Hackers Targeting Your Crypto Just Got An AI Upgrade — Google’s Report Is A Wake-Up Call

bitcoinistPublished on 2026-05-12Last updated on 2026-05-12

Abstract

Google's Threat Intelligence Group warns that AI is now being weaponized by hackers at an industrial scale, posing a direct threat to cryptocurrency users. The report details a major escalation: threat actors, including state-linked groups, are using AI to develop zero-day exploits and polymorphic malware that evades detection. A key threat is PROMPTSPY, AI-enabled malware capable of autonomous, real-time attacks that can bypass standard two-factor authentication (2FA) by observing and manipulating live authentication sessions. The findings indicate that conventional security measures like 2FA are becoming insufficient against these AI-driven tools. The report recommends advanced protections like hardware security keys and multi-signature wallets as essential for crypto users facing this new threat landscape.

Google’s Threat Intelligence Group (GTIG) has published a major security report warning that artificial intelligence is now being weaponized by state-linked hackers and criminal threat actors at industrial scale — with autonomous malware, AI-generated zero-day exploits, and credential-targeting operations posing a direct and escalating threat to crypto users relying on standard security measures.

The May 11 report, published on the Google Cloud blog by GTIG and drawing on Mandiant incident response engagements, marks a significant escalation from the group’s February 2026 findings. Where that earlier report identified AI-assisted adversarial activity as nascent and experimental, the latest assessment describes a mature transition — one where generative models are now embedded in offensive workflows at scale, not as a curiosity but as operational infrastructure.

ETH's price records some losses on the daily chart. Source: ETHUSD on Tradingview 

AI Writes Its First Zero-Day Exploit

The most significant disclosure in the report is unprecedented. For the first time, GTIG has identified a threat actor using a zero-day exploit believed to have been developed with AI assistance. According to the report, a criminal threat actor had planned to deploy the exploit in a mass exploitation event — a scenario that GTIG’s proactive counter-discovery may have prevented.

The report notes that state-linked actors associated with China and North Korea have separately demonstrated significant interest in using AI for vulnerability discovery. The implications for crypto users are direct: wallet interfaces, exchange login portals, and browser extension-based authentication tools all depend on the same underlying software layers that zero-day exploits target.

Polymorphic Malware And The Limits Of 2FA For Crypto Users

Beyond zero-day development, the report documents AI-accelerated development of polymorphic malware — code that rewrites its own structure to evade detection — linked to suspected Russia-nexus threat actors, per GTIG’s analysis. AI-generated decoy logic is being embedded in malware payloads to defeat signature-based security systems.

The most direct threat to crypto users, however, comes through a capability GTIG calls PROMPTSPY — an AI-enabled malware that signals a shift toward autonomous attack orchestration. According to the report, PROMPTSPY interprets system states dynamically and generates commands in real time to manipulate victim environments. Applied to credential theft, this class of malware can observe and respond to authentication flows in ways that static attack tools cannot — including timing attacks against SMS-based and app-based two-factor authentication systems during live sessions.

Standard 2FA, long considered a reliable security baseline for exchange and wallet access, operates on the assumption that an attacker cannot observe and respond to the authentication window in real time. Autonomous, AI-driven malware capable of interpreting system states changes that assumption materially.

A Threat Environment That Has Shifted

GTIG’s report frames the current moment as a dual-use inflection point — AI is simultaneously becoming a high-value target for attacks and a sophisticated engine driving them. For participants in the nascent digital asset sector, where a single compromised seed phrase or session token represents an irreversible loss, the implications are substantial.

The security practices that adequately protected crypto users two years ago are increasingly insufficient against an adversarial toolkit that now includes AI-generated exploits, self-modifying malware, and autonomous credential-harvesting operations operating faster than human defenders can respond.

Hardware security keys, air-gapped signing devices, and multi-signature wallet architectures represent the current frontier of meaningful protection — and the distance between those measures and standard 2FA has never been wider.

Cover image from Grok, ETHUSD chart from Tradingview

Related Questions

QAccording to Google's report, what major shift has occurred in how AI is being used by threat actors targeting crypto?

AThe report states that AI has transitioned from being used in nascent and experimental ways to being embedded at scale in offensive workflows as operational infrastructure, with generative models now used for autonomous malware, AI-generated zero-day exploits, and credential-targeting operations.

QWhat is the significance of the zero-day exploit mentioned in the GTIG report?

AThe report discloses, for the first time, a threat actor using a zero-day exploit believed to have been developed with AI assistance. This exploit was intended for a mass exploitation event, a scenario potentially prevented by GTIG's proactive discovery.

QHow does the AI-enabled malware PROMPTSPY specifically threaten crypto users' standard 2FA security?

APROMPTSPY is an autonomous malware that interprets system states and generates commands in real-time. It can observe and respond to authentication flows, enabling timing attacks against SMS-based and app-based two-factor authentication (2FA) during live sessions, which static tools cannot do.

QWhat are the primary defensive measures recommended for crypto users in light of the AI-upgraded threats described in the report?

AThe report suggests that hardware security keys, air-gapped signing devices, and multi-signature wallet architectures represent the current frontier of meaningful protection, as the gap between these measures and standard 2FA has widened significantly.

QWhich state-linked actors does the report mention as showing significant interest in using AI for vulnerability discovery?

AThe report notes that state-linked actors associated with China and North Korea have demonstrated significant interest in using AI for vulnerability discovery.

Related Reads

U.S. Government Bans Foreign Nationals from Using Fable 5, Anthropic Issues Rebuttal

U.S. Government Bans Foreign Access to Fable 5, Anthropic Issues Rebuttal On June 12th, the U.S. government ordered AI company Anthropic to immediately suspend all foreign access—including foreign nationals within the U.S. and Anthropic's own foreign employees—to its newly released Fable 5 and Mythos 5 AI models, citing national security concerns. This forced Anthropic to temporarily disable access to both models for all users globally, as it cannot technically differentiate user nationality at scale. The models, released just three days prior, represent Anthropic's highest public capability tier. Fable 5 is the first publicly available model from the advanced "Mythos" family, while Mythos 5 is a less-restricted version for approved cybersecurity and critical infrastructure partners. The government's directive was reportedly triggered by claims from another company that it could "jailbreak" Mythos 5, raising alarm within the Trump administration. Anthropic, in a detailed public statement, strongly challenged this rationale. The company argues the demonstrated "jailbreak" is a narrow, non-generalized technique that merely involves identifying minor, known software vulnerabilities—a capability common to other publicly available models like OpenAI's GPT-5.5 and routinely used by cybersecurity defenders. Anthropic stated it has complied with the order but disagrees with the government's standard, warning that applying it industry-wide would halt all new frontier model deployments. The company criticized the lack of a transparent, fact-based legal process and expressed confidence the situation stems from a misunderstanding. It is working to restore access and will release more technical details within 24 hours. Other Anthropic models remain unaffected.

链捕手16m ago

U.S. Government Bans Foreign Nationals from Using Fable 5, Anthropic Issues Rebuttal

链捕手16m ago

The Revelation from the Raydium Theft Incident: New DeFi Vulnerabilities Lurking in Forgotten Old Contracts

**Raydium Exploit Reveals DeFi's Hidden Risk: Forgotten "Zombie" Contracts** A recent attack on Raydium's deprecated V3 AMM pools resulted in a loss of approximately $1.34 million. The hacker exploited pools that were no longer supported by Raydium's current UI or SDK but remained fully functional and accessible on-chain. This incident highlights a critical, often overlooked category of risk in DeFi: inactive or legacy smart contracts that projects fail to properly decommission. Since March 2025, there have been at least 8 publicly reported attacks targeting such abandoned contracts, with total losses around $10.8 million. Including older pools and deprecated features, the count rises to 10 incidents with roughly $22.5 million in losses. These "zombie contracts" represent a lifecycle management failure rather than a code vulnerability, yet they are typically misclassified under general "code bug" categories in security reports, masking the true scale of the problem. The root cause is that projects often merely document a contract as "deprecated" without taking essential technical steps to secure it: withdrawing remaining assets, disabling external call functions, and implementing ongoing monitoring. These forgotten, under-monitored components become prime targets for attackers. To address this, the industry needs to recognize "zombie contracts" as a distinct risk category and establish standardized decommissioning protocols. Essential steps should include: 1) a formal retirement announcement, 2) removal of all front-end integrations, 3) withdrawal of locked assets, 4) disabling key contract functions, 5) ongoing security monitoring, 6) clear user communication, and 7) a post-mortem analysis. The value of a DeFi project lies not only in its current TVL but also in the security of its historical codebase, which has now become a new attack surface.

Foresight News2h ago

The Revelation from the Raydium Theft Incident: New DeFi Vulnerabilities Lurking in Forgotten Old Contracts

Foresight News2h ago

Robots Begin to 'Consume Data': The Hidden Production Chain from Indian Data Factories to Billion-Dollar Humanoid Robots

Robots have started to 'consume data,' driving the formation of a new industrial supply chain focused on producing training data for embodied AI. Unlike large language models, which are trained on vast internet text corpora, embodied AI models face a 'data desert' in the physical world. This has created a massive demand for first-person perspective video data (Ego Data), captured by workers wearing cameras in places like Indian garment factories. Companies like Neocambrian AI are establishing 'data factories' where workers perform standardized tasks (e.g., sorting clothes, kitchen organization) to generate thousands of hours of video. Research, such as NVIDIA's EgoScale, demonstrates that scaling this human demonstration data predictably improves robot performance, particularly for dexterous manipulation. This has validated a training path combining large-scale human data for pre-training with smaller amounts of robot-specific data for fine-tuning. The value of different data types varies significantly, forming a 'data pyramid.' The base consists of low-cost, large-scale internet and Ego Data. Higher layers include more expensive motion-capture data (e.g., from data gloves), simulation/synthetic data, and the most costly and scarce layer: real robot teleoperation data. This demand has spawned a layered ecosystem of data suppliers: low-cost data factories, motion capture and alignment specialists, robot-native teleoperation service providers, simulation data companies, and platforms aiming for data standardization. Robot companies themselves are adopting a 'layered procurement' strategy: outsourcing generic Ego Data while building in-house capabilities for robot-specific adaptation data and the critical deployment/failure data generated in real-world applications. The industry is shifting focus from hardware and basic mobility to the data pipelines required for general-purpose capability. While parallels exist to data labeling companies like Scale AI in the LLM boom, the physical complexity of robot data—involving action success ambiguity and sim-to-real gaps—requires more integrated solutions for data collection, annotation, and a continuous feedback loop. The race is on to build the data engines that will teach robots to operate reliably in the unstructured real world.

marsbit4h ago

Robots Begin to 'Consume Data': The Hidden Production Chain from Indian Data Factories to Billion-Dollar Humanoid Robots

marsbit4h ago

Trading

Spot
Futures

Hot Articles

Discussions

Welcome to the HTX Community. Here, you can stay informed about the latest platform developments and gain access to professional market insights. Users' opinions on the price of AI (AI) are presented below.

活动图片