Crypto lost $6.50B across 35 incidents in October 2026, nearly nine times September's $742.0M and the worst month for hacks on record. Almost all of it came from a single event. On Oct 29, attackers drained an estimated $6.3B from Binance's hot wallets in about 20 minutes, the largest crypto theft ever recorded, and the FBI attributed it on Oct 31 to North Korea's TraderTraitor group.
Crypto lost $742.0M across 33 incidents in September 2026, almost three times August's $255.5M and the biggest month of the year so far. Bitget's $387M hack and Liquid Network's $320M exploit account for about 95% of that, with the other 31 incidents adding up to $35.0M. In August, the prices going into contracts were wrong. In September, attackers went after the systems that approve transactions in the first place. Here's the month in security 👇
Crypto lost $255.4M across 46 incidents in August 2026, roughly level with July's $254.4M, with more incidents than any month this year. June and July incidents were largely due to key compromises, but August shifted to price manipulation. Eleven oracle and price-manipulation incidents accounted for $135.87M, or 53% of the month. Two chains stopped producing blocks and rolled back state to undo the damage. Here's the month in security 👇
Crypto lost more than $240 million across 29 incidents in July 2026, up roughly 216% from June's $75.87M, even as the number of attacks fell from 40 to 29. For the second month running, the damage didn't come from clever contract exploits. The same failure class ran through Web2, from a $12.3M rail-industry ransom to a 1TB bank breach that started with one employee's email. Here's the month in security 👇
$75.32M was lost across 32 crypto incidents in June, up from May's $59.52M. No coordinated campaign carried the month, but one targeted operation did. A targeted social-engineering attack against Humanity Protocol reached the keys behind the $H token and drained $32M, roughly 42% of every dollar lost in June. Quantstamp led the independent investigation and traced the tooling to a phishing campaign previously seen targeting macOS users. Offchain, a fresh npm supply chain wave hit Red Hat's packages on the first day of the month, and a PeopleSoft zero-day was exploited for two weeks before Oracle said a word. Here's the month in security 👇
$59.52M was lost across 29 crypto incidents, down sharply from April's ~$635M. No single hack carried the month. The bigger story happened off-chain, where a self-propagating npm worm called Mini Shai-Hulud kept resurfacing in new waves through the month, ultimately spanning more than 1,000 malicious package versions across the npm ecosystem.
April was undoubtedly a rocky month in security. $635M was lost across 28 crypto incidents. The Axios npm package was compromised on day one, exposing an estimated 600,000 installs in three hours. Vercel was breached through a third party. Three major CVEs under active exploitation. Here's the month in security 👇
Decentralized applications (dApps) (e.g., DEXes) increasingly span multiple Ethereum-compatible chains, such as a number of L2s. Although these chains are intended to be compatible with the Ethereum Virtual Machine (EVM), subtle differences in opcode implementations can significantly alter smart contract behavior and security. This poses an important question: how can developers efficiently code and manage smart contracts targeting different chains?
The upcoming EVM hardfork, Pectra, amongst other changes, will implement EIP-7702, a proposal introducing a new transaction type that allows Externally Owned Accounts (EOAs) to delegate—and later undelegate—their behavior to smart contracts. While this upgrade enhances flexibility, it also disrupts long-standing security assumptions in many deployed contracts. With the risk that malicious actors may exploit these changes once Pectra is enabled, it is crucial to assess whether your codebase might be negatively impacted.
In recent months, AI agents have attracted significant attention by the promise of assisting users and automating complex processes across diverse applications. The rapid performance improvements of Large Language Models (LLMs) in natural language processing (NLP) tasks drive this trend. However, as the capabilities and reach of these agents expand, so do the risks. The rapid pace of development, combined with the intricacies of integrating LLMs into real-world infrastructures—especially in dynamic fields like blockchain—has created an urgent need to scrutinize them for security, compliance, and operational integrity.
