Coinbase Exploit Hacker Swaps $5M DAI to USDC, Bridges Funds After 35-Minute Idle Window

ccn.comPublished on 2025-10-02Last updated on 2025-10-02

Key Takeaways
  • Coinbase threat actors behind the May breach have become active again, transferring $5 million DAI.
  • The hackers then swapped DAI to USDC using Circle’s CCTP bridge.
  • The stolen funds sat in a USDC address for over 35 minutes, but Circle’s compliance norms failed to freeze it.

After five months, the May Coinbase exploit hacker has swiped $5 million of DAI stablecoins for USDC using Circle’s CCTP bridge.

The incident is linked to a breach in which Coinbase users had been tricked into sending funds to attackers after they gained access to personal information.

At the time, Coinbase had estimated that the losses could mount to $400 million.

Try Our Recommended Crypto Exchanges
Sponsored
Disclosure
We sometimes use affiliate links in our content, when clicking on those we might receive a commission at no extra cost to you. By using this website you agree to our terms and conditions and privacy policy.

ZachXBT Alerts Community

On-chain Seluth ZachXBT shared the incident in his Telegram group, which tracked the movement of funds on the blockchain after months of idleness.

The on-chain investigator said that the threat actor from the “Coinbase breach swapped ~5M DAI for ~5M USDC, which had been sitting as USDC for 35 minutes.”

Due to Circle’s compliance policies and slow response times in freezing suspicious addresses, the funds were successfully extracted via bridges, including Circle’s official Cross-Chain Transfer Protocol (CCTP).

ZachXBT called out Circle for being inactive and non-compliant

“Due to Circle not being compliant, the funds were just bridged away.  A portion was bridged using the official Circle CCTP bridge.”

Circle’s policy allows blacklisting USDC addresses but requires manual review. The 35-minute idle was flagged in this case, but processing delays prevented a freeze. CCTP transfers are “validated” post-burn, so recovery is harder once they are minted at the destination.

Theat Actors and Social Engineering Technique

The May Coinbase breach was one of the largest in crypto exchange history. It exposed sensitive customer data for around 69,461 users and enabled social engineering attacks that led to direct thefts totaling $200–400 million.

Hackers bribed overseas customer support agents from Indian call centers like TaskUs to access internal Coinbase systems. These insiders stole data for <1% of monthly active users but targeted high-value accounts with 7–8 figure balances.

The threat actors managed to gain access to emails, phone numbers, the last four digits of SSNs, photo IDs, and physical addresses. This fueled phishing campaigns in which actors posed as Coinbase reps, tricking users into sending crypto.

The hackers behind the whole operation contacted Coinbase, demanding a $20 million bounty. However, the crypto exchange denied the ransom and converted it into a reward for anyone who could help them identify and recover funds.

Related Reads

Vitalik's Algorithmic Stablecoin Vision: Interpreting the Mechanism and Challenges from an Options Perspective

Vitalik Buterin's recent algorithmic stablecoin proposal envisions using an option-like mechanism to create a stablecoin without the liquidation risks inherent in traditional collateralized debt position (CDP) models. The design splits one unit of ETH into two components: a 'stable' leg (P) that maintains value up to a certain strike price, and an 'upside' leg (N) that captures any appreciation above that price. Together, they always sum to one ETH, eliminating the need for debt or liquidation mechanisms. From an options perspective, the stable leg essentially functions as a synthetic, covered call position. However, significant challenges exist. For the stable asset to maintain its peg, it must continuously roll deep in-the-money call options, leading to potential rollover slippage, predictable trading paths vulnerable to front-running, and liquidity issues. Crucially, the system's scalability depends on a constant demand for the upside leg—a form of leveraged ETH long position without funding rates or liquidation risk. It's unclear if such persistent, specific demand will materialize from speculators or market makers who have simpler alternatives like perpetual swaps. The author, drawing from experience with Rysk, argues that DeFi options have struggled as standalone trading products due to complexity and fragmented liquidity. Their potential lies instead as foundational infrastructure underpinning more complex financial primitives like stablecoins, structured yields, or index products—transforming from a direct product into a core pricing and risk distribution engine for the next generation of on-chain finance.

marsbit1h ago

Vitalik's Algorithmic Stablecoin Vision: Interpreting the Mechanism and Challenges from an Options Perspective

marsbit1h ago

GPT-5.6 Countdown: Abandon the Illusion of a Single API, Computational Iteration Can't Outpace a Single Page of Compliance

In mid-June, three seemingly independent industry events—the compliance-driven throttling of Fable 5, the open-sourcing of GLM-5.2, and the leaked release timeline for GPT-5.6—are pushing the global AI industry toward a watershed moment. These shifts signal a fundamental restructuring of the industry's underlying logic. First, **"usability" has substantially overtaken "advanced capabilities"** as the primary weight, pushing the global large language model (LLM) supply chain into a "dual-track" phase of controlled closed-source and local open-source coexistence. Second, **the competitive moats of closed-source giants are shifting**. Their technical focus is moving from "language intelligence" toward "spatial intelligence (world models)"—a domain heavily reliant on computing power. Third, faced with常态化 transnational compliance risks, **a "model-agnostic" decoupled design has become a survival necessity for application-layer developers to maintain business continuity.** The article details how Anthropic's Fable 5, despite its advanced engineering feats, was restricted for non-U.S. citizens within 72 hours of launch, highlighting how geopolitical compliance can instantly limit even the most advanced models. In response, the open-source camp, exemplified by Zhipu AI's MIT-licensed GLM-5.2, is gaining market share by offering stable performance improvements and significant cost advantages (up to 70% savings for enterprises), while achieving full adaptation with domestic semiconductor platforms. Meanwhile, closed-source leaders like OpenAI are pivoting. The anticipated GPT-5.6 reportedly shifts focus from language to spatial intelligence and world models, aiming to rebuild a generational gap in areas like 3D understanding, simulation, and industrial design that demand immense compute. The core conclusion is that the LLM supply chain's logic has changed. Enterprises must now evaluate infrastructure based on a composite of technical performance and policy compliance. For developers, complete reliance on a single closed-source API poses unacceptable risk. Implementing a truly model-agnostic architecture—enabling swift switches to compliant, locally deployable open-source alternatives—is no longer just good practice but a fundamental baseline for business continuity.

marsbit3h ago

GPT-5.6 Countdown: Abandon the Illusion of a Single API, Computational Iteration Can't Outpace a Single Page of Compliance

marsbit3h ago

Is the 'Token Subsidy War' Among AI Giants Almost Over?

The article discusses the ongoing "token subsidy war" among AI giants like OpenAI and Anthropic, questioning whether it's nearing its end. It reveals that current AI subscription prices are heavily subsidized, with some plans offering tokens at up to 70 times the actual cost to attract and retain heavy users, especially developers and enterprises. This strategy mirrors past internet-era subsidy battles, but with a key difference: AI tokens lack "lock-in" effects. Unlike ride-hailing or food delivery apps, users can easily switch between AI providers as APIs become standardized, making it difficult for companies to raise prices post-subsidy. The piece highlights a structural asymmetry in the competition. Giants like Google, with massive advertising revenue, can afford to subsidize tokens indefinitely, akin to using "tokens as a weapon." In contrast, venture-backed companies like OpenAI and Anthropic face pressure to become profitable, especially as they approach IPO. The article cites Google Ventures founder Bill Maris, who suggests Google could slash token prices by 80%, putting immense pressure on competitors. Two potential endgames are presented: the "internet service" model (subsidize, monopolize, then raise prices) and the "utility" model (tokens become a standardized, low-margin commodity like electricity). Given the low switching costs, the latter seems more likely. The competition may not have a single winner but could instead accelerate AI's evolution into a foundational, infrastructure-level technology, akin to a public utility. For now, users continue to benefit from heavily subsidized token costs.

marsbit4h ago

Is the 'Token Subsidy War' Among AI Giants Almost Over?

marsbit4h ago

Trading

Spot
Futures
活动图片