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Major US banks are tightening restrictions on third-party artificial intelligence agents that attempt to access customer accounts or carry out financial transactions, highlighting the challenges of introducing autonomous AI into highly regulated financial systems. The reported measures involve institutions including JPMorgan Chase, Morgan Stanley, Bank of America, Wells Fargo and Citigroup.

The move follows reports that some AI agents accessed banking platforms using customer-provided login credentials, with certain systems reportedly attempting actions that appeared to go beyond users’ instructions. Banks are concerned about the risks associated with granting external software access to sensitive financial information and the ability to initiate transactions. However, the reports do not establish widespread financial losses caused by these agents.

Financial institutions are responding by blocking or restricting access from certain third-party agents. The development raises a broader question for the financial sector: how can banks allow customers to benefit from AI automation without compromising security, accountability or control over their money?

From AI Assistance to Financial Action

AI systems have traditionally been used to answer questions, analyse information and generate recommendations. AI agents extend these capabilities by performing tasks across digital platforms, potentially navigating websites, comparing services and executing actions with limited human intervention.

In banking, this could mean reviewing spending patterns, identifying better savings rates, paying bills or moving eligible funds between accounts. Tasks that once required customers to navigate multiple applications could eventually be handled through a single AI assistant.

The difficulty arises when an agent moves from recommending a financial decision to executing it.

A system might misunderstand a user’s request, encounter misleading information or attempt a transaction without adequately confirming the intended outcome. Automated access can also create difficulties for banks trying to distinguish legitimate customer-directed activity from activity that exceeds the permissions granted.

JPMorgan Chase CEO Jamie Dimon has warned that unrestricted access by external agents creates risks for both customers and the bank. Morgan Stanley has also restricted third-party agents, although reporting indicated that it had not observed rogue AI agents accessing its platforms.

These developments suggest that banks are not necessarily rejecting AI itself. Rather, they are questioning whether external systems should receive direct access to financial accounts without controls that banks can verify and enforce.

Why Customer Deposits Are Part of the Debate

The implications extend beyond cybersecurity to the economics of banking.

An estimate attributed to Apollo chief economist Torsten Slok places the combined pool of non-interest-bearing deposits at JPMorgan Chase, Bank of America and Wells Fargo at approximately $1.6 trillion. A related scenario suggests that intensified competition for these deposits could increase annual interest expenses by around $47 billion.

These figures should be treated as estimates of potential exposure, not confirmed losses resulting from the reported AI activity. The amounts depend on assumptions about how customers might change their banking behaviour as automated financial tools become more widely used.

The underlying concern is nevertheless important.

Banks use customer deposits as a source of funding for lending and other financial activities. Deposits that pay little or no interest can be particularly valuable because they generally cost less to maintain than interest-bearing alternatives, although they still involve operational and other expenses.

AI agents could make it easier for customers to compare savings products, identify higher returns and move eligible balances between institutions. Instead of researching rates and completing several steps manually, customers could eventually delegate much of that process to an automated assistant.

If such behaviour becomes widespread, banks may face greater pressure to offer competitive deposit rates or improve their services to retain customers.

This would not necessarily produce an immediate or uniform outflow of deposits. Customers consider several factors when choosing a bank, including convenience, payment services, account requirements, security and the benefits of maintaining an established relationship. Nevertheless, automation could reduce some of the effort traditionally involved in switching providers.

The Difference Between Access and Authority

One of the central technical issues is the distinction between authentication and authorisation.

Authentication establishes who is accessing an account. Authorisation determines which actions that user or system is permitted to perform.

An AI agent might successfully sign in using credentials provided by a customer. That does not automatically mean it should be able to change account settings, initiate transfers or make payments without additional checks.

Many agents rely on browser automation to interact with websites designed primarily for human users. They interpret page layouts, identify buttons and navigate interfaces. These methods can be useful for routine tasks, but they introduce uncertainty when applied to financial transactions.

A page may change unexpectedly, a session may expire or an agent may misinterpret a confirmation message. Malicious instructions embedded in online content can also attempt to manipulate an AI system, a risk commonly known as prompt injection.

These possibilities do not mean that all agents are unreliable. They demonstrate why financial institutions need safeguards that operate independently of the AI model.

A more secure approach could involve approved application programming interfaces (APIs), restricted permissions, transaction limits and additional confirmation for sensitive actions. Such systems could also maintain audit records and provide a mechanism for revoking access.

The principle is straightforward: an AI agent should only be able to perform actions that have been explicitly authorised, within clearly defined limits.

Banks Could Lose Control of the Digital Customer Experience

There is also a competitive dimension to the issue.

Banking applications have traditionally served as the main point of contact between financial institutions and their customers. Through these platforms, banks present account information, promote products and allow users to manage their finances.

AI agents could change how customers interact with these services.

A customer might ask an assistant to maintain a minimum account balance, compare deposit rates and identify where surplus funds could earn better returns. The agent could then present options or prepare transactions for approval without requiring the customer to visit several banking applications.

In this model, the AI provider could increasingly control how customers discover and compare financial products, while banks continue to provide the underlying services.

That shift could increase competition and make financial products easier to evaluate. It could also reduce opportunities for banks to retain customers simply because changing providers requires time and effort.

However, it would be premature to conclude that protecting commercial interests is the primary reason for the reported restrictions. Banks have clear security and compliance responsibilities, and the immediate concerns involve account access, transaction authorisation and customer protection.

The longer-term competitive consequences remain a separate issue.

A Possible Middle Ground: Read-Only Access

One potential path towards wider adoption is to distinguish between viewing financial information and controlling money.

Read-only access could allow an AI agent to categorise spending, identify recurring payments, summarise account activity and compare financial products without being able to transfer funds.

If the system identifies a potentially beneficial action, it could explain the recommendation and request customer approval before proceeding.

More advanced arrangements could introduce temporary permissions, transaction-specific authorisation, spending limits and additional checks for unusual activity. These controls would need to be enforced by the banking infrastructure rather than relying entirely on the agent’s ability to follow instructions.

Such an approach could preserve much of the convenience of AI automation while reducing the risks associated with unrestricted access.

The challenge will be designing safeguards that are strong enough to protect customers without making automated financial services unnecessarily difficult to use.

What Comes Next for Agentic AI?

The banking industry’s response highlights a broader transition in AI development. As systems begin performing consequential tasks, their value will depend not only on their ability to complete requests but also on whether their actions are secure, transparent and accountable.

For AI developers, this means improving permission management, error handling and the ability to verify completed actions. For banks, it means establishing reliable ways to distinguish approved agents from unauthorised access and determining who bears responsibility when an automated action causes harm.

Customers could benefit from easier financial management and more efficient comparisons between products. However, those benefits will depend on meaningful consent, clear limits on delegated authority and effective protections against errors or misuse.

Similar questions are likely to arise in online shopping, insurance, healthcare administration and business software wherever AI systems gain the ability to act on behalf of users.

The reported restrictions therefore represent more than a dispute over website access. They reflect the challenge of integrating autonomous software into systems where mistakes can have direct financial consequences.

The next stage of AI adoption will depend not simply on how much an agent can do, but on whether institutions can safely control what it is allowed to do.

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Meta

Meta has announced that the end-to-end encryption feature for messages on Instagram will stop functioning after May 8, 2026. The change means that messages exchanged on the platform will no longer be protected by this form of encryption.

End-to-end encryption is designed to ensure that only the sender and recipient can read the contents of a message. When this protection is removed, platform operators may be able to access or analyse the content of communications under certain circumstances.

Meta confirmed the upcoming change through a blog post explaining that some existing chats may be affected once the feature is discontinued. Users who wish to keep messages or media stored in encrypted chats have been advised to download their data before the change takes effect.

According to the company, users will receive instructions inside the app on how to download messages and media files that they want to retain. The company also advised people using older versions of the application to update to the latest version so they can access the download tools before the feature is removed.

The decision comes amid increasing global concerns about harmful online content, particularly material related to child exploitation. Removing end-to-end encryption could allow platforms to scan messages and calls for content linked to child sexual abuse material, grooming, or harassment.

Social media platforms have faced growing pressure from governments and regulators to strengthen protections for younger users online. Several countries have proposed or introduced regulations aimed at requiring technology companies to detect harmful content even within private communications.

In recent years, governments in regions such as Europe and Australia have discussed policies aimed at limiting social media access for children under certain age thresholds. Some proposed laws would also give authorities greater powers to require platforms to identify illegal content online.

Instagram remains one of the most widely used social media platforms among younger audiences worldwide. The platform’s messaging system is frequently used for private communication between users, making privacy protections a key area of debate.

Encrypted messaging on Instagram was introduced in late 2023 as part of Meta’s broader efforts to strengthen privacy features across its platforms. However, the upcoming change signals a shift in the company’s approach as it balances privacy protections with efforts to detect and prevent harmful content.

The move has generated discussion among technology experts and privacy advocates. Many security specialists consider end-to-end encryption one of the most effective ways to protect digital communications from hackers or unauthorised surveillance.

At the same time, some policymakers argue that strong encryption can make it more difficult to identify illegal activities occurring within private messaging systems.

As the May 2026 deadline approaches, users and technology analysts will be watching how the change affects communication privacy on Instagram and whether similar policy discussions extend to other platforms owned by Meta.

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prompt flux malware

Google’s Threat Intelligence Group (GTIG) has identified an experimental malware family known as PROMPTFLUX — a strain that doesn’t just execute malicious code, but rewrites itself using artificial intelligence.

Unlike traditional malware that depends on static commands or fixed scripts, PROMPTFLUX interacts directly with Google Gemini’s API to generate new behaviours on demand, effectively creating a shape-shifting digital predator capable of evading conventional detection methods.

A Glimpse into Adaptive Malware

PROMPTFLUX represents a major shift in how attackers use technology. Instead of pre-coded evasion routines, this malware dynamically queries AI models like Gemini for what GTIG calls “just-in-time obfuscation.” In simpler terms, it asks the AI to rewrite parts of its own code whenever needed — ensuring no two executions look alike.

This makes traditional, signature-based antivirus systems nearly powerless, as the malware continuously changes its fingerprint, adapting in real time to avoid detection.

How PROMPTFLUX Operates

The malware reportedly uses Gemini’s capabilities to generate new scripts or modify existing ones mid-operation. These scripts can alter function names, encrypt variables, or disguise malicious payloads — all without human intervention.

GTIG researchers observed that PROMPTFLUX’s architecture allows it to:

  • Request on-demand functions through AI queries
  • Generate obfuscated versions of itself in real time
  • Adapt its attack vectors based on environmental responses

While still in developmental stages with limited API access, the discovery underscores how AI can be weaponised in cybercrime ecosystems.

Google’s Containment and Response

Google has moved swiftly to disable the assets and API keys associated with the PROMPTFLUX operation. According to GTIG, there is no evidence of successful attacks or widespread compromise yet. However, the incident stands as a stark warning — attackers are now experimenting with semi-autonomous, AI-driven code.

The investigation revealed that the PROMPTFLUX samples found so far contain incomplete functions, hinting that hackers are still refining the approach. But even as a prototype, it highlights the growing intersection of machine learning and malicious automation.

A Growing Underground AI Market

Experts warn that PROMPTFLUX is just the beginning. A shadow economy of illicit AI tools is emerging, allowing less-skilled cybercriminals to leverage AI for advanced attacks. Underground forums are now offering AI-powered reconnaissance scripts, phishing generators, and payload enhancers.

State-linked groups from North Korea, Iran, and China have reportedly begun experimenting with similar techniques — using AI to streamline reconnaissance, automate social engineering, and even mimic human operators in digital intrusions.

Defenders Turn to AI Too

The cybersecurity battle is no longer human versus human — it’s AI versus AI. Defenders are now deploying machine learning frameworks like “Big Sleep” to identify anomalies, reverse-engineer adaptive code, and trace AI-generated obfuscation patterns.

Security teams are being urged to:

  • Prioritize behaviour-based detection over static signature scans
  • Monitor API usage patterns for suspicious model interactions
  • Secure developer credentials and automation pipelines against misuse
  • Invest in AI-driven defensive frameworks that can predict evasive tactics

The Future: Cybersecurity in the Age of Adaptive Intelligence

PROMPTFLUX marks the early stage of a new class of cyber threats — self-evolving malware. As AI becomes more integrated into both legitimate development and malicious innovation, defenders must evolve just as quickly.

The next generation of cybersecurity will depend not only on firewalls and encryption but on the ability to detect intent — to distinguish between machine creativity and machine deception.

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