According to a report published by The Economic Times, the attacks have primarily targeted smaller NBFCs with annual turnovers of approximately ₹1,000 crore or less. Several financial institutions have experienced repeated attacks, exposing weaknesses in their cybersecurity infrastructure and payment systems.
In one reported incident, a Delhi-based NBFC allegedly lost ₹7.5 crore in an initial cyberattack, followed by another loss of ₹7.25 crore just days later. Investigators believe that inadequate security controls and insufficient monitoring have made smaller lenders attractive targets for cybercriminals.
Investigations into some of the incidents have identified a Chinese hacking platform known as CyberStrike. Security experts have also raised concerns about the growing use of artificial intelligence-powered toolkits, including ARTEX and CyberStrike, alongside open-source large language models.
These tools are lowering the technical barriers for cybercriminals by automating reconnaissance, vulnerability identification and other stages of cyberattacks. Attackers can use AI-assisted frameworks to analyse targeted systems and identify weaknesses more efficiently.
According to officials familiar with the investigations, attackers typically spend around 10 to 12 days studying an NBFC's payment infrastructure, including transaction processes, whitelisted identification details and application code, before attempting to steal funds.
Experts have also warned that stolen customer information could be exploited for targeted phishing and social engineering campaigns. Scammers equipped with borrowers' names, loan details and lender information may impersonate financial representatives to deceive customers into sharing sensitive information.
India's national cybersecurity agency, the Indian Computer Emergency Response Team (CERT-In), along with CSIRT-Fin, issued a critical advisory on October 9, highlighting cyber threats targeting NBFCs, digital lenders, fintech companies and payment service providers.
The advisory identified vulnerabilities in applications and payment Application Programming Interfaces (APIs) that could allow attackers to steal banking credentials and initiate unauthorised fund transfers through trusted financial infrastructure.
CERT-In also warned that AI-driven attack frameworks have significantly reduced the time between initial system compromise and financial damage, allowing attackers to execute operations within hours rather than days.
Japan Airlines has warned roughly 92,000 customers that hackers accessed their personal information through a third-party reservation platform, in what is turning out to be one of the most critical data security incidents to hit the global aviation industry in recent memory.
The airline said the breach originated at Amadeus IT Group, the Spain-based technology company whose software powers ticketing, check-in, and reservation systems for airlines worldwide. JAL said Amadeus notified it the week before it published its own disclosure notice on October 28, 2026, stating that an unauthorized party had accessed "a portion of customer reservation data" stored within the Amadeus platform.
For the vast majority of affected customers, the exposed information included names, booking reference codes, and flight details. But for approximately 2,000 of those customers, the picture is considerably worse. Hackers also accessed passport numbers, frequent-flyer numbers, email addresses, and phone numbers. No credit card numbers or passwords were compromised, JAL said, because that data was not stored in the same system.
Japan Transocean Air, a JAL affiliate, was swept up in the same incident. The two carriers share infrastructure through the Amadeus platform, which is how the breach spread across both brands without requiring a separate intrusion.
JAL said it would reach out to affected customers individually and urged everyone to stay alert for phishing emails, fraudulent phone calls, and fake JAL websites. The warning carries real weight: when scammers know your name, flight number, and booking code, their messages tend to look far more convincing than a generic blast.
The company said its own internal systems were not touched. That distinction, while technically accurate, offers only limited reassurance. From a customer's perspective, the data was real, the exposure was real, and JAL was the brand they trusted when they handed over their information.
JAL is far from alone in this. The Amadeus breach has since grown into an industry-wide event. Cathay Pacific disclosed that hackers used login credentials assigned to one of its system accounts to access reservation data inside Amadeus, with the intrusion running from August 2023 to September 2026, a window of more than three years. Amadeus reportedly detected the unauthorized access in late September, then reset the compromised credentials, disabled the account, and added monitoring. Airlines were notified between October 20 and October 28.
ANA, another major Japanese carrier, said about 37,800 of its customers were affected, with roughly 1,700 of those having passport details viewed. Cathay Pacific put its figure at approximately 53,000 bookings. Korean Air, Asiana Airlines, Malaysia Airlines, and German leisure carrier Condor have all said they are investigating their exposure. Qantas was still assessing whether its bookings were affected as of early November. Three smaller Japanese regional carriers, Air Do, Solaseed Air, and Starflyer, said they had been notified and were reviewing their records.
No single overall figure covering all airlines has been published, but the numbers keep climbing.
The mechanism behind the breach is straightforward and deeply familiar to anyone who follows enterprise security incidents. At its core, attackers used legitimate credentials to log into a system they were not supposed to access. No elaborate zero-day exploit, no ransomware deployment. Someone had the right username and password, and they used it. Cathay Pacific's disclosure that the credentials were tied to a "system account" suggests the compromised login may not have belonged to a human employee at all, but rather to an automated or service-level account, which tend to receive less routine monitoring than individual user logins.
The timeline raises its own troubling questions. If the unauthorized access began in August 2023 and was not detected until September 2026, attackers had more than three years to move through reservation data at their leisure. It is not yet clear whether that access was continuous or sporadic, or whether the full scope of what was taken is known.
The disclosure lands at a sensitive moment for Japan's cybersecurity posture. The country has seen a string of high-profile incidents over the past two years, and Prime Minister Sanae Takaichi convened a cabinet meeting on the issue, pledging emergency measures, though specifics had not been released at the time of publication.
For passengers, the practical exposure is primarily the risk of targeted fraud. Scammers with access to real booking records can craft fake refund notices or schedule-change alerts that clear most basic suspicion checks. Security researchers consistently advise the same response: access your booking only by typing the airline's official website address directly into your browser, and treat any unsolicited message about your flight as suspicious until verified through official channels.
Amadeus has not issued a detailed public statement about how the breach occurred, its full scope, or what it is doing to prevent a similar incident. As more airlines finish their own assessments, that number is likely to rise.
The research was conducted by Google DeepMind in collaboration with cryptography researchers from the University of California San Diego and Inria.
RSA is a widely used public-key cryptographic algorithm that relies on the mathematical difficulty of factoring large numbers. Although RSA-1024 was once considered sufficiently secure, advances in computing and cryptanalysis have made it unsuitable for protecting sensitive information. Modern systems generally use RSA keys of 2048 bits or longer.
The first major finding focused on digital signature forgery involving the PKCS#1 v1.5 padding format. Padding is additional data inserted into a message before signing to ensure that it meets the required block size. Researchers examined how this protection behaves when an attacker can influence the content of the message being signed.
The team found that when an attacker controls approximately half or more of the signed data block, the padding may no longer provide additional protection against forgery. This weakness is relevant to the PKCS#11 signing interface used by cryptographic devices, including hardware security modules.
To show the improvement, DeepMind's AI agents reproduced an earlier academic signature-forgery attack against an RSA-1024 key. The agents reduced the required computing time from approximately 1,380 CPU core-years to 391 CPU core-years, making the process around 3.5 times more efficient.
Two AI agents reportedly designed, executed and debugged the computation in less than four days, with approximately 20 messages from a human researcher, most of which involved approving their actions.
Researchers also developed a more efficient approach for situations where attackers control a smaller portion of the signed message, such as a 32-byte hash. Their theoretical calculations suggest that the proposed method could be approximately 1,600 times more efficient than the previous best approach for certain RSA-1024 signature-forgery scenarios.
The second major finding concerns RSA key factoring. RSA security depends on the difficulty of breaking down a large number into its original prime factors. The researchers developed an algorithm that could make factoring each additional RSA-1024 key approximately 30 times cheaper on average after an expensive initial computation.
However, this technique remains theoretical and has not yet been implemented. The initial computation would also require substantial resources, meaning the findings do not show an immediate practical attack against deployed encryption systems.
The researchers also highlighted important limitations of the signature-forgery technique. It requires approximately four billion signing queries in advance, does not recover the private key and does not work against devices that internally hash messages before signing.
According to Pushmeet Kohli, Vice President at Google DeepMind and Chief Scientist at Google Cloud, modern cryptographic protocols generally use RSA keys of 2048 bits or larger and remain secure against the demonstrated techniques.
Iris Ohyama, one of Japan's most recognized consumer goods manufacturers, has disclosed that a third-party cloud service used by its online shopping platform was accessed without authorization, potentially exposing personal data belonging to roughly 7.7 million people, including customers who never created an account.
The company, founded in 1971 and headquartered in Sendai, is best known for plastic storage solutions and home appliances sold across Asia, Europe, and North America. Its e-commerce platform, Iris Plaza, caters to millions of customers in Japan and handles a large volume of household product purchases. The scale of the breach puts it among the larger data security incidents Japan has seen this year, in a period when Japanese companies and government agencies have dealt with a string of serious intrusions.
How the Breach Was Detected
On October 28, 2026, Iris Ohyama staff detected suspicious files inside a cloud service that Iris Plaza relies on for its operations. The company did not name the cloud provider or describe the method the attacker used to get in. Within a day, the company suspended access to Iris Plaza and related systems, reported the incident to Japan's Personal Information Protection Commission, and filed a report with police.
A follow-up disclosure on November 5 gave a clearer picture of the scope: up to 5.8 million registered and former members may have had their data accessed, along with up to 1.9 million non-members, meaning people who had completed a guest checkout without registering. The fact that guest buyers are part of the exposure is insurmountable, since many people assume their data is not retained when they shop without creating an account. In Iris Plaza's case, it was.
What Data Was in the Affected System
The range of information potentially accessed varies depending on the type of account. For registered members, exposed fields can include full name, home address, phone number, email address, gender, and date of birth. Account passwords were also stored in the affected environment, though the company said they were encrypted. It did not specify which encryption method was used, leaving questions open about how resistant those passwords are to cracking.
Former members are in a slightly different position: Iris Ohyama said it does not retain passwords for accounts that have been closed, so that group faces a narrower exposure. For non-members who checked out as guests, the data stored was more limited.
One notable reassurance: the company confirmed that credit card information was not stored in the affected cloud environment, sparing customers from the most immediately damaging form of financial exposure.
As of November 5, Iris Ohyama said it had not received any communication from whoever carried out the intrusion, had not identified any perpetrators, and had not seen reports of fraudulent use or financial damage tied to the breach. The investigation into how many records were actually exfiltrated, as opposed to merely accessible, is still ongoing.
What Iris Plaza Customers Should Do Now
The company has urged customers to change their Iris Plaza passwords and, critically, to change passwords on any other service where they used the same credentials. Password reuse across sites remains one of the most common ways a single breach cascades into wider account takeovers. Anyone who shops with the same email and password combination across multiple retail or financial platforms is at elevated risk.
Iris Ohyama also warned customers to be on guard for phishing attempts. With names, addresses, phone numbers, and email addresses potentially in circulation, attackers have enough to craft convincing messages that appear to come from the company. Iris Ohyama said it will not send emails or SMS messages asking for passwords, PINs, authentication codes, or other sensitive information through links, and customers should treat any such request as fraudulent.
A Year of Costly Data Incidents in Japan
The Iris Ohyama breach lands in the middle of a rough stretch for data security in Japan. This year alone, cloud hosting provider Sakura Internet reported a hack affecting up to 1.36 million accounts, telecommunications giant KDDI disclosed a breach tied to a zero-day vulnerability in third-party software that hit around 12 million accounts across shared email platforms, and Japan's Digital Agency acknowledged that a VPN flaw exposed approximately 246,000 government personnel records. The common thread across several of these incidents is reliance on third-party services and cloud environments where access controls or vendor security fell short.
Iris Ohyama's breach fits that pattern. A third-party cloud provider, an intrusion the retailer only caught when suspicious files turned up, and millions of customer records sitting in an environment the company did not fully control. The investigation remains open, and the company has not said when it expects to restore Iris Plaza to service.
The attackers reportedly gained access to the bank's network through the platform of an external service provider.
The incident came to light on November 2, when blockchain investigator ZachXBT reported suspicious withdrawals involving several cryptocurrency exchange accounts. According to the investigator, the attackers had potentially stolen around €118 million and moved the funds through cryptocurrency platforms.
Crédit Mutuel Arkéa, one of France's major cooperative banking groups, officially acknowledged the cyberattack on November 4. The bank confirmed that attackers had gained unauthorized access to its systems and used an account belonging to the institution to initiate fraudulent transactions involving other banking organizations.
In a frequently asked questions section published after the incident, the bank explained that the attackers accessed its network directly through the platform of one of its service providers. However, the financial institution did not disclose the identity of the provider. It also clarified that its online banking subsidiary, Fortuneo, was not involved in the attack.
The attackers reportedly used the bank's own account rather than individual customer accounts to transfer the money. Blockchain investigators also tracked the movement of the stolen funds through cryptocurrency exchanges, where some of the money was reportedly converted into stablecoins such as USDC and USDT.
ZachXBT stated that approximately $30 million had already been laundered by November 3. Some of the stolen funds were reportedly blocked, although the complete amount recovered remains unclear. The bank has not independently confirmed the reported $133 million loss, describing the amount involved as significant while acknowledging that the final figure was still being assessed.
After the discovery, Crédit Mutuel Arkéa temporarily suspended certain operations as a precautionary measure while investigating the incident. The bank subsequently announced that the attack had been contained and that its operations were gradually returning to normal.
The financial institution reassured customers that their deposits and personal assets were not affected by the incident. However, some transfer transactions experienced delays because of the security measures introduced in response to the attack.
Arkéa also ruled out human error as the cause, describing the incident as a sophisticated external attack. It is now working with law enforcement agencies and judicial authorities to determine how the attackers gained access and carried out the fraudulent transactions.
When the Los Angeles Police Department started feeding decades of arrest records into predictive algorithms, it believed it was upgrading how it approached crime. What it actually did, researchers now argue, was automate its existing prejudices.
The growing use of artificial intelligence in law enforcement is raising alarms among criminologists and legal scholars who say these systems absorb the biases already embedded in historical police data and repeat them at scale. Far from offering a neutral, data-driven view of crime, the tools risk entrenching the same inequalities they claim to transcend.
At the core of the problem is how these systems learn. AI tools for predictive policing are trained on past arrest and crime records, which means they begin with a picture of crime shaped by previous policing decisions rather than by crime itself. Areas that received heavier patrol in earlier decades show more recorded incidents. The algorithm reads that density as a future risk signal and directs officers back to those same neighborhoods. More arrests follow. The data is updated. The cycle repeats.
Sarah Brayne, a sociologist at the University of Texas at Austin, spent time inside the LAPD studying how the department used big data systems. In research published in the *American Sociological Review*, she found the department was increasingly using data to predict crime rather than respond to it. The data flowing into those systems was skewed from the start: crimes in public places were more likely to be recorded, and patrol resources were disproportionately concentrated in Black neighborhoods. Once that history was absorbed by the algorithm, Brayne wrote, "the predictions appear impartial." They were not. The models, she concluded, create "a feedback loop in which they not only predict events such as crime or police contact, but also contribute to their future occurrence."
The problem sharpens when that underlying data was produced under conditions of institutional misconduct. Researchers at New York University's AI Now Institute studied 13 jurisdictions across the US and found that in nine of them, police departments had trained predictive systems on records gathered during documented periods of unlawful or biased policing. Chicago is the starkest case. The city's police department was under federal investigation for discriminatory practices at the same moment it adopted a risk-flagging algorithm to identify people likely to be involved in shootings. The residents federal investigators had identified as targets of biased policing overlapped substantially with those the algorithm flagged. The list eventually grew to more than 400,000 names, 56 percent of them Black men.
Chicago shut down the program in 2019 after its own inspector general raised concerns about its effectiveness. Los Angeles scrapped a similar initiative, Operation LASER, the same year. A 2023 analysis of Geolitica, formerly known as PredPol, one of the most widely deployed predictive policing tools in the country, found a success rate below half a percent across more than 23,000 predictions made in Plainfield, New Jersey.
Not everyone in the field believes the technology is fundamentally broken. Dr. Dimitrios Kollias, an associate professor in multimodal artificial intelligence at Queen Mary University of London, argues that modern AI systems do not simply replay their training data. "They can combine learned information in new ways and generate outputs that were not explicitly present in those examples," he told the BBC. He acknowledges dirty data is a genuine concern but argues systems can be tested for unfair outcomes and redesigned when they fail. As those systems grow more autonomous, he adds, ensuring they remain under meaningful human control becomes a priority in its own right.
The consequences of algorithmic bias run well beyond law enforcement. A 2019 study published in the journal Science examined a healthcare AI tool used to allocate additional patient support across US hospitals. The system measured patient need by predicting the cost of their care. Because less was already being spent on Black patients than on equally sick white patients, often due to poorer access to care, the algorithm systematically directed resources toward white patients. When researchers recalibrated it to predict illness directly rather than cost, the share of Black patients selected for extra support would have risen from roughly 18 percent to 47 percent. The fix was straightforward once someone looked for the problem. That is precisely the case researchers make for independent oversight.
Legislators are beginning to respond. In January 2024, seven US Congress members asked the Justice Department to pause grant funding for predictive policing programs until their discriminatory impact could be assessed. A White House policy from March 2024 requires federal agencies to independently test rights-impacting AI tools, though the mandate does not extend to state and local police departments. In Britain, 17 civil society organizations called the same year for a full ban on predictive policing.
Dr. Carolyn McKay, an associate professor in digital justice at the University of Sydney Law School, says cleaning up biased data is not a matter of removing a few stray errors. The corrupted records are not sitting at the edges of an otherwise sound dataset. They run through the foundation the algorithm was built on. Even systems redesigned to flag locations rather than individual people can produce the same outcomes, she warns, because the neighborhoods they target were shaped by years of concentrated policing long before any algorithm got involved.
The root of the problem, most researchers agree, is not the machine. It is the human judgment baked into the data the machine inherited. The algorithm did not decide what suspicious behavior looked like, or which crimes were worth recording. People did. Until those decisions are independently examined, AI will keep reflecting them back.
The vulnerabilities, tracked as CVE-2026-116606 and CVE-2026-111560, affect Barracuda's email-filtering appliance and could allow remote attackers to bypass security restrictions and gain complete control of vulnerable systems.
According to cybersecurity firm watchTowr, which published its technical analysis, the attack chain combines an authorization bypass flaw with a command injection vulnerability. Barracuda released security patches for the issues earlier this week.
The first vulnerability, CVE-2026-116606, is an authorization bypass flaw that allows unauthenticated attackers to access a configuration restoration function and modify the appliance's global settings. The second, CVE-2026-111560, is a command injection vulnerability that enables commands to execute with root privileges.
Mandiant assigned CVSS severity scores of 9.3 and 8.6 to the two vulnerabilities, respectively.
In its technical investigation, watchTowr discovered that the authorization bypass stems from the appliance's password-change process. When a password change request fails, the system incorrectly returns a low-privilege user role instead of terminating the request completely.
The researchers also found that Barracuda's backup verification mechanism relies on a secret embedded within the appliance's code. Since this secret is not securely protected, attackers can generate a valid checksum for a specially crafted configuration backup and restore it on the targeted device.
The uploaded configuration can contain a manipulated timezone value. When the appliance processes this setting, it passes the value to a shell command that executes with root privileges.
watchTowr demonstrated the attack by successfully writing a file to the appliance with root-level permissions.
CVE-2026-116606 affects Barracuda Email Security Gateway, while CVE-2026-111560 impacts five additional products, including Load Balancer Application Delivery Controller (ADC), Message Archiver, WAF Control Center, Web Application Firewall (WAF), and Web Security Gateway (WSG).
However, the complete unauthenticated attack chain applies specifically to Email Security Gateway. Exploitation of the command injection vulnerability on the other affected products requires administrator access.
Barracuda has released security updates addressing both vulnerabilities and recommends that customers install the appropriate patches or upgrade to the latest fixed firmware versions.
As of the disclosure, neither vulnerability had been listed in CISA's Known Exploited Vulnerabilities (KEV) catalog, and no publicly reported incidents linked the flaws to active exploitation.