling Law persists, World Models are the next key battleground, and Agents are transitioning from usable to好用 (user-friendly). Scaling Law is not ending but diversifying. New models like Anthropic's Fable 5 demonstrate scaling through parameter size, synthetic data, and reinforcement learning. Advancements in AI Coding and Agent deployment are enabling a trend of AI self-evolution, potentially allowing AI to take over digital world iterations. World Models represent the next frontier for large models extending into the physical realm, but no current model is truly impressive at solving real-world problems. Technical consensus is lacking, with debates on data sources (video, simulation, real-world). Different approaches are emerging: language-centric, pixel-centric, 3D-structure-centric, and visual-representation-centric models. Zhiyuan Institute is exploring a fifth path: unified latent space modeling fusing language and visual representations, and introduced its own under-development World Model, Physis-v0.1. On the product side, Agents are key to bringing AI into daily life. Since 2025, the "Year of the Agent," products have become more proactive and capable of complex tasks. Zhiyuan showcased four vertical Agents for cardiac diagnosis, autonomous research, meeting summarization, and protein risk discovery. However, technical challenges remain, particularly in context engineering like memory and orchestration. "Harness" – the engineering framework around an Agent – is crucial for maximizing its capabilities by clarifying intent, designing workflows, and incorporating validation and feedback. In summary, AI's breakneck pace continues on multiple fronts: foundational model scaling, the ambitious pursuit of World Models for physical understanding, and the ongoing refinement of practical Agents. The journey from capable to truly reliable and useful AI systems is well underway.

marsbitHá 5h

Tremble Humans, AI Continues Its Accelerated Sprint

marsbitHá 5h

Trading

Spot
Futuros
活动图片