Beware of Prompt Injection Attacks: How Hackers Trick Your AI into Transferring Crypto Assets

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You may interact with AI assistants every day, even using them to help manage crypto assets. A simple conversation could hide a prompt injection attack — hackers only need to carefully craft a piece of content to make the AI misinterpret instructions and transfer your digital assets. Have you ever carefully considered the security risks behind AI wallets?

Key Takeaways

• Prompt injection attacks use cleverly designed input content to induce AI to perform unauthorized operations, potentially leading to the theft of crypto assets.
• Attackers employ social engineering, malicious links, and embedded prompts to hide malicious instructions, increasing the stealth of attacks.
• Users should carefully verify input content, use clear prompt delimiters, restrict AI permissions, and prevent leakage of sensitive information.
• Developers need to strengthen input validation, limit AI access to sensitive operations, regularly update security policies, and ensure system security.
• Enterprises should establish layered defense systems, implement multi-factor authentication and real-time monitoring to reduce risks brought by prompt injection.

Attack Flow and Deception Mechanisms

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Typical Scenarios

When using AI assistants to manage crypto assets, you may encounter various prompt injection attack scenarios. Attackers often use the input channels of AI models to hide malicious instructions in seemingly harmless content. For example, attackers may embed special commands in web pages, emails, or documents. When the AI assistant automatically reads this content, it unintentionally executes unauthorized operations. You may have heard that Microsoft’s new Bing search engine once exposed hidden instructions due to prompt injection attacks, and GitHub Copilot has also been exploited to steal confidential information. These cases show that AI systems are highly vulnerable to prompt injection threats when processing external data.

In crypto asset scenarios, attackers also use generated fake social media profiles, forged investment websites, or embedded AI-powered chatbots to induce you to click malicious links or enter sensitive information. Once you interact with AI assistants on these platforms, attackers can seize the opportunity to manipulate the AI into performing high-risk operations such as asset transfers or leaking private keys. In theory, any process involving AI automated decision-making can become a target for attacks.

Attack Steps

You need to understand the complete flow of prompt injection attacks to effectively defend against them. Attackers typically go through the following stages:

In daily operations, you may encounter the following specific techniques:

• Attackers embed malicious instructions in content that the AI is about to retrieve (such as web pages, documents, emails) to induce the AI to perform unauthorized operations.
• In direct prompt injection, attackers interact with AI applications and design input content to bypass security protections, directly triggering sensitive operations such as asset transfers.
• Indirect prompt injection is more covert; attackers poison content that the AI will access later, and the malicious instructions are only activated when you query or operate.
• Once initial access is obtained, attackers attempt to bypass the AI model’s safety policies and perform “jailbreaking” to further elevate privileges.
• Attackers then reconnaissance the AI’s capabilities and connected services, establish persistence, and maintain control over the compromised application.

You also need to be wary of social engineering attacks. Attackers may induce you to disclose wallet private keys or account information through interpersonal interactions, forged information, spreading fake news, and other methods. Generative AI tools enable attackers to quickly create fake social media profiles, automatically send phishing messages, and generate multilingual scam content, greatly improving attack efficiency and stealth.

In the process of managing crypto assets, you must be vigilant about the following emerging techniques:

• Malicious links: After you click, the AI assistant automatically parses and executes hidden prompts.
• Embedded prompts: Instructions hidden in documents, emails, or web pages, manipulating the AI’s memory and behavior.
• Social engineering: You are induced to paste prompts containing memory modification commands, causing long-term control of the AI.

Prompt injection attacks can not only bypass traditional security protections but may also lead to unauthorized data access, asset transfers, and leakage of sensitive information. You must remain constantly vigilant and identify these complex and ever-changing attack flows to effectively protect the security of your crypto assets.

Prompt Injection Principles and Risks

Definition of Prompt Injection

When using AI assistants, you may encounter prompt injection attacks. Prompt injection is an attack method targeting natural language processing systems, where attackers induce AI to perform unauthorized operations through carefully designed input content. Unlike traditional SQL injection, prompt injection exploits the AI model’s understanding of natural language, blurring the boundary between instructions and data. You can intuitively understand the differences between the two through the table below:

You need to note that prompt injection attacks cannot be defended by traditional parameterized queries or similar methods. Attackers can override system instructions and bypass safety policies, causing AI assistants to perform sensitive operations they should not execute. This attack method is highly stealthy and easily overlooked by you.

System Vulnerabilities

When managing crypto assets, AI systems often expose various vulnerabilities. The following are the most common system weaknesses:

• Social engineering: Attackers impersonate developers or administrators to manipulate AI assistants; the vast majority of cases (85.2%) use this method.
• Insufficient input validation: AI systems lack strong input validation mechanisms, allowing attackers to easily inject malicious content.
• Malicious instruction embedding: Attackers hide malicious instructions in web pages, emails, documents, etc., using plaintext injection, HTML attribute concealment, CSS rendering hiding, and other methods to induce AI to perform dangerous operations.

In daily operations, you may encounter scenarios where AI assistants automatically parse web content, process user input, read external data, etc. If the system does not strictly validate input content, attackers can easily exploit these vulnerabilities to launch prompt injection attacks. You need to be wary of the AI assistant’s “memory” function — attackers may implant malicious instructions in the AI’s long-term storage, forming “false memories” that activate in subsequent operations.

Risks in Crypto Asset Scenarios

Prompt injection poses extremely high risks in crypto asset management scenarios. You need to understand the following specific threats:

• Malicious actors exploit security vulnerabilities in AI-managed investment portfolios to launch attacks.
• After the system is hijacked, the AI assistant may automatically perform operations such as asset transfers and leaking private keys.
• Man-in-the-middle attacks insert hackers into communication channels to steal data or redirect transactions.
• AI assistants interact with scam tokens, fall into traps, or mishandle slippage, leading to financial losses.
• Malicious skills such as “crypto asset checker,” “gas fee optimization tool,” and “smart contract audit assistant” specifically target crypto user needs, forming a complete “skill supply chain” attack system.

Research shows that attackers can manipulate the stored context in AI agents to reroute transactions and steal funds in crypto wallets. You need to be vigilant against attackers injecting false information into the memory of AI agents, causing the AI to send funds to the attacker’s wallet address during transaction execution.

Security audit data shows that 36% of AI-driven crypto platforms have reported prompt injection vulnerabilities, indicating that this risk has become a key industry concern.

You also need to pay attention to the ranking of prompt injection in the AI security field. In OWASP’s 2025 Top 10 security risks for LLM applications, prompt injection is ranked first. Reports from HackerOne and others also indicate that prompt injection ranked first in both 2023 and 2025, showing that this threat will not disappear. You must treat prompt injection as a core issue in AI security protection to prevent AI assistants from becoming “Trojan horses” that endanger the security of your digital assets.

Attack Techniques and Real Cases

Direct Injection

When interacting with AI assistants, direct injection is the most common attack method. Attackers directly embed malicious instructions in the input content to induce the AI to perform unauthorized operations. For example, attackers may create a web page containing hidden text that is invisible to humans but visible to AI. This text instructs the AI assistant to ignore safety policies or even leak sensitive information. You also need to note that some AI systems, such as the external-content.ts module in OpenClaw, have weak input validation mechanisms, allowing attackers to inject payloads via Unicode escaping or Base64 encoding to bypass routine detection.

• Attackers use hidden text to manipulate AI behavior
• Use encoding methods to bypass input validation

Indirect Injection

Indirect injection is more covert. In daily operations, AI assistants may automatically process external data such as market reports, emails, or third-party APIs. Attackers embed malicious instructions in these data sources, and the AI cannot distinguish normal content from added commands during analysis. For example, attackers insert instructions in market research reports; when the AI analyzes them, it may unintentionally leak sensitive company data. You need to be vigilant — indirect injection is often difficult to detect and causes great harm.

• Malicious content hidden in external data sources
• Unauthorized operations triggered during automatic AI processing

Hidden Payloads

In practical applications, you will find that attackers continuously innovate ways to hide payloads, improving the stealth and complexity of prompt injection. Common techniques include:

• Delimiter injection: Insert false system prompt boundaries to induce the AI to recognize new instructions
• Role and context hijacking: Forge conversation frameworks to override real system prompts
• Language switching and encoding: Hide malicious content using multiple languages or encoding methods
• Semantic rephrasing: Indirectly express intentions to evade keyword filtering
• Advanced techniques: Such as invisible content, cross-modal attacks, long-context hijacking, etc.

These methods make it difficult for traditional security detection to identify anomalies, increasing the difficulty of protection.

Real Cases

You can refer to the following real cases to understand the actual harm of attack techniques:

A certain AI-driven crypto asset management platform once suffered a direct injection attack due to insufficient input validation. The attacker successfully bypassed detection using a Base64-encoded malicious payload, resulting in the transfer of some users’ assets. In addition, research shows that attackers embedded instructions in market analysis reports, causing the AI assistant to leak sensitive pricing information during automatic analysis, resulting in significant economic losses.

These cases demonstrate that prompt injection has become a core threat in the AI security field. You must enhance security awareness, improve input validation and data processing flows to effectively prevent such attacks.

Crypto Asset Risks and Consequences

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Asset Transfer Paths

After encountering a prompt injection attack, hackers quickly transfer stolen crypto assets using various complex paths to conceal the flow of funds. Common methods include:

• Hackers launder mainstream assets such as Ethereum through mixing services (e.g., Tornado Cash), breaking up the fund flow and increasing tracking difficulty.
• Funds are frequently traded between multiple decentralized exchanges (such as Uniswap and ParaSwap) to disperse digital footprints and conceal the true destination.
• Hackers repeatedly convert assets between different cryptocurrencies such as USDT and BTC, ultimately dispersing them to exchanges with weaker regulation, further increasing recovery difficulty.
• Funds may also be routed through legitimate contracts (such as SushiSwap); the trading behavior appears to be ordinary token swaps, but actually hides the real intent and reduces the probability of detection by security systems.
• Some security service providers allow interaction with popular contracts by default, providing hackers with more cover.

You need to understand that these paths are extremely covert. Hackers exploit the anonymity and decentralization characteristics of blockchain to quickly complete asset transfers, greatly increasing the difficulty of asset recovery.

Tracking and Recovery Difficulty

After discovering stolen assets, you usually try to track the flow of funds through blockchain explorers. Although blockchain is public and transparent, hackers use mixing services and frequent cross-chain operations to greatly increase tracking difficulty. You will find:

• Funds pass through multiple mixers and cross-chain bridges, forming complex fund flow networks that are difficult to fully reconstruct.
• Assets are dispersed to multiple small-amount accounts, reducing the feasibility of tracking single transactions.
• Exchange accounts in regions with weak regulation become the final destination for hackers, lacking effective identity verification and hindering law enforcement intervention.

You need to understand that although blockchain technology has traceability, hackers’ professional techniques and global distribution make recovering stolen assets extremely difficult. Even if some funds are frozen, the recovery process is often time-consuming, costly, and has a low success rate.

Legal and Economic Losses

After crypto assets are stolen, you will face serious legal and economic losses. First, once assets are transferred to overseas or anonymous accounts, legal accountability becomes significantly more difficult. You may need cross-border cooperation involving multiple jurisdictions, increasing the cost of rights protection. Second, economic losses are not limited to direct asset losses but also include:

You also need to bear chain reactions such as business interruption and customer trust crises caused by asset loss. For enterprise users, once a security incident occurs on the platform, it may face regulatory penalties and high compensation. You must attach great importance to the systemic risks brought by prompt injection, improve security protection, and reduce the probability of legal and economic losses.

Prompt Injection Protection Recommendations

User Security Recommendations

When using AI wallets or digital asset management tools, you should proactively adopt multiple protective measures. First, carefully verify all input content on platforms such as AFL and BiyaPay to avoid copying and pasting information from unknown sources. You can use the following methods to enhance security:

• Check and filter input to identify obvious attack patterns.
• Use clear prompt delimiters (such as ### or XML tags) to distinguish system instructions from user data and prevent AI misjudgment.
• Grant the AI only the minimum necessary permissions to reduce potential losses.
• Proactively check whether the AI output contains sensitive information such as private keys or identity data before it is finalized.

If the action itself involves moving assets, the security habit needs to go one step further. You should enter relevant functions only through the official BiyaPay website or the official remittance page, and manually verify the transfer route, currency, amount, and account details instead of acting directly on chat content, auto-generated summaries, or prompts from unfamiliar pages.

The point of this approach is not to rely on “smarter automation,” but to bring sensitive actions back into a human confirmation workflow. BiyaPay works as a multi-asset wallet covering cross-border payments, investing, trading, and fund management scenarios, and it operates with relevant compliance registrations in jurisdictions including the United States and New Zealand. For users, the most practical defense still comes from official entry points, restricted permissions, and step-by-step verification.

You can also pay attention to open-source security tools such as Rebuff and NeMo Guardrails, which can help you detect and defend against prompt injection risks. Regularly learn relevant security knowledge to improve self-protection capabilities.

Developer Protection Measures

As a developer, you should prioritize input validation and content detection when designing AI-driven crypto asset applications. You can:

• Automatically detect and clean potential malicious commands before data enters the model.
• Adopt boundary awareness mechanisms and special tag integration to help the model distinguish external content from user instructions.
• When designing applications, restrict AI access to sensitive operation interfaces and adopt the principle of least privilege.
• Continuously update security policies, combined with behavior monitoring and context analysis, to proactively identify abnormal requests.

You should also pay attention to industry standards, regularly participate in red team exercises and penetration testing to ensure the defense system is effective.

Enterprise Risk Control Practices

When enterprises deploy AI financial platforms (such as BiyaPay’s global payment and receipt, USDT to USD/HKD exchange services, etc.), they should establish layered defense systems. You can:

• Implement multi-factor authentication and role-based access control to ensure only authorized users can operate key assets.
• Real-time monitoring of all AI interactions, logging records, and timely detection of suspicious behavior.
• Regularly conduct system penetration testing to identify and fix security vulnerabilities.
• Adopt output filtering mechanisms to prevent leakage of sensitive data.

You also need to continuously update AI security strategies, adopt advanced AI-driven defense systems, and cultivate employee cybersecurity awareness. This can effectively reduce systemic risks brought by prompt injection and protect the security of the platform and user assets.

You need to attach great importance to the systemic threat that prompt injection poses to AI and crypto asset security. Attackers can manipulate the memory and logic layers of AI agents to initiate unauthorized operations, leading to the loss of sensitive account assets. The table below summarizes the main risks:

You should continue to monitor AI security developments, proactively learn protection knowledge, and leverage security mechanisms such as Darktrace to protect the security of your digital assets.

FAQ

What is a prompt injection attack?

A prompt injection attack refers to an attacker inducing an AI to perform unauthorized operations through carefully designed input content, leading to the leakage of sensitive data or asset transfers.

How to determine if an AI assistant has prompt injection risks?

You can check whether the AI assistant automatically processes external data, lacks input validation, or allows users to directly control system instructions — these are all high-risk characteristics.

Can stolen assets be recovered after a prompt injection attack?

After assets are transferred, hackers usually use mixing services and cross-chain operations to conceal the flow of funds, making recovery extremely difficult. It is recommended to report to the police promptly and contact professional security teams.

How can AI-driven crypto asset management platforms improve security?

You should adopt multi-factor authentication, permission minimization, real-time monitoring and logging, regularly conduct penetration testing, promptly fix system vulnerabilities, and reduce risks.

What prompt injection protection tools are available?

You can use open-source tools such as Rebuff and NeMo Guardrails, combined with behavior monitoring and context analysis, to enhance the AI system’s ability to identify malicious instructions.