Binance Wallet Prevents $540 Million in Potential Losses in First Half of 2026
According to Binance's official disclosure, in the first half of 2026, the Binance Wallet Security Center helped users avoid approximately $540 million in potential losses, conducting real-time risk interception from various aspects such as spam transfers, malicious transactions, and authorizations. This security center officially launched on February 3 of this year.
Data shows that in the first half of the year, the security center filtered around 206 million spam transfers, covering 2.6 million users; identified about 4.93 million high-risk transactions across 19 blockchains; and discovered approximately 996,000 malicious authorizations.
Binance stated that attackers are using AI to generate malicious code, phishing websites, and false identities in bulk, leading to a nearly threefold increase in the number of new malicious entities compared to the previous period. The attack patterns have shifted from "large-scale net casting" to more precise targeted fraud; the application of AI technology has reduced the cost for attackers to create fake websites, deepfake videos, and malicious scripts to nearly zero.
Binance claims that its security system has begun using AI to analyze the behavioral logic and risk intentions of tokens and websites, focusing on identifying new attack methods such as "buy only, cannot sell" and hidden backdoors; this security center supports Binance's keyless wallet using multi-party computation (MPC) technology and also covers wallets imported via mnemonic phrases or private keys, allowing users to revoke malicious authorizations, filter suspicious transactions, and manage login devices within the same interface.
Reports mention that the security company SlowMist audited the design and implementation of this security system in February this year; Binance advises users to confirm specific operational content before signing transactions and to regularly scan for risk authorizations and assets through the Binance Wallet Security Center.
Additionally, according to a broader security report previously disclosed by Binance, from Q1 2025 to Q2 2026, its AI-driven security system helped users avoid approximately $10.53 billion in potential losses, with about $1.98 billion in potential losses intercepted from 22.9 million fraud and phishing attempts in Q1 2026 alone, recovering approximately $12.8 million (involving 48,000 cases) and confiscating illegal funds of about $131 million; currently, the AI-driven decision-making mechanism covers 57% of anti-fraud risk control processes, reducing credit card fraud rates by 60% to 70%.
ABAB AI Insight
Binance's investment in security protection is not new—previously, the platform disclosed broader security achievements that avoided approximately $10.53 billion in potential losses from Q1 2025 to Q2 2026, continuously covering scenarios such as computer vision recognition of fake payment vouchers and real-time language analysis to identify malicious P2P trading patterns through about twenty AI-driven security features; the specific disclosure of the Binance Wallet Security Center's interception results of $540 million in the first half of the year represents the latest step in the platform's security system construction, extending from "exchange-level anti-fraud" to "wallet terminal-level proactive defense."
From the cost structure of both attackers and defenders, AI technology is simultaneously lowering the marginal costs on both sides—attackers are using AI to reduce the production costs of fake websites, deepfake videos, and malicious scripts to nearly zero, which is the direct reason for the nearly threefold increase in the number of malicious entities; Binance is also leveraging AI to analyze the behavioral logic of tokens and websites, attempting to use an equivalent level of technological leverage to hedge against the explosive growth of attack scales. This "arms race" around AI objectively raises the technical investment threshold required to maintain basic security levels in the entire cryptocurrency industry, and if small and medium-sized trading platforms and wallet vendors cannot match this investment intensity, their risk exposure will be further amplified.
This is highly similar to the historical path of the traditional financial industry's credit card anti-fraud systems evolving from rule engines to machine learning models—banks also experienced an upgrade process of "being forced to introduce behavioral analysis and AI modeling after fraud rules were breached" in earlier years, and the data disclosed by Binance showing a "60% to 70% reduction in credit card fraud rates" is, to some extent, borrowing the mature narrative of traditional financial anti-fraud systems to endorse the security capabilities of the cryptocurrency industry. Currently, cryptocurrency wallet security protection is in a critical transition phase from "manual rule interception to AI behavioral intent recognition," especially targeting new contract traps designed specifically for retail investors, such as "buy only, cannot sell."
Essentially, this is a "technological substitution"—at the mechanism level, traditional security protection primarily relies on blacklists of known malicious addresses and static rule interceptions, while attackers, using AI to achieve large-scale and customized attacks, have dramatically amplified the lag of this static defense system; Binance has turned to using AI to analyze the "behavioral logic and risk intentions" of token contracts and websites, essentially upgrading the basis for security protection judgment from "whether it hits known features" to "whether it has potential malicious intent." This upgrade path is expected to become the standardized direction for the entire cryptocurrency wallet industry in responding to AI-driven attacks.
ABAB News · Cognitive Law
- AI lowers not only production costs but also the threshold for wrongdoing.
- When both attackers and defenders compete with the same weapon, the one lagging behind is always the one that hasn't kept up.
- Rules can defend against known bad actors, but intent recognition can defend against unseen bad actors.