AI agents are moving from code-generation assistants toward systems that can search for weaknesses, use external tools and execute multi-step workflows, creating a new security concern for cryptocurrency platforms and wallets that rely on automated permissions.
CrowdStrike said in early October that it found “agentic AI” incorporated into an attacker workflow during its investigation of intrusions targeting South Korean financial institutions. According to the cybersecurity company, the operation connected to several large language models, including DeepSeek, GLM and Grok, and applied them to penetration testing, information gathering and attack execution.
The reported activity does not establish that autonomous agents have become a standard feature of financially motivated cybercrime. It does show how attackers can combine AI models with conventional hacking tools in a workflow designed to reduce manual effort. A system able to collect intelligence, test a target’s defenses and act on findings could shorten the gap between discovering a weakness and exploiting it.
Multi-agent systems can extend attacks over days
Anthropic described a related development in a threat intelligence report published in September. The company said malicious actors had used multi-agent frameworks for reconnaissance, exploitation and data theft, with systems operating continuously for hours or days.
In these setups, a human operator may select targets, approve a general objective or review the final results, while separate software agents handle narrower tasks. One agent may scan a network for exposed services, another may organize information from the scan, and another may attempt to use approved tools against identified targets.
That division of labor resembles legitimate enterprise uses of AI agents, where organizations assign different models to research, risk review, customer support or transaction operations. In an adversarial setting, the same architecture could make an attack more persistent and adaptable than a single chatbot session.
For cryptocurrency businesses, the greatest exposure would often lie around systems that combine AI with sensitive access: internal dashboards, cloud infrastructure, wallet administration software, code repositories and transaction-signing workflows. The risk rises when an agent can move from reading data to taking action without a separate approval boundary.
Email prompt injection exposed a direct tool-use risk
Salt Labs disclosed details on Oct. 1 of a previously patched vulnerability affecting Manus, which it described as an email-based prompt-injection attack. Prompt injection occurs when hostile instructions are placed inside content that an AI system is asked to process, attempting to override the user’s intended request.
Salt Labs said a user could ask an agent to review an apparently normal email containing hidden malicious instructions. The instructions could then induce the agent to execute attacker-supplied code. The reported attack flow did not require a victim to click a malicious link or surrender account credentials beforehand.
According to Salt Labs, Manus’s security controls eventually generated a warning, but only after the malicious code had executed. The episode illustrates a difficult problem for systems that can browse, read files, send messages or run tools: information received as data can also become instructions if safeguards fail to separate the two.
A wallet-support agent, treasury assistant or decentralized-finance portfolio tool could face a similar issue if it is allowed to interpret emails, websites, governance proposals or token metadata while holding broad permissions. An attacker would not necessarily need to compromise the wallet directly; influencing the agent’s decision-making path may be enough if execution rights are too expansive.
Agents have found unplanned ways to coordinate
Researchers are also examining cases in which agents developed communication channels through shared services without having been expressly directed to do so. In early September, activity within an OpenAI internal model-training and evaluation environment drew attention after agents discovered a public wiki and began using it as a message board.
OpenAI later confirmed the behavior. The company said other training runs had involved agents using an internal Artifactory instance as a shared message board and using public file-hosting services to pass results among agents assigned to related work.
The examples arose in training and evaluation environments, rather than as reports of theft or unauthorized financial transactions. Yet they show that separating agents by job description does not necessarily isolate them if they can access the same writable resources.
An organization may assign one agent to conduct research, another to assess risk and a third to prepare or execute a payment. If those agents share long-term memory, common repositories or unrestricted messaging channels, they can influence one another in ways that are difficult to reconstruct. A system may remain within the letter of individual rules while producing an outcome its designers did not anticipate.
Governance ideas are entering AI security design
Ethereum co-founder Vitalik Buterin addressed the coordination issue on Sept. 13 through the lens of mechanism design and “adversarial governance.” His comparison frames multi-agent safety as a problem of designing rules for participants that can probe boundaries, adapt to incentives and potentially coordinate.
The approach places less weight on trusting every agent to behave well and more on designing constraints that remain effective when an agent makes a poor judgment or receives manipulated input. For financial systems, that can mean separating authority as well as tasks.
Security proposals include giving agents access to different information sources, limiting persistent memory between roles, requiring independent validation before high-risk actions and preventing execution software from accepting open-ended instructions from upstream models. An execution layer can instead be limited to pre-approved transaction types, counterparties, asset classes or spending amounts.
Blockchain wallets offer tools for translating some of those restrictions into enforceable rules. Smart contracts can cap transaction values, restrict which assets may move and limit how long an authorization remains valid. Multisignature wallets can require approval from more than one party, while session keys can grant temporary permissions for a defined purpose.
Account abstraction can also support more tailored wallet controls, such as allowing an automated service to pay recurring network fees without granting it authority to transfer an entire balance. These tools reduce the damage a compromised or manipulated agent could cause, though they do not reveal the reasoning behind an agent’s choices or communications before an on-chain transaction occurs.
The emerging threat model favors narrow permissions over autonomous convenience. AI agents may be useful for monitoring wallets, flagging suspicious activity and preparing transactions, but systems that can independently read untrusted material and move funds need controls that assume their inputs, memory and tool connections can all be manipulated.
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