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Alphabet, Tesla Shares Slide as Wall Street Questions Mounting AI Investment Costs

  Investors wiped billions from the market value of Alphabet and Tesla after the companies disclosed another sharp increase in spending tie...

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Technology
Economic crisis Financial Crash AI Stocks Ray Dalio Technology

Ray Dalio Warns AI Bubble Could Trigger Financial Crash

 

Billionaire investor Ray Dalio, who famously predicted the 2008 financial crisis, is now warning that the artificial intelligence boom could burst and trigger a similar economic collapse. The founder of Bridgewater Associates says investors are confusing AI’s transformative potential with guaranteed investment returns, creating dangerous market conditions. 

Dalio explains that bubbles form when prices rise dramatically as everyone rushes to invest, often borrowing money to participate. He notes the current AI euphoria has reached approximately 75-80% of the extremes seen before the 1929 crash and the 2000 dot-com bubble. The problem, according to Dalio, is that people stop paying attention to whether prices make sense because they fear missing out. He emphasizes a crucial distinction: “This will change the world” does not mean “this investment can’t lose money.” When wealth holders need cash for taxes, debt payments, or other obligations, they must sell assets, triggering a cascade where falling prices force more selling. 

Historical parallels and warning signs 

The 76-year-old investor draws direct parallels to previous bubbles, particularly the dot-com era when investors assumed internet companies were sure bets. Many borrowed heavily to invest, only to lose everything when the bubble burst. Dalio warns AI stocks could drop as much as 80% even if the technology succeeds in revolutionizing industries. He points out that during the dot-com boom, the internet genuinely transformed society, but most early internet companies still collapsed because valuations were unsustainable. The same pattern could repeat with AI, where the technology delivers on its promises but overvalued companies fail to generate adequate profits.  

Beyond the AI bubble, Dalio warns that the broader debt situation has passed a “point of no return.” When debt service payments consume so much income that they squeeze out spending, economic contraction becomes inevitable. He describes this as similar to plaque in arteries restricting blood flow—eventually, the system seizes up. Combined with potential Federal Reserve policy shifts, rising interest rates, or wealth taxes, these factors could prick the AI bubble and trigger widespread margin calls. Dalio also highlights geopolitical tensions that could lead to a “capital war,” where foreign investors reduce bond purchases, making borrowing more expensive and drying up the capital fueling AI investments. 

Preparing for what comes next 

Dalio stresses that understanding these cause-and-effect relationships is essential for navigating what lies ahead. He advocates for diversified portfolios including gold and other assets that perform well during debt crises. While AI will bring revolutionary changes to productivity, drug discovery, and logistics, investors must separate technological success from investment success. The key lesson from history is that bubbles always burst, and those who recognize the signs early can protect their wealth while others face devastating losses.
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data security
Credential Stuffing Credential Stuffing Attacks Credential Theft Customer Data Customer Data Theft Data Breach Data Leak data security

Chick-fil-A Warns Customers After Credential Stuffing Attack Compromises User Accounts

 

Chick-fil-A notifies customer about personal information exposure after data breach occurred due to credential stuffing attack Chick-fil-A company has announced that personal and account information about some of its customers may have been exposed due to a data breach. This breach occurred through the use of credential stuffing, which is not a vulnerability within the corporation’s website or mobile application.

As explained in the company note to customers, unauthorized access attempts came from bad actors using credentials stolen elsewhere. The company discovered unauthorized access attempts to customer accounts after noticing anomalous activity in the login database, and the phishing campaign occurred between June 17-19, 2026, targeting Chick-fil-A One loyalty program accounts. The corporation concluded its investigation on July 13 th and established that attackers had used compromised credentials to access the account information of some customers. 

The information available to bad actors and potentially at risk of being misused varies depending on the customer’s account. It may include names, contact information, mailing addresses, phone numbers, dates of birth, and Chick-fil-A One account information like ID or QR code and mobile payment credentials. Moreover, attackers may have gained access to reward balances, gift card balances, and the last four digits of payment cards. Although the corporation has not revealed the number of affected clients, the number exceeds several thousand. 

According to the documents filed with the state, 2,182 Texas residents and 39 Massachusetts residents were impacted by the breach. However, there are also other states affected, as notifications to state attorney generals in charge of consumer protection have also been filed, including the District of Columbia. After discovering the issue, the corporation remediated the security risks and notified the affected clients. 

Moreover, Chick-fil-A took measures to enhance account security for all customers, including allowing password reset, account logout, and removing payment methods in the application. Some customers also received bonus points on their accounts as compensation for the issues experienced. Chick-fil-A corporation acknowledges the concern caused by the data breach and assures clients that it takes customer account security seriously. Moreover, the company has recommended that customers change passwords to strong and unique words or phrases not used for other accounts. 

Credential stuffing works only when the same or similar passwords are used across different accounts, so changing them to unique ones decreases the chances of experiencing another breach. Chick-fil-A data breach demonstrates once more that it is crucial to make sure that each online account, including email, banking, and social media accounts, uses a unique and strong password. 

If one suspects that an account may have been compromised, it should be changed to a strong password immediately. Also, it is essential to use multi-factor authentication when available and to monitor account activity regularly for unauthorized transactions or unauthorized access attempts.
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Rat Trojan
Alibaba malware NPM Organization Security Rat Trojan

Malicious NPM Packages Attack Alibaba Users and Companies


Cybersecurity experts have found a new set of harmful npm packages that attack users of Alibaba developer tools via cross-platform RAT (Remote Access Trojan). This was part of an advanced, specific software supply chain attack against Chinese-speaking environments.

About the packages

Lib-mtop is an unscoped package with the same name as the private Alibaba package as @ali scope. Experts have not confirmed if this was due to the project developer going rogue or takeover of the maintainer account.

“Ch4ce,” the same maintainer account which presently redirects to a ‘not found’ error on npmjs[.]com also posted four other packages: local-config-parser, aone-kit-cli, aone-kit, and aone-sandbox. Three of these are empty wrappers carrying the same name as private, @ali-scoped packages, “which they declare as a dependency in the package.json file,” said Socket security researcher.

Attack tactic

The local-confi-parser package uses a genuine JSON configuration file parser, but shows dependencies that are posted from other npm user accounts. Together, they provide a channel for an advanced RAT attacking developers who may be working in organizations related with the Alibaba group.

Particularly, the infected loader functionality is divided and deployed into various packages sent to the victims. "When such a package is installed in an environment that has access to impersonated, scoped private packages, the dependency resolution works as expected, with a little extra functionality delivered through additional dependencies that get installed," Socket said.

Experts found 10 top-layer lure packages that depend on “smart-config-manager,” which works similar to a middle-layer bridge that links them to harmful payloads consisting of the loader logic. A low layer package continues to reach out to a GitHub repository to extract and store a rule engine configuration for use to run a malicious payload and contacts a remote server for fetching secondary malware.

What sets this attack apart?

A unique thing about the campaign is that the rule engine uses the vm module to implement the last phase and run the payload according to the target’s OS. The payload is fetched from a domain that mimics Alibaba to look natural and escape detection. 

The final payload is an advanced backdoor integrated with arbitrary file upload and download, comprehensive command execution, payload staging, lateral movement functions and host reconnaissance. The payload can also inject infected code into enterprise apps like Qoder, DingTalk, and Wukong.

"The goal of the campaign seems to be industrial espionage. While the number of downloads for the malicious packages is not significant, the impact of the campaign is hard to evaluate, because of the targeted nature and lateral-spread capabilities of the final-stage payload,” Socket said.  

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Workforce Transformation
AI Automation AI Hiring Trends Artificial Intelligence Entry-Level Jobs Future Of Work Human Resources Technology Workforce Transformation

AI Threatens Entry-Level Jobs as Automation Accelerates Across Industries


 

As artificial intelligence rapidly transforms the global workforce, new research suggests that entry-level positions in technology, finance, customer service, and creative industries are especially vulnerable to automation. A recent analysis by the BBC indicates that advances in large language models (LLMs) have enabled AI to perform previously difficult tasks.

Initially, artificial intelligence systems were limited to performing simple tasks in a matter of minutes. However, nowadays, the latest models are capable of performing complex tasks that require skilled professionals several hours to complete, especially in software development, financial analysis, legal research, and content development. 

As indicated by a recent Gartner survey, AI has already made significant contributions to workforce planning. According to a survey conducted by 110 chief human resources officers (CHROs), 22% of those HR leaders claimed at least one business leader at their organization had stopped hiring entry-level employees as a result of artificial intelligence automation. A study also found that 95% of organizations have implemented some form of artificial intelligence in the last year, although only one in five said the investments have generated significant or transformational business value. 

AI benchmarks have shown a sharp increase in performance over the past three years. The new generation models, released in 2026, have the ability to complete much larger coding and analytical tasks than earlier systems, which raises concerns about their increasing impact on white-collar jobs. Stanford University research indicates that young professionals have already felt the effects of AI. 

Researchers found that the prevalence of ChatGPT and similar AI tools has decreased employment among workers aged 22 to 25 by 2.7%. According to Gartner, most organizations are currently using artificial intelligence (AI) to automate or augment routine, low-complexity tasks traditionally performed by junior employees in sectors with the highest exposure to artificial intelligence (AI), including software, finance, and creative professions. 

In response to the automation of these responsibilities, companies are reassessing entry-level roles, creating an increasing gap between new graduates' skills and increasingly complex jobs for human workers. Despite some economists arguing that other factors such as interest rates and a slowdown in hiring have also contributed to a weaker economy, AI is becoming increasingly recognized as a key factor in workforce disruption.

In a separate study conducted by the Organization for Economic Cooperation and Development (OECD), job postings in occupations highly exposed to artificial intelligence (AI) have also decreased significantly compared to occupations which require physical work. Additionally, businesses are increasing their investments in artificial intelligence-based "agents" capable of performing repetitive and specialized tasks simultaneously. 

There has been a dramatic increase in the use of Artificial Intelligence measured by trillions of text processing tokens as companies encourage their employees to maximize productivity through artificial intelligence. The soaring operational costs have led some organizations to limit AI deployment, which suggests economic constraints may still prevent widespread automation from occurring. 

Adapting lower-cost artificial intelligence models, including open-source alternatives originating from China, has also become a trend that enables organizations to utilize artificial intelligence while reducing operating expenses. The firm warns that reducing graduate recruitment could result in long-term talent shortages by limiting opportunities for developing future skilled professionals internally. Even though the shift toward automation is occurring, Gartner warns against eliminating early-career hiring altogether. 

According to Gartner, entry-level positions should be redesigned to focus on higher-value responsibilities, mentorship and team support should be strengthened, and employees should be provided with adaptive skills to work effectively with AI. In many cases, human-AI collaboration is expected to result in the evolution of many jobs rather than eliminating entire professions. Moreover, Gartner recommends organizations to move beyond traditional training methods by emphasizing business judgment, versatility, and hands-on learning as a means of preparing employees for increasingly AI-enabled workplaces. 

In spite of this, economists warn policymakers and businesses that they must act rapidly to equip workers with new skills and ensure technology increases productivity without displacing large numbers of workers. The growth of AI across industries poses a challenge to businesses seeking to balance automation with workforce development. Experts believe that the building of a resilient workforce for the future will require investments in skills, redesign of entry-level roles, and fostering human-AI collaboration.
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Hugging Face
AI Attack API Credentials Data Breach Hugging Face

Hugging Face Breach Raises Concerns Over AI-Driven Attacks

 



Hugging Face is investigating a security incident after its production infrastructure was compromised in an intrusion the company says involved an autonomous AI agent, raising fresh concerns about how artificial intelligence could reshape offensive cyber operations.

In a security disclosure published on July 16, the open-source AI platform said the attack leveraged an autonomous agent framework built on top of an agentic security research environment powered by a large language model (LLM). According to the company, the system executed thousands of actions across multiple sandboxed environments, allowing the attackers to move through internal infrastructure and obtain unauthorized access to datasets and service credentials.

The company said the intrusion began when a malicious dataset exploited two separate code execution paths on a processing worker. After establishing an initial foothold, the attacker reportedly escalated privileges to node-level access before collecting cloud and cluster credentials and moving laterally into several internal clusters.

Hugging Face has not yet confirmed whether customer or partner information was affected and said its investigation remains ongoing.

The incident has attracted attention across the cybersecurity community because it suggests that AI systems may now be capable of carrying out increasingly complex intrusion workflows with limited human intervention. Unlike traditional automated malware or scripts that perform predefined tasks, autonomous AI agents can adapt to changing environments, plan sequences of actions and make decisions throughout an attack.

Researchers have long warned that advances in generative AI could lower the barrier for sophisticated cyberattacks by accelerating vulnerability discovery, reconnaissance, privilege escalation and post-compromise activities. While many of these scenarios have remained largely theoretical, Hugging Face's disclosure indicates that elements of these capabilities may already be appearing in real-world operations.

According to the company's investigation, the attacking system generated thousands of individual actions during the compromise, demonstrating a level of operational scale that would normally require substantial manual effort.

Hugging Face co-founder and CEO Clément Delangue said the incident reinforces the view that threat actors are already adopting AI agents in offensive operations. He also argued that restricting advanced AI models behind commercial APIs alone is unlikely to prevent misuse because determined attackers can often circumvent safety controls, while defenders may lose valuable access to tools needed for security research and incident response.

The company encountered another challenge during its investigation when content moderation mechanisms on a frontier AI model reportedly prevented analysts from processing portions of the attack data. To continue the forensic investigation, the security team instead relied on GLM-5.2, an open-weight language model that was deployed within Hugging Face's own infrastructure.

Using the model, investigators reconstructed the attack timeline, identified indicators of compromise, mapped affected credentials and accelerated forensic analysis that would otherwise have required significantly more manual effort. The company also revoked compromised credentials, rotated authentication tokens and remediated the exploited vulnerability.

Security researchers say the incident highlights both the opportunities and limitations of AI-assisted security operations. While AI can substantially reduce investigation time by processing large volumes of telemetry, organizations may encounter operational constraints if externally hosted models refuse to analyze sensitive security artifacts because of built-in safety guardrails.

Industry experts increasingly argue that enterprises should maintain trusted self-hosted AI models that can support internal incident response without exposing sensitive forensic data to external services.

The disclosure comes amid bigger concerns about the growing availability of permissive AI models that operate with fewer content restrictions. Recent threat intelligence research has identified thousands of publicly accessible models advertised as uncensored or unrestricted, raising concerns that malicious actors have expanding access to AI systems capable of assisting offensive cyber activities.

Cybersecurity professionals caution that AI is changing the economics of cybercrime by enabling attackers to automate portions of reconnaissance, exploitation, credential harvesting and post-compromise operations. As these technologies continue to mature, sophisticated attack capabilities may become accessible to a broader range of threat actors.

For defenders, the incident reinforces the need to integrate AI into security operations rather than relying solely on conventional manual workflows. AI-assisted detection, forensic analysis and incident response are increasingly becoming essential capabilities as organizations attempt to match the speed and scale of modern attacks.

Although the investigation into the Hugging Face breach remains ongoing, the incident serves as another indication that autonomous AI systems are beginning to influence both offensive and defensive cybersecurity strategies. As organizations continue adopting AI throughout their technology environments, security teams will need to prepare for a future in which machine-speed attacks are met with equally intelligent defensive capabilities.

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information management
AI adoption AI Data Governance AI governance AI technology Cyber Security Data Governance information management

AI Adoption Shifts Focus Toward Data Governance and Enterprise Trust

 

The rise of artificial intelligence (AI) is uncovering vulnerabilities in enterprise data governance, as organizations grapple with managing information rather than applications and users. As companies rely on AI to analyze, create, research, and make decisions using enterprise data, experts say data governance is becoming a priority. 

According to industry research, over 50% of employees are already using AI outside of corporate systems, raising concerns about shadow AI. However, experts say the bigger issue is understanding how enterprise information is being used, accessed, and processed by employees and systems. AI is fundamentally changing the value of enterprise data as it empowers organizations to analyze, summarize, and act on information instantly. Documents that previously required human analysis can now be processed by AI to extract business intelligence in seconds. 

This makes enterprise information more valuable than ever before as it becomes embedded in decision-making processes and systems. As the value of enterprise data increases, so does the need to ensure its context is appropriately maintained. Experts say that business documents, customer data, intellectual property, and presentations have value and meaning based on their intended use. 

As this information is shared internally and externally and processed by AI, policies, accountability, and governance must be attached to the data to ensure it is used as intended. Security professionals say information governance should be connected to the data itself rather than where that information is stored. They recommend that policies, procedures, and enforcement be attached to the information to ensure its proper use in an increasingly distributed and AI-driven enterprise. 

Trust is quickly becoming a critical success factor for organizations that want to maximize the value of AI while minimizing risk. Business leaders, regulators, and customers are demanding more excellent transparency, which puts pressure on enterprises to ensure sensitive information is handled responsibly. Experts say organizations that get governance right will be best positioned to adopt AI while maintaining the trust of their stakeholders. 

The next wave of AI innovation will include autonomous agents that can access and retrieve information, coordinate tasks, make recommendations, and take action across enterprise systems. These AI agents will require access to data, which means organizations must have robust information governance practices to ensure the data being processed is accurate and secure. 

As the capabilities of AI continue to evolve, enterprises are focusing on ensuring the information that fuels these systems is governed appropriately. Experts recommend organizations prioritize information governance, maintain the context of enterprise data, and leverage trusted AI to maximize the value of their data assets.
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