Coinkite Faces Criticism for Storing Customer Emails After $88M Coldcard Hack

cryptonews.ru2026-08-02 tarihinde yayınlandı2026-08-02 tarihinde güncellendi

Özet

Coinkite faces renewed criticism after a vulnerability in generating random seed phrases for its Coldcard hardware wallets allowed attackers to steal over 1,000 BTC in two days. While sending warning emails to customers dating back to 2019, the company was questioned for retaining these email addresses. Coinkite cited its public policy stating emails are kept so customers can verify their other data was deleted, but did not specify a deletion timeline, only saying "for now." Co-founder Rodolfo Novak emphasized the company's commitment to privacy, offering anonymous purchases and deleting customer data after 90 days, and appealed for help contacting all potentially affected users. According to Galaxy Research, losses from the exploit had reached 1,367 BTC, worth over $88 million.

Coinkite is once again facing the wrath of its customers after a flaw in random seed phrase generation was discovered, allowing malicious actors to steal over 1,000 $BTC in the past two days.

In an effort to reach the majority of customers who may have been affected by the issue impacting a series of Coldcard hardware wallets, the company sent out warning emails about the severity of the incident to addresses associated with purchases made since 2019.

On social media, Coldcard confirmed it had contacted its customers via email and assured the authenticity of these messages.

"It wasn't easy, but we have now sent emails to all addresses we could find through our store and mailing list systems. The emails are being sent out in batches since Friday. If you received such an email, we want to confirm that it was indeed sent by Coinkite," the company explained.

When Coinkite was criticized for storing email data, it referenced its public policy, which states that email addresses provided during purchase are retained so that customers can log in and verify that their other information has been deleted. However, the company did not specify a deletion policy for this data, noting that these addresses would be stored "for now."

Despite this, Rodolfo Novak, co-founder and CEO of Coinkite, emphasized that the company does not store customer information and has no way to contact affected customers, calling for help in reaching all potentially impacted users.

In an earlier post, Novak stated that the company offers the possibility of making anonymous purchases and deletes customer data after 90 days.

"Every other month, one of our competitors has a data leak. Coinkite/COLDCARD takes this extremely seriously — like our customers, we are bitcoiners first," he emphasized at the time.

According to Galaxy Research, by 5:36 PM Eastern Time (EDT) on Saturday, the amount of damage from the incident had already reached 1,367 $BTC, amounting to over $88 million.

İlgili Sorular

QWhat vulnerability was recently discovered in Coldcard hardware wallets manufactured by Coinkite?

AA vulnerability in the generation of random seed phrases in Coldcard hardware wallets allowed attackers to steal funds.

QHow did Coinkite try to warn its customers about the Coldcard vulnerability?

ACoinkite sent warning emails to addresses associated with purchases dating back to 2019 to reach potentially affected customers.

QHow much was stolen according to Galaxy Research's data reported on Saturday evening EDT?

AAccording to Galaxy Research data as of Saturday evening EDT, losses had reached 1,367 BTC, worth over $88 million.

QWhy did Coinkite face criticism regarding customer data in the wake of this incident?

ACoinkite faced criticism for storing customer email addresses. The company cited its public policy stating emails are kept so customers can verify their other data was deleted, but it did not specify a deletion timeline.

QWhat contradictory statements did Coinkite's co-founder make about customer data handling?

ACo-founder Rodolfo Novak stated the company does not store customer information and lacks a way to contact affected users, yet he had previously claimed the company deletes customer data after 90 days and offers anonymous purchases.

İlgili Okumalar

From South Korea to the United States: Blue-Collar Jobs Are Becoming Increasingly Popular, Thanks to AI

AI is reshaping the labor market's value proposition. The traditional four-year college degree is losing its appeal as a guaranteed career path, while skilled blue-collar trades like electricians, welders, and plumbers are experiencing historic demand and wage premiums. This shift is driven by dual pressures: AI's displacement of certain white-collar roles and a booming need for physical infrastructure and data center construction. Data confirms the trend. In the U.S., vocational school revenue surged, and a significant portion of recent layoffs are AI-related. Surveys show a majority of Gen Z adults plan to pursue blue-collar work, citing better job security against AI automation. Vocational education interest has exploded recently. Experts cite a psychological shift as younger generations seek tangible, AI-resistant careers and avoid high student debt. In many cases, salaries for skilled trades now match or exceed those requiring a bachelor's degree. In South Korea, semiconductor vocational high schools boast near-total employment, with graduates securing high-paying roles at companies like Samsung. The shortage is structural, exacerbated by a retiring baby boomer workforce and massive infrastructure projects. Companies like JPMorgan Chase, Meta, and Lowe's are investing heavily in training programs. However, overcoming historical stigma and a "perception gap" around trade careers remains a key challenge to closing the talent gap.

marsbit17 dk önce

From South Korea to the United States: Blue-Collar Jobs Are Becoming Increasingly Popular, Thanks to AI

marsbit17 dk önce

Qualcomm: AI Hype Subsides, When Will Smartphones Emerge from the Gloom?

Qualcomm reported its Q3 FY2026 results (ending June 2026), with revenue of $9.95B, down 4% YoY but above expectations. Gross margin declined to 53.1%, pressured by rising costs across manufacturing and memory. Key business segments showed mixed performance: Handset revenue fell 19.6% YoY to $5.09B, dragged by an 11% decline in non-Apple Android shipments and weaker high-end mix. Conversely, Automotive revenue surged 61% to $1.59B, and IoT grew 9% to $1.83B. Core operating profit dropped 41% YoY due to margin compression and higher expenses. Management's Q4 FY2026 guidance projects revenue of $9.7B-$10.5B, in line with consensus, but Non-GAAP EPS guidance of $2.05-$2.25 fell short of expectations. Amidst persistent weakness in its core handset market, Qualcomm is pursuing growth in AI, focusing on Edge AI (smartphones, PCs, automotive) and Data Center AI. Its data center strategy includes four pillars: AI accelerators (e.g., AI200), commercial CPUs (Dragonfly C1000), custom silicon, and connectivity solutions. While these initiatives initially boosted its stock, concerns over AI capital expenditure sustainability have since erased those gains. The company targets $5B in data center revenue for FY2027 and $15B for FY2029. The report concludes that with the traditional handset business still under pressure, the data center opportunity is currently viewed as a longer-term option, and a more conservative valuation based on core operations may be warranted until AI contributions materialize.

marsbit21 dk önce

Qualcomm: AI Hype Subsides, When Will Smartphones Emerge from the Gloom?

marsbit21 dk önce

From TPU to Self-Evolving Agents: How Jeff Dean Predicts the Next Step in AI

At the 2026 YC Startup School, Jeff Dean outlined his vision for AI's next phase, shifting focus from simply scaling models to building intelligent, autonomous systems. He believes AI's progress is no longer just about creating smarter models, but about integrating them into systems capable of long-term, iterative work, automated experimentation, and continuous learning. This evolution moves the competition from "who has the bigger model" to "who can best organize intelligence." Dean suggests AI capabilities are now comparable to a junior engineer, enabling the automation of complex workflows. However, the true challenge and opportunity lie in managing these AI "workers" at scale. He emphasizes the importance of **context engineering**—structuring tools, memory, and feedback loops—over raw model power. For startups, this means building deep expertise in niche domains where general models currently fail (near 0-1% success rates), leveraging proprietary data, specialized tools, and domain-specific evaluators. A recurring theme is re-examining fundamental constraints. Dean's past work, like moving Google's search index to memory or creating the TPU, stemmed from questioning outdated assumptions about hardware and cost. He sees similar inflection points today, particularly in **specialized inference hardware** to drastically reduce latency and energy consumption for real-time Agent operation. Notably, he points out that in modern AI systems, the dominant cost is often not computation but **data movement**. Reliable, long-running Agents require robust system design, borrowing concepts from distributed computing like checkpointing, state management, and parallel exploration to handle failures and maintain progress over days or weeks. As AI automates execution, the scarcest human skills will shift to **defining clear specifications**, **judging what problems are worth solving** (taste), and designing effective feedback loops. Ultimately, Dean's framework prioritizes understanding the problem deeply, identifying the true bottlenecks, and systematically building closed-loop systems where AI can not only perform tasks but also improve AI itself.

marsbit21 dk önce

From TPU to Self-Evolving Agents: How Jeff Dean Predicts the Next Step in AI

marsbit21 dk önce

İşlemler

Spot
活动图片