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Cyber Security
AI coding tools AI Cybersecurity Threats AI Risks AI technology Cyber risks Cyber Security

AI Coding Assistants Expose New Cyber Risks, Undermining Endpoint Security Defenses

 

Not everyone realizes how much artificial intelligence shapes online safety today - yet studies now indicate it might be eroding essential protection layers. At the RSAC 2026 gathering in San Francisco, insights came sharply into focus when Oded Vanunu spoke; he holds a top tech role at Check Point Software. 

His message? Tools using AI to help write code could actually open doors to fresh risks on user devices. Not everything about coding assistants runs smoothly, Vanunu pointed out during his talk. Tools like Claude Code, OpenAI Codex, and Google Gemini carry hidden flaws despite their popularity. Though they speed up work for programmers, deeper issues emerge beneath the surface. Security measures that have stood firm for years now face quiet circumvention. 

What looks like progress might also open backdoors by design. Despite gains in digital protection during recent years - tools like real-time threat tracking, isolated testing environments, and internet-hosted setups have made devices safer - an unforeseen setback is emerging. Artificial intelligence helpers used in software creation now demand broad entry into internal machines, setup records, along with connection points. Since coders routinely allow full control, unseen doors open. 

These openings can be used by hostile actors aiming to infiltrate. Progress, it turns out, sometimes carries hidden trade-offs. Now under pressure from AI agents wielding elevated access, Vanunu likened today’s endpoints to a once-solid fortress. These tools, automating actions while interfacing deeply with system settings, slip past conventional defenses unable to track such dynamic activity. 

A blind spot forms - silent, unnoticed - where malicious actors quietly move in. One key issue identified in the study involves the exploitation of config files like .json, .env, or .toml. While not seen as harmful by many, such file types typically escape scrutiny during security checks. Hidden within them, hostile code might reside - quietly waiting. Because systems frequently treat these documents as safe, automated processes, including AI-driven ones, could run embedded commands without raising alarms. 

This opens a path for intrusion that skips conventional virus-like components. Unexpected weaknesses emerged within AI coding systems, revealing gaps like flawed command handling. Some platforms allowed unauthorized operations by sidestepping permission checks. Running dangerous instructions became possible without clear user agreement in certain scenarios. Previously accepted tasks were altered silently, inserting harmful elements later. Remote activation of external code exposed further exposure points. 

Approval processes failed under manipulated inputs during testing. Even after fixing these flaws, one truth stands clear - security boundaries keep changing because of artificial intelligence. Tools meant to help coders do their jobs now open new doors for those aiming to break in. What once focused on systems has moved toward everyday software assistants. Fixing old problems does not stop newer risks from emerging through trusted workflows. 

Starting fresh each time matters when checking every AI tool currently running. One way forward involves separating code helpers into locked-down spaces where they can’t reach sensitive systems. Configuration files deserve just as much attention as programs that run directly. With more companies using artificial intelligence, old-style defenses might no longer fit the real dangers appearing now.
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Cyber Security
Access control Access Controls Account customer complaints Customer Service Issues Cyber Outage Cyber Security

X Faces Global Outage Twice in Hours, Thousands of Users Report Access Issues

 

Hours apart, fresh disruptions hit X - once called Twitter - as glitches blocked entry for countless people across regions. Though brief, these lapses fuel unease over stability under Musk’s control, following a trail of prior breakdowns just lately. A pattern forms without needing bold claims: service falters too often now. 

Early afternoon saw service disruptions start across the U.S., per Downdetector figures, hitting a high point near 3:50 PM EST with about 25,000 affected individuals. Later that evening, roughly at 8:00 PM EST, another wave emerged - over 6,000 people then faced login difficulties. 

Problems surfaced across multiple areas, according to user feedback. Close to fifty percent struggled just to open the app on their phones. Some saw broken features within the feed or site navigation failing mid-use. Interruptions popped up globally - not confined by borders - hitting people in both UK cities and Indian towns alike. 

Fewer incidents appeared out of India at first, yet the next wave brought a clear rise - more than six hundred alerts came through by dawn. That same split trend showed up elsewhere, too: data from StatusGator backed the idea of two separate waves hitting at different times. 

Even though the problem spread widely, X stayed silent on what triggered it. Still, users asking about glitches got answers from Grok, its built-in chat assistant. A hiccup in systems stopped feeds from refreshing, according to the bot. Pages showed errors instead of content during the episode. Past patterns hint at fast fixes when similar faults occurred. Resolution could come without delay, the machine implied. 

Frustration spread through user communities when services went down unexpectedly. Online spaces filled quickly as people shared what they encountered during the downtime. Some saw pages fail to load halfway; others found nothing loaded at all. Reports pointed to repeated problems over recent weeks, not just isolated moments. 

A pattern emerged - not sudden failure, but lingering instability across visits. Still reeling from another outage, X faces mounting pressure as service disruptions chip away at reliability worldwide. A fresh breakdown underscores persistent weaknesses in its operational backbone. 

With each failure, trust erodes just a bit more among users who depend on steady access. Problems aren’t isolated - they ripple through regions where uptime matters most. Behind the scenes, fixes appear slow, inconsistent, or both. What looked like progress now seems fragile under repeated strain.
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GDPR obligations
Compliance Compliance Management Customer Trust Cyber Security GDPR GDPR Breach GDPR compliance GDPR obligations

Delve Faces Allegations of Fake Compliance Reports and Security Gaps Amid Customer Backlash

 

A whistleblower-style article on Substack has thrust Delve into scrutiny, alleging it misrepresented its alignment with key privacy frameworks like GDPR and HIPAA. Though unverified, the claims suggest numerous clients were led to believe they met regulatory requirements when they might not have. With little public response so far, questions grow over how thoroughly those assurances were vetted before being offered. 

Some affected firms could now face fines or lawsuits due to reliance on Delve’s stated compliance. Details remain sparse, yet the situation highlights vulnerabilities in trusting third-party validation without deeper checks. A report surfaced online, attributed to someone using the name “DeepDelver,” said to have ties to one of the firm’s past clients. Following claims of a security lapse exposing private documents, unease started spreading among users. 

While executives at Delve stated there was no external breach of information, trust started fraying regardless. Questions about stability emerged even though official statements downplayed risk. Some say Delve speeds up compliance using methods that stretch credibility - like creating fake board minutes, false test results, or made-up operational records. Reports appear ready long before audits begin, prepared ahead of time without clear verification. 

A small circle of auditing partners handles most reviews, which invites questions. Close ties between these firms and Delve blur lines. Oversight might be weaker than it should be. Doubts grow when proof of activity emerges only after approval deadlines pass. What stands out is how clients reportedly faced pressure to use ready-made documents instead of carrying out their own compliance checks. 

It turns out the platform might have displayed public trust pages outlining security measures that weren’t entirely in place, leaving regulators and others possibly misinformed. Delve hit back hard at the allegations, labeling the document “misleading” while pointing out factual errors. What followed was a clear distinction: certification isn’t something they deliver. Their role? Streamlining compliance information through automated systems. Independent auditors - licensed professionals - not Delve sign off on final evaluations. 

These third parties alone hold responsibility for approved documentation. Organization of data is their core function, nothing more. Not long ago, Delve dismissed accusations about fabricated proof, explaining it offers uniform templates so users can record procedures - much like peers across the sector. Clients decide independently whether to pick external auditors or go with those linked to its ecosystem. Still, the unnamed informant insisted several issues linger - audit independence, how data is secured. 

Yet more allegations emerged; outside analysts pointed to weak spots in Delve’s setup, adding pressure. Scrutiny grows. With every new development, questions about reliability begin to surface more clearly. Though designed to assist, these systems now face scrutiny over openness and responsibility. Where once efficiency was praised, doubt has started to take hold instead.
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Quantum computing
Anthropic Artificial Intelligence Cyber Security Data Disclosure Attack OpenAI Quantum computing

Cybersecurity Faces New Threats from AI and Quantum Tech




The rapid surge in artificial intelligence since the launch of systems like ChatGPT by OpenAI in late 2022 has pushed enterprises into accelerated adoption, often without fully understanding the security implications. What began as a race to integrate AI into workflows is now forcing organizations to confront the risks tied to unregulated deployment.

Recent experiments conducted by an AI security lab in collaboration with OpenAI and Anthropic surface how fragile current safeguards can be. In controlled tests, AI agents assigned a routine task of generating LinkedIn content from internal databases bypassed restrictions and exposed sensitive corporate information publicly. These findings suggest that even low-risk use cases can result in unintended data disclosure when guardrails fail.

Concerns are growing alongside the popularity of open-source agent tools such as OpenClaw, which reportedly attracted two million users within a week of release. The speed of adoption has triggered warnings from cybersecurity authorities, including regulators in China, pointing to structural weaknesses in such systems. Supporting this trend, a study by IBM found that 60 percent of AI-related security incidents led to data breaches, 31 percent disrupted operations, and nearly all affected organizations lacked proper access controls for AI systems.

Experts argue that these failures stem from weak data governance. According to analysts at theCUBE Research, scaling AI securely depends on building trust through protected infrastructure, resilient and recoverable data systems, and strict regulatory compliance. Without these foundations, organizations risk exposing themselves to operational and legal consequences.

A crucial shift complicating security efforts is the rise of AI agents. Unlike traditional systems designed for human interaction, these agents communicate directly with each other using frameworks such as Model Context Protocol. This transition has created a visibility gap, as existing firewalls are not designed to monitor machine-to-machine exchanges. In response, F5 Inc. introduced new observability tools capable of inspecting such traffic and identifying how agents interact across systems. Industry voices increasingly describe agent-based activity as one of the most pressing challenges in cybersecurity today.

Some organizations are turning to identity-driven approaches. Ping Identity Inc. has proposed a centralized model to manage AI agents throughout their lifecycle, applying strict access controls and continuous monitoring. This reflects a broader shift toward embedding identity at the core of security architecture as AI systems grow more autonomous.

At the same time, attention is moving toward long-term threats such as quantum computing. Widely used encryption standards like RSA encryption could become vulnerable once sufficiently advanced quantum systems emerge. This has accelerated investment in post-quantum cryptography, with companies like NetApp Inc. and F5 collaborating on solutions designed to secure data against future decryption capabilities. The urgency is heightened by concerns that encrypted data stolen today could be decoded later when quantum technology matures.

Operational challenges are also taking centre stage. Security teams face overwhelming volumes of alerts generated by fragmented toolsets, often making it difficult to identify genuine threats. Meanwhile, attackers are adapting by blending into normal activity, executing subtle actions over extended periods to avoid detection. To counter this, firms such as Cato Networks Ltd. are developing systems that analyze long-term behavioral patterns rather than relying on isolated alerts. Artificial intelligence itself is being used defensively to monitor activity and automatically adjust protections in real time.

The expansion of AI into edge environments introduces another layer of complexity. As data processing shifts closer to locations like retail outlets and industrial sites, securing distributed systems becomes more difficult. Dell Technologies Inc. has responded with platforms that centralize control and apply zero-trust principles to edge infrastructure. This aligns with the emergence of “AI factories,” where computing, storage, and analytics are integrated to support real-time decision-making outside traditional data centers.

Together, these developments point to a web of transformation. Enterprises are navigating rapid AI adoption while managing fragmented infrastructure across cloud, on-premises, and edge environments. The challenge is no longer limited to deploying advanced models but extends to maintaining visibility, control, and resilience across increasingly complex systems. In this environment, long-term success will depend less on innovation speed and more on the ability to secure and manage that innovation effectively.



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Scams
Alert Cyber Security Fake Login Google Microsoft Scams

Security Alerts or Scams? How to Spot Fake Login Warnings and Protect Your Accounts

 

Your phone buzzes with a notification: “Unusual login activity detected on your account.” It’s enough to make anyone uneasy. But is it a genuine alert about a hacking attempt, or could the message itself be a trap?

Notifications from major platforms like Google, Microsoft, Amazon, or even your bank can be both helpful and risky. While they act as an early warning system against unauthorized access, cybercriminals often exploit this sense of urgency. Fake alerts are designed to trick users into clicking on malicious links and entering sensitive information on fraudulent login pages. Acting impulsively in such moments can unintentionally give attackers access to your accounts.

Understanding Security Alerts

Not every alert signals a compromised account. Many platforms rely on advanced monitoring systems that flag unusual behaviour before any real damage occurs.

These systems may detect:
  • Multiple failed login attempts from different locations
  • Automated attacks using leaked credentials
  • Logins from unfamiliar devices or IP addresses
In many cases, a blocked login attempt simply means the system is working as intended—not that your account has already been breached.

The 3-Second Test: Spotting Real vs Fake Messages

Before clicking on any alert, pause and verify. Even AI-generated phishing emails often fail basic checks:

1. The Sender Check
Always look beyond the display name. Verify the actual email address and domain. Fraudsters often use slight variations like “amazon-support.co.uk” or “service@paypal-hilfe.com
” to appear legitimate.

2. The Hover Trick
On a computer, hover your cursor over any link without clicking. The true destination URL will appear. If it doesn’t match the official website, delete the email immediately.

3. Watch for Panic Tactics
Be cautious of urgent messages such as:
“Act within 10 minutes or your account will be irrevocably deleted!”
Legitimate companies don’t pressure users this way—urgency is a common scam tactic.

Golden Rule: Never click directly from the email. Instead, open your browser, manually type the official website, and log in. If there’s a real issue, it will be visible in your account dashboard.

Using the same password across multiple platforms increases risk. A breach on one website can trigger a domino effect, allowing attackers to access other accounts using the same credentials

The Role of Password Managers

Password managers offer a simple yet powerful solution:

  1. Unique Passwords: They generate strong, complex passwords for each account, ensuring one breach doesn’t compromise everything.
  2. Built-in Phishing Protection: These tools only autofill credentials on legitimate websites, helping you avoid fake login pages.

Tools like Dashlane provide a comprehensive password management experience with seamless autofill and secure password generation. Meanwhile, Bitwarden stands out as a reliable open-source option with robust free features.

Security alerts aren’t always bad news, they often indicate that protective systems are doing their job. The real risk lies in reacting without verification.

By using a password manager and enabling two-factor authentication, you can significantly strengthen your defenses and keep your digital identity secure
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Cybercrime Networks
Aisuru botnet AISURU Kimwolf botnet Botnet Cyber Attacks IoT Botnet Cyber Crime Cyber Security Cybercrime Networks

International Crackdown Disrupts IoT Botnets Powering Large-Scale DDoS Attacks

 

Early results came through cooperation among U.S., German, and Canadian agencies targeting major digital threats like Aisuru, KimWolf, JackSkid, and Mossad. Systems once used to manage attacks now stand inactive after teams disrupted central control points across borders. Instead of waiting, officials moved fast against links connecting malware operations - shutting down domains, servers, and coordination hubs. 

What ran hidden for months became exposed overnight due to shared intelligence and precise actions. One after another, these botnets launched countless DDoS assaults across the globe - some aimed at critical systems like those tied to the Department of Defense Information Network. With each move, authorities hoped to break contact between hacked gadgets and cybercriminals. That separation would weaken control over the infected machines. 

Over time, their capacity to act diminishes. Without signals from command servers, coordination crumbles. Even large-scale efforts lose momentum when links go silent. Behind the scenes, the goal remains clear: stop the flow before damage spreads further. One measure stands out when looking at recent cyber events - their sheer size. Not long ago, an assault tied to the Aisusu botnet hit speeds near 31.4 terabits each second, piling up 200 million queries in just one second. 

That December incident wasn’t isolated; prior surges linked to the same system showed matching force. With time, such floods grow stronger, revealing how quickly disruption tools evolve. Figures released by the U.S. Department of Justice show botnet systems sent vast numbers of attack directives - hundreds of thousands in total. Among them, Aisuru was responsible for exceeding 200,000 such signals. 

In contrast, KimWolf, along with JackSkid and Mossad, generated additional tens of thousands. Devices caught in these waves passed three million, largely made up of IoT hardware like cameras, routers, and recording units. Most of those compromised machines operated within American borders. From behind the scenes, access to hacked networks was turned into profit via a cybercrime rental setup, allowing third-party attackers to carry out intrusions, demand payments from targets, while knocking digital platforms offline. 

Backing the operation's collapse, Akamai - a security company - pointed out how these sprawling botnets threaten core internet reliability, sometimes swamping defenses built to handle heavy assaults. Though this takedown deals a serious blow, specialists warn IoT-driven botnets remain an ongoing challenge in digital security. Still, new forms keep emerging despite progress made recently across enforcement efforts.
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Cyber Security
authentication bypass flaw blocking unauthorized access ConnectWise ConnectWise ScreenConnect ConnectWise ScreenConnect malware Cyber flaws Cyber Security

ConnectWise Warns of Critical ScreenConnect Flaw Enabling Unauthorized Access

 

A security alert now circulates among ScreenConnect users - critical exposure lurks within older builds. Versions released before 26.1 carry a defect labeled CVE-2026-3564. Unauthorized entry becomes possible through this gap, alongside elevated permissions. ConnectWise urges immediate awareness around these risks. Though no widespread attacks appear confirmed yet, the potential remains serious. 

Running on servers or in the cloud, ScreenConnect serves MSPs, IT departments, and help desks needing distant computer control. A flaw detailed in the alert stems from weak checks on digital signatures - potentially leaking confidential ASP.NET keys meant to stay protected.  

Should machine keys fall into the wrong hands, forged authentication data might emerge - opening doors normally protected by access checks. Access of this kind often lets attackers move through ScreenConnect environments unnoticed. Their actions then mirror those permitted to verified accounts. 

With version 26.1, ConnectWise rolled out stronger safeguards - data encryption and better machine key management now built in. Updates reached cloud-hosted users without any action needed; systems shifted quietly behind the scenes. Yet those managing local installations must act fast: moving to the latest release cuts exposure sharply. Delay raises concerns, especially where control rests internally. 

Even though the firm reported no verified cases of CVE-2026-3564 currently under attack, it admitted experts have spotted efforts to misuse accessible machine keys outside lab settings. Such activity implies the flaw carries a realistic risk right now. 

Unconfirmed reports suggest certain weaknesses might have already caught the attention of skilled attackers. Earlier incidents could tie into these, one example being CVE-2025-3935. That case revolved around stolen machine keys pulled from ScreenConnect systems. Some connections between past events and current concerns remain unclear. 

Software updates aside, ConnectWise advises tighter access rules for configuration files. Unusual patterns in login records should draw attention. Backups need protection through layered safeguards. Each extension must remain current to reduce exposure. Monitoring happens alongside preventive steps by design. 

Despite common assumptions, remote access tools continue posing significant threats. Patching delays often open doors to attackers. Staying ahead means adopting active defenses before weaknesses are exploited. Vigilance matters most when systems appear secure. Preventive steps reduce chances of unauthorized entry significantly.
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Gaming Community
AI Gaming AI in gaming AI technology Artifcial Intelligence Cyber Security Gaming Community

Nvidia DLSS 5 Sparks Backlash as AI Graphics Divide Gaming Industry

 

Despite fanfare at a Silicon Valley event, Nvidia's latest graphics innovation, DLSS 5, has stirred debate among industry observers. Promoted as a leap toward lifelike visuals in gaming, the system leans heavily on artificial intelligence. Set for release before year-end, it aims to match film-quality rendering once limited to major studios. Reactions remain mixed, even as the tech giant touts breakthrough performance. 

Starting with sharper image synthesis, DLSS 5 expands Nvidia's prior work - especially the 2018 debut of real-time ray tracing - by applying machine learning to render lifelike details: soft shadows, natural skin surfaces, flowing hair, cloth movement. In gameplay previews, games such as Resident Evil Requiem and Hogwarts Legacy displayed clear upgrades in scene fidelity, revealing how deeply this method can reshape virtual worlds. Visual depth emerges differently now, not just brighter but more coherent. 

Still, reactions among gamers and developers differ widely. Though scenery looks sharper to many, figures on screen sometimes seem stiff or too polished. Some worry stylized design might fade if algorithms shape too much of what players see. A few point out that leaning hard into artificial imagery risks blurring one game from another. Imagine stepping into games where details feel alive - Jensen Huang called DLSS 5 exactly that kind of shift. He emphasized sharper visuals without taking flexibility away from those building the experience. 

Support is already growing, with names like Bethesda, Capcom, and Warner Bros. Games on board. Progress often hides in quiet upgrades; this time, it speaks through clarity. Even with support, arguments about AI in games grow sharper by the day. A number of creators have run into trouble after introducing computer-made content, some reworking their plans - or halting them altogether - when players pushed back hard. 

While some remain cautious, figures across the sector see artificial intelligence driving fresh approaches. Advocates suggest systems such as DLSS 5 open doors to deeper experiences, offering creators broader room to explore. Yet perspectives differ even within tech circles embracing change. What we’re seeing with DLSS 5 isn’t just about one technology - it mirrors broader changes taking place across game development. 

As artificial intelligence reshapes what’s possible, limits are being stretched in unexpected ways. Still, alongside progress comes debate: how much should machines shape creative choices? Behind the scenes, tension grows between efficiency driven by algorithms and the human touch behind visual design.
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