Terraform Labs Co-Founder Do Kwon Sentenced To 15 Years In Prison

bitcoinistОпубліковано о 2025-12-12Востаннє оновлено о 2025-12-12

Анотація

Terraform Labs co-founder Do Kwon has been sentenced to 15 years in prison for his role in a major cryptocurrency collapse. A US district judge described Kwon’s actions as “unusually serious” fraud, noting he lied to the market for years about TerraUSD, a so-called stablecoin that ultimately failed. The collapse of TerraUSD and another digital currency erased an estimated $40 billion in market value in 2022, devastating hundreds of thousands of investors. The judge emphasized the need for deterrence, rejecting a lighter sentence. Kwon, who has already served over 17 months in custody, may be transferred to Fort Dix or serve part of his sentence in South Korea, where he faces additional charges including securities fraud and money laundering.

The legal saga surrounding Do Kwon, co-founder of Terraform Labs, has culminated in a significant ruling, with the crypto magnate sentenced to 15 years in prison this Thursday.

This decision follows a tumultuous period marked by the collapse of two digital currencies created by the firm, which collectively erased an estimated $40 billion from the market in 2022, leading to widespread repercussions within the broader cryptocurrency industry.

Do Kwon’s 15-Year Sentence

During the sentencing hearing, US District Judge Paul A. Engelmayer underscored the seriousness of Do Kwon’s actions, stating, “Your fraud was unusually serious. For four years you publicly lied to the market.”

The judge emphasized that Kwon misrepresented TerraUSD as a stablecoin backed by a system designed to sustain its peg to the dollar, asserting that Kwon’s claims were ultimately fraudulent when the peg faltered.

Judge Engelmayer remarked that Do Kwon’s actions had devastating effects, contributing to the collapse of investments for “hundreds of thousands of investors.” He noted that a lighter sentence would be unacceptable, stating:

“Five years would be so implausible it would require appellate reversal. Others must be deterred. People are watching this [live]. There will be future entrepreneurs. This case will serve as a reminder of breaking bad and what happens.”

With that, the US District Judge imposed a 15-year sentence, factoring in time already served—17 months and eight days while in pre-extradition custody.

Judge Hints At Fort Dix Transfer

Interestingly, there were suggestions from both the judge and prosecutors that Kwon could be transferred to Fort Dix, a facility where some high-profile inmates are held. There’s also the possibility that part of his sentence could be served in South Korea, where he is facing additional legal challenges.

In January, Do Kwon was charged with nine criminal counts that included securities fraud, wire fraud, commodities fraud, and conspiracy to commit money laundering.

The daily chart shows the total crypto market cap at $3.1 trillion. Source: TOTAL on TradingView.com

Featured image from ABC, chart from TradingView.com

Трендові криптовалюти

CitreaCTR

wrapped stUSDTWSTUSDT

Velodrome FinanceVELODROME

BrevisBREV

ZRX（0X）ZRX

PancakeSwapCAKE

Пов'язані матеріали

NVIDIA's New Open-Source MoE: One Line of Import, Fine-Tuning Accelerated by 3.7x

NVIDIA has open-sourced NeMo AutoModel, a tool designed to significantly accelerate the fine-tuning of Mixture-of-Experts (MoE) large language models. By adding just one import line to existing code based on Hugging Face Transformers v5, users can achieve a 3.4x to 3.7x increase in training throughput and reduce GPU memory usage by 29% to 32% without altering their API. The key innovations include Expert Parallelism (EP) to distribute expert weights across GPUs, lowering memory pressure; DeepEP to fuse computation and communication; and TransformerEngine kernels for accelerated core operations. Benchmarks on models like Qwen3-30B-A3B show training throughput per GPU jumping from 3075 to 11340 tokens per second. The solution also enables the fine-tuning of very large models, such as the 550B parameter Nemotron 3 Ultra, which would exceed memory limits with the standard Transformers v5. Code and benchmarks are available on GitHub.

marsbit10 хв тому

NVIDIA's New Open-Source MoE: One Line of Import, Fine-Tuning Accelerated by 3.7x

marsbit10 хв тому

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.

marsbit12 хв тому

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

marsbit12 хв тому

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.

marsbit18 хв тому

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

marsbit18 хв тому

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

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-powered defense, integrating continuous monitoring, real-time on-chain risk detection, and embedding security directly into the development phase, as seen with tools like OpenZeppelin's Skills system. Ultimately, the era of "audit once, secure forever" is over. Security must become a continuous, embedded infrastructure investment for projects. For audit companies, survival depends on proactively transforming from traditional service providers into platforms offering AI-native, ongoing security solutions.

链捕手26 хв тому

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

链捕手26 хв тому

$3M Exploit Hits Polymarket: Users to Receive Full Refunds After Third-Party Breach

Polymarket will fully reimburse users affected by a front-end exploit that stole approximately $3 million in crypto assets. The company stated the incident was not a flaw in its core protocol but a supply chain attack. A compromised third-party vendor injected a malicious script into the platform's front-end for a limited number of users, allowing the attacker to drain funds from about 15 wallets while they interacted with the site. The stolen assets, primarily Polymarket's pUSD stablecoin, were bridged to Ethereum and converted to roughly 1,893 ETH. This marks the second security incident for Polymarket in under two months, following a separate $700,000 loss. The event highlights growing risks associated with third-party dependencies in the crypto industry.

TheNewsCrypto27 хв тому

$3M Exploit Hits Polymarket: Users to Receive Full Refunds After Third-Party Breach