SagaEVM Chain Exploit Sees $7M Drained, Funds Moved to Ethereum

TheNewsCrypto2026-01-22 tarihinde yayınlandı2026-01-22 tarihinde güncellendi

Özet

On January 21, the SagaEVM chain, part of the Saga Layer-1 ecosystem, was paused following a security exploit. The team identified the attacker’s wallet and confirmed approximately $7 million in assets were stolen, with some funds bridged to Ethereum and converted to ETH or other tokens. The chain was halted at block height 6,593,800 to prevent further unauthorized transfers. The attack involved contract deployments, cross-chain interactions, and rapid liquidity withdrawals. Saga is working with exchanges to blacklist the address and is conducting a forensic investigation. The exploit affected SagaEVM and related EVM environments, but the mainnet and validator security remained uncompromised.

The SagaEVM chain, part of the Saga Layer-1 blockchain ecosystem, remained paused after a security exploit on January 21. With that, the investigation update was released on January 22, the attacker’s wallet was found, and around $7 million worth of assets, with some converted to Ethereum. Further, the team is working to blacklist that hacker’s address.

Saga Identifies Attacker Wallet as Funds Bridged to Ethereum

After the exploit was identified, on the first day itself, the team paused the chain at block height 6,593,800 to stop unauthorized transfers. Also, appears to have involved a sequence of contract deployments, cross-chain interactions, and rapid liquidity withdrawals that allowed the attacker to extract assets.

The stolen assets, including USDC, were transferred to the Ethereum mainnet and, in some cases, converted to ETH or other tokens. Also, the Saga has identified the wallet linked to the exploit and is working with exchanges and bridge operators to blacklist it and support asset recovery.

With that, currently, the Saga team is conducting a detailed forensic investigation and plans to publish a comprehensive technical post-mortem report.

The exploit affected the SagaEVM network chain itself, as well as environments like Colt and Mustang that rely on EVM functionality, whereas the Saga SSC mainnet, consensus layer, and Validator security were unaffected, and there was no evidence of private key compromise.

Chainalysis Theft Estimation in 2025

The cryptocurrency industry lost more than $3.4 billion in thefts between January and early December 2025, highlighting ongoing security issues.

The report says that the attacks on investors’ personal wallets increased significantly in 2025, with the stolen value rising from 7.3% to 44%. Where the direct crypto wallet drain occurrences were around 158,000, with over 80,000 distinct victims.

Highlighted Crypto News Today:

Thailand Drafts Crypto ETF Rules as Institutional Demand Rises

TagsETHEREUMSagaEVM cHAIN

Trend Kriptolar

İlgili Sorular

QWhat was the total value of assets drained in the SagaEVM chain exploit?

AApproximately $7 million worth of assets were drained.

QTo which blockchain were the stolen funds primarily moved?

AThe stolen funds were primarily moved to the Ethereum mainnet.

QWhat immediate action did the Saga team take after identifying the exploit?

AThe team paused the chain at block height 6,593,800 to stop unauthorized transfers.

QWhich parts of the Saga ecosystem were unaffected by this security incident?

AThe Saga SSC mainnet, consensus layer, and Validator security were unaffected.

QAccording to the article, what was the estimated total value of cryptocurrency thefts in 2025?

AThe cryptocurrency industry lost more than $3.4 billion in thefts between January and early December 2025.

İlgili Okumalar

Claude's Major New Feature: Screen Recording + Voice, Distilling Your Skills into AI Tasks in One Click

Claude has introduced a major new feature called "Record a Skill," available for Pro, Max, and Team users. This function, found in the Claude desktop app's CoWork menu, allows users to create reusable AI skills simply by recording their screen and providing voice narration while performing a task. Claude then automatically analyzes the recording and generates a functional Skill. A hands-on test confirmed the feature works seamlessly. Users start recording via the Skills manager, perform their workflow while verbally explaining the steps and logic, and avoid including sensitive information. After recording, Claude processes the content and creates the Skill, which can be saved and later invoked with a slash command (/). This eliminates the need for manual adjustments or writing complex instruction files. The innovation goes beyond mere efficiency. Previously, creating a Skill required writing a detailed SKILL.md file in Markdown—a significant barrier for non-technical users. "Record a Skill" bypasses this by directly capturing both actions and the implicit reasoning shared in the narration. This lowers the barrier to knowledge transfer and automation, addressing a core challenge in corporate knowledge management: the difficulty of getting experts to write and maintain documentation. However, the feature also highlights a shift in the nature of work. A case study from March 2026 showed a freelancer whose five-year client relationship was effectively replaced by a hand-coded Claude Skill automating their content workflow. With the even lower barrier of screen recording, the ability to distill personal expertise into automatable skills accelerates this trend. The "moat" for work is moving from simply knowing how to do a task to mastering tasks that are difficult or impossible to automate.

marsbit5 dk önce

Claude's Major New Feature: Screen Recording + Voice, Distilling Your Skills into AI Tasks in One Click

marsbit5 dk önce

Feeding AI "Noise" Can Also Boost Scores, This Work Enables Positive Transfer with Noise

Feeding "Noise" to AI Can Improve Performance: A Method Enables Positive Transfer from Noise This work, Semi-Supervised Noise Adaptation (SSNA), introduces a Noise Adaptation Framework (NAF) that challenges traditional transfer learning. Instead of requiring a labeled source domain of real data (e.g., images, text), NAF uses randomly generated Gaussian noise as the source. For a target task with C classes, it constructs C noise clusters by sampling from Gaussian distributions. Although this synthetic noise contains no semantic meaning, NAF trains it to form a discriminative class structure in a shared representation space—clustering same-class noise and separating different classes. The key is aligning this learned structure from the noise domain to the real, sparsely labeled target domain. A small number of target labels are still essential to establish the correspondence between noise clusters and actual classes. The training objective combines: 1) supervised loss on the few labeled target samples, 2) classification loss for the noise to build its structure, and 3) a distribution alignment loss (using Negative Domain Similarity) to minimize the gap between the noise and target domains in the shared space. Experiments show significant gains in few-label settings. With just 4 labels per class, NAF with a ResNet-18 backbone improves accuracy over a standard supervised baseline (ERM) by +12.35% on CIFAR-10, +7.61% on CIFAR-100, +4.38% on DTD-47, and +2.74% on Caltech-101. It also benefits fine-grained datasets and scales to ImageNet-1K (with 100 labels/class) and text classification (AG News). NAF can be integrated into existing semi-supervised methods like FixMatch for further gains. Ablation studies confirm the transferred benefit comes from the discriminative structure of the noise, not randomness itself. Collapsing all noise into a single point causes negative transfer, while increasing separation between noise cluster centers improves performance. The amount of noise per class is less critical once a basic structure forms. In conclusion, this work demonstrates that for positive transfer, the semantic content of source data may not be necessary. What can be effectively transferred is the *organizational structure* of categories within a representation space. This offers a promising alternative for scenarios where real source data is unavailable due to privacy, copyright, or procurement constraints.

marsbit6 dk önce

Feeding AI "Noise" Can Also Boost Scores, This Work Enables Positive Transfer with Noise

marsbit6 dk önce

İşlemler

Spot

Popüler Makaleler

SAGA Nasıl Satın Alınır

HTX.com’a hoş geldiniz! Saga (SAGA) satın alma işlemlerini basit ve kullanışlı bir hâle getirdik. Adım adım açıkladığımız rehberimizi takip ederek kripto yolculuğunuza başlayın. 1. Adım: HTX Hesabınızı OluşturunHTX'te ücretsiz bir hesap açmak için e-posta adresinizi veya telefon numaranızı kullanın. Sorunsuzca kaydolun ve tüm özelliklerin kilidini açın. Hesabımı Aç2. Adım: Kripto Satın Al Bölümüne Gidin ve Ödeme Yönteminizi SeçinKredi/Banka Kartı: Visa veya Mastercard'ınızı kullanarak anında Saga (SAGA) satın alın.Bakiye: Sorunsuz bir şekilde işlem yapmak için HTX hesap bakiyenizdeki fonları kullanın.Üçüncü Taraflar: Kullanımı kolaylaştırmak için Google Pay ve Apple Pay gibi popüler ödeme yöntemlerini ekledik.P2P: HTX'teki diğer kullanıcılarla doğrudan işlem yapın.Borsa Dışı (OTC): Yatırımcılar için kişiye özel hizmetler ve rekabetçi döviz kurları sunuyoruz.3. Adım: Saga (SAGA) Varlıklarınızı SaklayınSaga (SAGA) satın aldıktan sonra HTX hesabınızda saklayın. Alternatif olarak, blok zinciri transferi yoluyla başka bir yere gönderebilir veya diğer kripto para birimlerini takas etmek için kullanabilirsiniz.4. Adım: Saga (SAGA) Varlıklarınızla İşlem YapınHTX'in spot piyasasında Saga (SAGA) ile kolayca işlemler yapın.Hesabınıza erişin, işlem çiftinizi seçin, işlemlerinizi gerçekleştirin ve gerçek zamanlı olarak izleyin. Hem yeni başlayanlar hem de deneyimli yatırımcılar için kullanıcı dostu bir deneyim sunuyoruz.

157 Toplam GörüntülenmeYayınlanma 2024.12.13Güncellenme 2026.06.02

SAGA Nasıl Satın Alınır

Tartışmalar

HTX Topluluğuna hoş geldiniz. Burada, en son platform gelişmeleri hakkında bilgi sahibi olabilir ve profesyonel piyasa görüşlerine erişebilirsiniz. Kullanıcıların SAGA (SAGA) fiyatı hakkındaki görüşleri aşağıda sunulmaktadır.

活动图片