CySecurity News - Latest Information Security and Hacking Incidents: Artificial Intelligence

AI Models API Artificial Intelligence Claude Codex Security Headless 360 Salesforce Technology

Salesforce’s New “Headless 360” Lets AI Agents Run Its Platform

Salesforce has introduced what it describes as the most crucial architectural overhaul in its 27-year history, launching a new initiative called “Headless 360.” The update is designed to allow artificial intelligence agents to control and operate the company’s entire platform without requiring a traditional graphical interface such as a dashboard or browser.

The announcement was made during the company’s annual TDX developer conference in San Francisco, where Salesforce revealed that it is releasing more than 100 new developer tools and capabilities. These tools immediately enable AI systems to interact directly with Salesforce environments. The move reflects a deeper shift in enterprise software, where the rise of intelligent agents capable of reasoning and executing tasks is forcing companies to rethink whether conventional user interfaces are still necessary.

Salesforce’s answer to that question is direct: instead of designing software primarily for human interaction, the platform is now being rebuilt so that machines can access and operate it programmatically. According to the company, this transformation began over two years ago with a strategic decision to expose all internal capabilities rather than keeping them hidden behind user interfaces.

This shift is taking place during a period of uncertainty in the broader software industry. Concerns that advanced AI models developed by companies like OpenAI and Anthropic could disrupt traditional software business models have already impacted market performance. Industry indicators, including software-focused exchange-traded funds, have declined substantially, reflecting investor anxiety about the long-term relevance of existing SaaS platforms.

Senior leadership at Salesforce has indicated that the new architecture is based on practical challenges observed while deploying AI systems across enterprise clients. According to internal insights, building an AI agent is only the initial step. Organizations also face ongoing challenges related to development workflows, system reliability, updates, and long-term maintenance.

To address these challenges, Headless 360 is structured around three foundational pillars.

The first pillar focuses on development flexibility. Salesforce has introduced more than 60 tools based on Model Context Protocol, along with over 30 pre-configured coding capabilities. These allow external AI coding agents, including systems such as Claude Code, Cursor, Codex, and Windsurf, to gain direct, real-time access to a company’s Salesforce environment. This includes data, workflows, and underlying business logic. Developers are no longer required to use Salesforce’s own integrated development environment and can instead operate from any terminal or external setup.

In addition, Salesforce has upgraded its native development environment, Agentforce Vibes 2.0, by introducing an “open agent harness.” This system supports multiple agent frameworks, including those from OpenAI and Anthropic, and dynamically adjusts capabilities depending on which AI model is being used. The platform also supports multiple models simultaneously, including advanced systems like Claude Sonnet and GPT-5, while maintaining full awareness of the organization’s data from the start.

A notable technical enhancement is the introduction of native React support. During demonstrations, developers created a fully functional application using React instead of Salesforce’s traditional Lightning framework. The application connected to Salesforce data through GraphQL while still inheriting built-in security controls. This significantly expands front-end flexibility for developers.

The second pillar focuses on deployment. Salesforce has introduced an “experience layer” that separates how an AI agent functions from how it is presented to users. This allows developers to design an experience once and deploy it across multiple platforms, including Slack, mobile applications, Microsoft Teams, ChatGPT, Claude, Gemini, and other compatible environments. Importantly, this can be done without rewriting code for each platform. The approach represents a change from requiring users to enter Salesforce interfaces to delivering Salesforce-powered experiences directly within existing workflows.

The third pillar addresses trust, control, and scalability. Salesforce has introduced a comprehensive set of tools that manage the entire lifecycle of AI agents. These include systems for testing, evaluation, monitoring, and experimentation. A central component is “Agent Script,” a new programming language designed to combine structured, rule-based logic with the flexible reasoning capabilities of AI models. It allows organizations to define which parts of a process must follow strict rules and which parts can rely on AI-driven decision-making.

Additional tools include a Testing Center that identifies logical errors and policy violations before deployment, custom evaluation systems that define performance standards, and an A/B testing interface that allows multiple agent versions to run simultaneously under real-world conditions.

One of the key technical challenges addressed by Salesforce is the difference between probabilistic and deterministic systems. AI agents do not always produce identical results, which can create instability in enterprise environments where consistency is critical. Early adopters reported that once agents were deployed, even small modifications could lead to unpredictable outcomes, forcing teams to repeat extensive testing processes.

Agent Script was developed to solve this problem by introducing a structured framework. It defines agent behavior as a state machine, where certain steps are fixed and controlled while others allow flexible reasoning. This approach ensures both reliability and adaptability.

Salesforce also distinguishes between two types of AI system architectures. Customer-facing agents, such as those used in sales or support, require strict control to ensure they follow predefined rules and maintain brand consistency. These operate within structured workflows. In contrast, employee-facing agents are designed to operate more freely, exploring multiple paths and refining their outputs dynamically before presenting results. Both systems operate on a unified underlying architecture, allowing organizations to manage them without maintaining separate platforms.

The company is also expanding its ecosystem. It now supports integration with a wide range of AI models, including those from Google and other providers. A new marketplace brings together thousands of applications and tools, supported by a $50 million initiative aimed at encouraging further development.

At the same time, Salesforce is taking a flexible approach to emerging technical standards such as Model Context Protocol. Rather than relying on a single method, the company is offering APIs, command-line interfaces, and protocol-based integrations simultaneously to remain adaptable as the industry evolves.

A real-world example surfaced during the announcement demonstrated how one company built an AI-powered customer service agent in just 12 days. The system now handles approximately half of customer interactions, improving efficiency while reducing operational costs.

Finally, Salesforce is also changing its business model. The company is shifting away from traditional per-user pricing toward a consumption-based approach, reflecting a future where AI agents, rather than human users, perform the majority of work within enterprise systems.

This transformation suggests a new layer in strategic operations. Instead of resisting the rise of AI, Salesforce is restructuring its platform to align with it, betting that its existing data infrastructure, enterprise integrations, and accumulated operational logic will continue to provide value even as software becomes increasingly autonomous.

Can AI Own Its Work? A Debate That Started With a Monkey Photo

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Technology

AI Generated Content Law Artificial Intelligence copyright ownership Technology

Can AI Own Its Work? A Debate That Started With a Monkey Photo

A single photograph captured in a remote forest over a decade ago has become central to one of the most complex legal questions of the digital age: what happens when creative work is produced without direct human authorship? The answer now carries long-term consequences for artificial intelligence, creative industries, and ownership rights in the modern world.

The image in question originated in 2011, when wildlife photographer David Slater was documenting crested black macaques in Indonesia. These monkeys are not only endangered but also known for their highly expressive faces, making them attractive subjects for photography. However, Slater faced difficulty capturing close-up shots because the animals were wary of human presence.

To work around this, he positioned his camera on a tripod, enabled automatic focus, and used a flash, allowing the monkeys to approach and interact with the equipment without feeling threatened. His approach relied on curiosity rather than control. Eventually, one macaque handled the camera and pressed the shutter button while looking directly into the lens. The resulting image, widely known as the “monkey selfie,” appeared almost intentional, with the animal’s expression resembling a posed portrait.

While the photograph initially brought attention and recognition, it soon triggered an unexpected legal dispute. The core issue was deceptively simple: if a photograph is not taken by a human, can anyone claim ownership over it?

The situation escalated when the image was uploaded to Wikipedia, making it freely accessible worldwide. Slater objected to this distribution, arguing that he had lost approximately £10,000 in potential earnings because the image could now be used without payment. However, the Wikimedia Foundation refused to remove the photograph. Its reasoning was based on copyright law, which generally requires a human creator. Since the image was captured by an animal, the organisation classified it as public domain material.

This interpretation was later reinforced by the U.S. Copyright Office, which formally clarified that works produced without human authorship cannot be registered. In its guidance, the office explicitly listed a photograph taken by a monkey as an example of ineligible material, establishing a clear precedent.

The dispute took another unusual turn when People for the Ethical Treatment of Animals filed a lawsuit attempting to assign copyright ownership to the macaque itself. Although framed as a legal claim over the photograph, the case was widely interpreted as an effort to establish broader legal rights for animals. After several years of legal proceedings, a court dismissed the case, concluding that animals do not have the legal capacity to initiate lawsuits.

Legal experts later observed that, although the case focused on animal authorship, it introduced a broader conceptual challenge that would become more relevant with the rise of artificial intelligence. According to intellectual property lawyer Ryan Abbott, the debate could easily extend beyond animals to machines capable of producing creative outputs.

This possibility became reality when computer scientist Stephen Thaler attempted to secure copyright protection for an image generated by his AI system, DABUS. Thaler described the system as capable of independently producing ideas, arguing that it should be recognised as the sole creator of its output. He characterised the system as exhibiting a form of machine-based cognition, though this view is strongly disputed within the scientific community.

Despite these claims, the Copyright Office rejected the application, applying the same reasoning used in the monkey selfie case. Because the work was not created by a human, it could not qualify for copyright protection. This rejection led to a legal challenge that progressed through multiple levels of the U.S. judicial system.

When the case reached the Supreme Court of the United States, the court declined to hear it, leaving lower court rulings intact. The outcome effectively confirmed that, under current U.S. law, works generated entirely by artificial intelligence cannot be owned by anyone, including the developer of the system or the individual who prompted it.

This position has reverberating implications for the creative economy. Copyright law exists to allow creators and organisations to control and monetise their work. Without ownership rights, it becomes difficult to build sustainable business models around fully AI-generated content. Legal scholar Stacey Dogan noted that this limitation reduces the likelihood of a future where machine-generated content completely replaces human-created media.

At the same time, the rapid expansion of generative AI tools continues to complicate the landscape. These systems function by analysing large datasets and producing outputs based on user instructions, often referred to as prompts. While they can generate text, images, and video at scale, their outputs raise questions about originality and authorship, particularly when human involvement is minimal.

Recent industry developments illustrate this uncertainty. Experimental AI-generated content has attracted large audiences online, suggesting a level of public interest, even if motivations such as novelty or criticism play a role. However, some technology companies have begun reassessing their AI content strategies, particularly where ownership and profitability remain unclear.

Expert opinion on the value of fully AI-generated content remains divided. Some specialists argue that such content lacks depth or authenticity, while others view AI as a useful tool for supporting human creativity rather than replacing it. This perspective positions AI as a collaborator rather than an independent creator.

Legal approaches also vary internationally. In the United Kingdom, copyright law allows ownership of computer-generated works by assigning authorship to the individual responsible for arranging their creation. However, this framework is currently being reconsidered as policymakers evaluate whether it remains appropriate in the context of modern AI systems.

One of the most complex unresolved issues involves hybrid creation. When humans actively guide, refine, and edit AI-generated outputs, determining ownership becomes less straightforward. A notable example involves an AI-assisted artwork that won a competition after extensive prompting and editing, raising questions about how much human contribution is required for copyright protection.

This debate is not entirely new. When photography first emerged, similar concerns were raised about whether cameras, rather than humans, were responsible for creative output. Over time, legal systems adapted by recognising the role of human intention and decision-making. Artificial intelligence now presents a more advanced version of that same challenge.

For now, the legal position in the United States remains clear: without meaningful human involvement, creative works cannot be protected by copyright. However, as AI becomes increasingly integrated into creative processes, the distinction between human and machine contribution is becoming more difficult to define.

What began as an unexpected interaction between a monkey and a camera has therefore evolved into a defining case in the global conversation about creativity, ownership, and technology. The decisions made in courts today will shape how creative work is produced, distributed, and valued in the future.

AI Was Meant to Help. So Why Is It Making Work Harder for Women in Indonesia?

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women

Artificial Intelligence Capitalism developing countries Gig Economy Indonesa Technology women

AI Was Meant to Help. So Why Is It Making Work Harder for Women in Indonesia?

Artificial intelligence is often presented as a neutral and forward-looking force that improves efficiency and removes human bias from decision-making. In practice, however, many women working in Indonesia’s gig economy experience these systems very differently. Rather than easing workloads, AI-driven platforms are intensifying existing pressures.

Recent research examining female gig workers introduces the concept of “AI colonialism.” This idea describes how older patterns of domination continue through digital systems. In this framework, powerful technology actors, largely based in wealthier regions, extract labour, data, and economic value from workers in developing countries, reinforcing unequal global relationships. The structure resembles historical colonial systems, but operates through algorithms and platforms instead of direct political control.

In Indonesia, platforms such as Gojek, Grab, Maxim, and Shopee rely heavily on informal workers. These companies have not transformed the nature of employment. Instead, they have digitised an already informal labour market. Workers are labelled as independent “partners,” which excludes them from basic protections such as minimum wages, paid sick leave, and maternity benefits. Earnings depend entirely on the number of completed tasks and algorithm-based performance scores.

For women, this structure intersects with what is often described as the “double burden,” where paid work must be balanced alongside unpaid domestic responsibilities. One delivery worker, Lia, begins her day before sunrise by preparing meals and organising her children’s routines. Only after completing these responsibilities can she log into the platform. As she explains, the system recognises only whether she is online, not the constraints shaping her availability.

Platform algorithms prioritise continuous, uninterrupted activity. Incentive systems often require completing a fixed number of orders within strict time windows. For workers managing caregiving roles, this creates structural disadvantages. Logging off to attend to family responsibilities can result in lost bonuses, while reducing work hours due to fatigue or health issues leads to declining performance metrics.

This reflects a greater economic reality in which unpaid domestic labour underpins the formal economy without recognition or compensation. Instead of addressing this imbalance, AI systems can intensify it. Another worker, Cinthia, observed a noticeable drop in job assignments after taking time off due to illness. The experience created a sense that the system penalises any interruption, making workers reluctant to pause even when necessary.

Although algorithms do not explicitly target women, they are designed around an ideal worker who is always available and unconstrained by caregiving duties. This assumption produces indirect but consistent disadvantage. The claim that digital platforms operate neutrally is further challenged by everyday experiences. For example, a driver named Yanti often informs passengers in advance that she is female, leading to frequent cancellations. While the system records these cancellations, it does not capture the gender bias behind them.

Safety concerns also shape participation. Many women avoid working late hours due to risk, which limits access to peak-demand periods and higher earnings. The system interprets this reduced availability as lower productivity. Scholars such as Virginia Eubanks have argued that automated systems frequently replicate and amplify existing social inequalities rather than eliminate them.

Similar patterns have been observed in other countries. In India, women working in ride-hailing services report lower average earnings, partly because safety considerations influence when and where they work. Algorithms, however, measure output without accounting for these risks.

Safety challenges persist even within delivery roles. Around 90% of women in group discussions reported choosing delivery work over ride-hailing due to perceived safety advantages, yet harassment remains a concern from both customers and other drivers. During the COVID-19 pandemic, gig workers were classified as essential, but their incomes declined sharply, in some cases by up to 67% in early 2020. To compensate, many worked more than 13 hours a day. Despite these conditions, platform performance systems remained unchanged, and illness-related breaks often resulted in lower ratings.

This inflicts a deeper impact in the contemporary labour control, where oversight is embedded within digital systems rather than managed by human supervisors. AI colonialism, in this sense, extends beyond ownership to the structure of control itself. Workers provide labour, time, and data, while platforms retain authority over decision-making processes.

In response, women workers have developed informal networks through messaging platforms to share information, warn others about unsafe situations, and adapt to algorithmic changes. They support each other by increasing activity on inactive accounts, lending money for operational costs, and collectively responding to account suspensions. When harassment occurs, information is circulated quickly to protect others.

These practices represent a form of mutual support rooted in shared vulnerability. Rather than relying on formal recognition as employees, many women build systems of protection among themselves. This surfaces a form of everyday resistance, where collective action becomes a strategy for navigating structural constraints.

Artificial intelligence is not inherently exploitative. However, when deployed within unequal economic systems, it can reinforce patterns of extraction and imbalance. As digital platforms continue to expand, understanding the lived experiences of workers, particularly women in developing economies, is essential. Behind every efficient system is a human reality shaped by trade-offs between income, safety, and dignity.

AI Scams Are Becoming Harder to Detect — 7 Warning Signs You Should Watch Closely

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Technology

Artificial Intelligence cryptocurrency Cyber Fraud Deepfake Scams Technology

AI Scams Are Becoming Harder to Detect — 7 Warning Signs You Should Watch Closely

Artificial intelligence is not only improving everyday technology but also strengthening both traditional and emerging scam techniques. As a result, avoiding fraud now requires greater awareness of how these schemes are taking new shapes.

Being able to identify scams is an essential skill for everyone, regardless of age. This is especially important as AI tools continue to advance rapidly, contributing to a noticeable increase in reported fraud cases. According to the Federal Bureau of Investigation’s 2025 Internet Crime Report, complaints linked to cryptocurrency and artificial intelligence ranked among the most financially damaging cybercrimes, with total losses approaching $21 billion. The agency also highlighted that, for the first time in its history, its Internet Crime Complaint Center included a dedicated section on artificial intelligence, documenting 22,364 cases that resulted in losses of nearly $893 million.

These scams are increasingly convincing. AI can generate realistic emails and replicate human voices through audio deepfakes, making fraudulent communication difficult to distinguish from legitimate interactions. Because of this, such threats should be treated as ongoing and persistent risks.

Protecting yourself, your family, and your finances requires both instinct and awareness. By training both your attention to detail and your ability to listen carefully, you can better identify suspicious activity. Below are seven warning signs that can help you recognize AI-driven scams and avoid serious consequences.

1. Messages that feel unusually personalized

AI can gather publicly available details, including your job, interests, or recent purchases, to create messages that appear tailored specifically to you. While these messages may seem accurate, they can still contain subtle errors or incorrect assumptions about your life, which should raise concern.

2. Requests that create urgency

Scammers often attempt to rush you with statements such as warnings that your account will be locked, demands for immediate payment, or requests for login credentials to restore access. This pressure is designed to force quick decisions without careful thinking.

3. Messages that appear overly polished

Unlike older scams filled with spelling or grammar mistakes, AI-generated messages are often clear and well-written. However, phrases like “confirm your information to avoid cancellation” or “we noticed unusual activity” should still be treated cautiously, especially if accompanied by suspicious visuals or a lack of supporting detail.

4. Audio that sounds slightly unnatural

Voice-cloning technology can imitate people you know, making phone-based scams more believable. Still, these voices may reveal themselves through unnatural pacing, limited emotional variation, or requests that seem out of character for the person being impersonated.

5. Deepfake videos that seem real but contain flaws

AI can also generate convincing videos of colleagues, family members, or even public figures. These may appear during video calls, workplace interactions, or through compromised social media accounts. Warning signs include inconsistent lighting, unusual shadows, or subtle distortions in facial movement.

6. Attempts to move conversations across platforms

Scammers may begin communication through email or professional platforms and then attempt to shift the interaction to messaging apps, payment platforms, or other channels. This tactic, often supported by chatbot-driven conversations, is used to appear credible while avoiding detection.

7. Unusual or suspicious payment requests

Requests for payment through gift cards, wire transfers, or cryptocurrency remain a major red flag. These methods are difficult to trace and are frequently used in fraudulent schemes, regardless of how legitimate the request may initially appear.

Why awareness matters

While AI has not changed the underlying tactics of scams, it has made them far more refined and scalable. Techniques such as impersonation, urgency, and trust-building are now enhanced through automation and data-driven personalization.

As these technologies continue to become an omnipresent aspect of our lives and keep developing, the risk will proportionately grow. Staying cautious, verifying unexpected requests, and sharing this knowledge with friends and family are critical steps in reducing exposure.

In a digital environment where scams increasingly resemble genuine communication, recognizing these warning signs remains one of the most effective ways to stay protected.

Why Stolen Passwords Are Now the Biggest Cyber Threat

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Artificial Intelligence Credentials Data Breach login data

Why Stolen Passwords Are Now the Biggest Cyber Threat

Organizations today often take confidence in hardened perimeters, well-configured firewalls, and constant monitoring for software vulnerabilities. Yet this defensive focus can overlook a more subtle reality. While attention remains fixed on preventing break-ins, attackers are increasingly entering systems through legitimate access points, using valid employee credentials as if they belong there.

This shift is not theoretical. Current threat patterns indicate that nearly one out of every three cyber intrusions now involves the use of real login credentials. Instead of forcing entry, attackers authenticate themselves and operate under the identity of trusted users. In practical terms, this allows them to function like an ordinary colleague within the system, making their actions far less likely to trigger suspicion.

Credential theft itself has existed for years, but its scale and execution have changed dramatically. Artificial intelligence has removed many of the barriers that once limited these attacks. Phishing campaigns, which previously required careful design and technical effort, can now be generated rapidly and in large volumes. At the same time, stolen usernames and passwords can be automatically tested across multiple platforms, allowing attackers to validate access almost instantly. This combination has created a form of intrusion that appears routine while expanding at a much faster pace.

The ecosystem behind these attacks has also evolved into a structured and highly organized market. Certain actors specialize in collecting credentials, others focus on verifying them, and many sell confirmed access through underground platforms. Importantly, the buyers are no longer limited to financially motivated groups. State-linked actors are also acquiring such access, using it to conduct operations that resemble conventional cybercrime, thereby making attribution more difficult.

This level of organization becomes especially dangerous in supply chain environments. Modern businesses rely on interconnected systems, vendors, and third-party services. Within such networks, a single compromised credential can act as a gateway into multiple systems. Attackers understand this interconnected structure and actively collaborate, sharing tools, scripts, and access to maximize efficiency while minimizing risk.

In contrast, defensive efforts often remain fragmented. Security teams frequently operate within isolated frameworks, with limited information sharing across organizations. Cultural challenges, including reluctance to disclose incidents, further restrict transparency. As a result, attackers benefit from collaboration, while defenders struggle to identify patterns across incidents.

Artificial intelligence has further transformed how credential-based attacks are carried out. Previously, executing such operations at scale required advanced technical expertise, including writing scripts to validate login attempts and maintaining stealth within a network. Today, automated tools can handle these tasks. Attackers can deploy stolen credentials across platforms almost instantly. Once access is gained, AI-driven tools can replicate normal user behavior, such as typical login times, navigation patterns, and file interactions. Whether conducting broad password-spraying campaigns or targeted intrusions, attackers can now move at a speed and level of sophistication that traditional defenses were not designed to counter.

At the same time, the supply of stolen credentials is increasing. Research shows that information-stealing malware, a primary method used to capture login data, has risen by approximately 84 percent over the past year. This surge, combined with easier exploitation methods, is widening a critical detection gap for security teams.

Closing this gap requires a fundamental rethinking of detection strategies. Traditional systems often fail when an attacker is already authenticated and operating within expected conditions, such as normal working hours. To address this, organizations must begin monitoring identity threats earlier in the attack lifecycle. This includes integrating intelligence from underground forums and illicit marketplaces into active defense systems. When compromised credentials are identified externally, immediate actions such as password resets and enforced multi-factor authentication should be triggered before those credentials are used internally.

Authentication methods themselves must also evolve. Widely used approaches like SMS codes and push notifications are increasingly vulnerable to interception through advanced attack techniques. More secure alternatives, including hardware-based authentication keys and certificate-driven systems, offer stronger protection because they cannot be easily intercepted or replicated. If an authentication factor can be captured in transit, it cannot be considered fully secure.

Another necessary shift is moving away from one-time authentication. Traditional systems grant ongoing trust after a single successful login. In contrast, modern security models rely on continuous verification, where user behavior is assessed throughout a session. Indicators such as unusual file access, sudden geographic changes, or inconsistencies in typing patterns can reveal compromise even after initial authentication.

Help desk operations have also emerged as a growing vulnerability. Advances in AI-driven voice synthesis now allow attackers to convincingly impersonate employees during account recovery requests. A simple “forgot password” call can become an entry point if verification processes are weak. Strengthening these processes through additional identity checks outside standard channels is becoming essential.

Organizations must also address the issue of identity sprawl. Over time, systems accumulate unused accounts, third-party integrations, and service credentials that may not follow standard security controls. Many of these accounts rely on static credentials, bypass multi-factor authentication, and are rarely updated. Conducting regular audits, enforcing least-privilege access, and assigning clear ownership and expiration policies to each account can exponentially reduce exposure.

When a credential is identified as compromised, the response must be immediate and comprehensive. This goes beyond simply changing a password. Security teams should review all activity associated with that identity, particularly within the preceding 48 hours, to determine whether unauthorized actions have already occurred. A valid login should be treated with the same level of urgency as any confirmed malware incident.

The growing reliance on credential-based attacks reflects a deliberate turn by adversaries toward methods that are efficient, scalable, and difficult to detect. These attacks exploit trust rather than technical weaknesses, allowing them to bypass even the most robust perimeter defenses.

If organizations continue to treat identity as a one-time checkpoint rather than an ongoing signal, they risk overlooking early indicators of compromise. Strengthening identity-focused defenses and adopting continuous verification models will be critical. Without this shift, breaches will continue to occur in ways that appear indistinguishable from everyday business activity, making them harder to detect until the damage has already been done.

DARWIS Taka: A Web Vulnerability Scanner with AI-Powered Validation

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Vulnerability Scanner

Artificial Intelligence DARWIS darwis taka Vulnerability Vulnerability Scanner

DARWIS Taka: A Web Vulnerability Scanner with AI-Powered Validation

DARWIS Taka, a new web vulnerability scanner, is now available for free and runs via Docker. It pairs a rules-based scanning engine with an optional AI layer that reviews each finding before it reaches the report, aimed squarely at the false-positive problem that has dogged vulnerability scanning for years.

Built in Rust, Taka ships with 88 detection rules across 29 categories covering common web vulnerabilities, and produces JSON or self-contained HTML reports. Setup instructions, the Docker configuration, and documentation are published on GitHub at github.com/CSPF-Founder/taka-docker.

Two modes of AI validation

Taka's AI layer runs in one of two modes. In passive (evidence-analysis) mode, the model reviews the data the scanner already collected and returns a verdict without sending any further traffic to the target. In active mode, the AI acts as a second-stage tester: it proposes a small number of targeted follow-up requests, such as paired true and false payloads for a suspected SQL injection, Taka executes them, and the responses are fed back to the AI for differential analysis. Active mode is more decisive on borderline findings but generates additional traffic.

In both modes, every result is tagged with a verdict (confirmed, likely false positive, or inconclusive), a confidence score, and the AI's written reasoning. The report surfaces those labels alongside a summary of how many findings fell into each bucket. Nothing is dropped silently, so reviewers see what the AI believed and why, and can focus triage on the findings marked confirmed.

The validation layer currently supports Anthropic and OpenAI. The project team has tested Taka extensively with Anthropic's Claude Sonnet, which gave the best balance of reasoning quality and speed in their evaluation, and recommends it for the strongest results. AI validation is optional; without a key, Taka runs as a standard scanner with its own false-positive controls.

Scoring by evidence, not by single matches

Most scanners trigger on the first matcher that fires, which is why a single stray string in a response can produce a flood of bogus alerts. Taka uses a weighted scoring system instead. Each matcher in a rule, whether a status code, a regex, a header check, or a timing comparison, carries an integer weight reflecting how strong a signal it is. The rule declares a detection threshold, and a finding is raised only when the combined weight of the matchers that fired meets or exceeds that threshold.

Built to run against real systems

A circuit breaker halts scanning against hosts showing signs of distress, per-host rate limiting caps concurrent requests, and a passive mode disables all attack payloads for environments where only non-intrusive checks are acceptable. Three scan depth levels (quick, standard, deep) trade coverage against runtime, while a two-phase execution model keeps time-based blind rules from interfering with the rest of the scan.

A web interface ships with the tool for launching scans, inspecting findings alongside the raw evidence, and revisiting results.

Only the optional AI validation requires a third-party API key, supplied by the user. Taka is aimed at security engineers, penetration testers, bug bounty hunters, DevSecOps teams, and developers who want a scanner that respects their triage time.

Full setup instructions are available at github.com/CSPF-Founder/taka-docker.

Google Expands Gemini in Gmail, Forcing Billions to Reconsider Privacy, Control, and AI Dependence

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Privacy

AI integration Artificial Intelligence Email Gemini Gmail Google Privacy

Google Expands Gemini in Gmail, Forcing Billions to Reconsider Privacy, Control, and AI Dependence

Google has introduced one of the most extensive updates to Gmail in its history, warning that the scale of change driven by artificial intelligence may feel overwhelming for users. While some discussions have focused on surface-level changes such as switching email addresses, the company has emphasized that the real transformation lies in how AI is now embedded into everyday tools used by nearly two billion people. This shift requires far more serious attention.

At the center of this evolution is Gemini, Google’s artificial intelligence system, which is being integrated more deeply into Gmail and other core services. In a recent update shared through a short video message, Gmail’s product leadership acknowledged that the rapid pace of AI innovation can leave users feeling overloaded, with too many new features and decisions emerging at once.

Gmail has traditionally been built around convenience, scale, and seamless integration rather than strict privacy-first principles. Although its spam filters and malware detection systems are widely used and generally effective, they are not flawless. Importantly, Gmail has not typically been the platform users turn to for strong privacy assurances.

The introduction of Gemini changes this bbalance substantially. Google has clarified that it does not use email content to train its AI models. However, the way these tools function introduces new concerns. Features that automatically draft emails, summarize conversations, or search inbox content require access to emails that may contain highly sensitive personal or professional information.

To address this, Google describes Gemini as a temporary assistant that operates within a limited session. The company compares this interaction to allowing a helper into a private room containing your inbox. The assistant completes its task and then exits, with the accessed information disappearing afterward. According to Google, Gemini does not retain or learn from the data it processes during these interactions.

Despite these assurances, concerns remain. Even if the data is not stored long term, granting a cloud-based AI system access to private communications introduces an inherent level of risk. Additionally, while Google has denied automatically enrolling users into AI training programs, many of these AI-powered features are expected to be enabled by default. This shifts responsibility to users, who must actively decide how much access they are willing to allow.

This is not a decision that can be ignored. Once AI tools become integrated into daily workflows, they are difficult to remove. Relying on default settings or delaying action could result in long-term dependence on systems that users may not fully understand or control.

Shortly after promoting these updates, Gmail experienced a disruption that affected its core functionality. Users reported delays in sending and receiving emails, and Google acknowledged the issue while working on a fix. Initially, no estimated resolution time was provided. Later the same day, the company confirmed that the issue had been resolved.

According to Google’s official status update, the disruption was fixed on April 8, 2026, at 14:49 PDT. The cause was identified as a “noisy neighbor,” a term used in cloud computing to describe a situation where one service consumes excessive shared resources, negatively impacting the performance of others operating on the same infrastructure.

With a user base of approximately two billion, even a short-lived outage becomes of grave concern. More importantly, it emphasises the scale at which Gmail operates and reinforces why decisions around AI integration are critical for users worldwide.

The central issue now facing users is the balance between convenience and security. Google presents Gemini as a helpful and well-behaved assistant that enhances productivity without overstepping boundaries. However, like any guest given access to a private space, it requires clear rules and careful oversight.

This tension becomes even more visible when considering Google’s parallel efforts to strengthen security. The company recently expanded client-side encryption for Gmail on mobile devices. While this may sound similar to end-to-end encryption used in messaging apps, it is not the same. This form of encryption operates at an organizational level, primarily for enterprise users, and does not provide the same device-specific privacy protections commonly associated with true end-to-end encryption.

More critically, enabling this additional layer of encryption dynamically limits Gmail’s functionality. When it is turned on, several features become unavailable. Users can no longer use confidential mode, access delegated accounts, apply advanced email layouts, or send bulk emails using multi-send options. Features such as suggested meeting times, pop-out or full-screen compose windows, and sending emails to group recipients are also disabled.

In addition, personalization and usability tools are affected. Email signatures, emojis, and printing functions stop working. AI-powered tools, including Google’s intelligent writing and assistance features, are also unavailable. Other smart Gmail features are disabled, and certain mobile capabilities, such as screen recording and taking screenshots on Android devices, are restricted.

These limitations exist because encrypted data cannot be accessed by AI systems. As a result, users are forced to choose between stronger data protection and access to advanced features. The same mechanisms that secure information also prevent AI tools from functioning effectively.

This reflects a bigger challenge across the technology industry. Privacy and security measures often limit the capabilities of AI systems, which depend on access to data to operate. In Gmail’s case, these two priorities do not align easily and, in many ways, directly conflict.

From a wider perspective, this also highlights a fundamental limitation of email itself. The technology was developed in an earlier era and was not designed to handle modern cybersecurity threats. Its underlying structure lacks the robust protections found in newer communication platforms.

As artificial intelligence becomes more deeply integrated into everyday tools, users are being asked to make more informed and deliberate decisions about how their data is used. While Google presents Gemini as a controlled and temporary assistant, the responsibility ultimately lies with users to determine their comfort level.

For highly sensitive communication, relying solely on email may no longer be the safest option. Exploring alternative platforms with stronger built-in security may be necessary. Ultimately, this moment represents a critical choice: whether the convenience offered by AI is worth the level of access it requires.

Dutch Court Issues Order Against X and Grok Over Sexual Abuse Content

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xAI

Artificial Intelligence data compliance Data Regulation Digital Harrasment GDPR Grok Netherlands Technology xAI

Dutch Court Issues Order Against X and Grok Over Sexual Abuse Content

A court in the Netherlands has taken strict action against the platform X and its artificial intelligence system Grok, directing both to stop enabling the creation of sexually explicit images generated without consent, as well as any material involving minors. The ruling carries a financial penalty of €100,000 per day for each entity if they fail to follow the court’s instructions.

This decision, delivered by the Amsterdam District Court, marks a pivotal legal development. It is the first time in Europe that a judge has formally imposed restrictions on an AI-powered image generation tool over the production of abusive or non-consensual sexual content.

The legal complaint was filed by Offlimits together with Fonds Slachtofferhulp. Both groups argued that the pace of regulatory enforcement had not kept up with the speed at which harm was being caused. Existing Dutch legislation already makes it illegal to create or share manipulated nude images of individuals without their permission. However, concerns intensified after Grok introduced an image-editing capability toward the end of December 2025, which led to a sharp increase in reported incidents. On February 4, 2026, Offlimits formally contacted xAI and X, demanding that the feature be withdrawn.

In its ruling, the court instructed xAI to immediately halt the production and distribution of sexualized images involving individuals living in the Netherlands unless clear consent has been obtained. It also ordered the company to stop generating or displaying any content that falls under the legal definition of child sexual abuse material. Alongside this, X Corp and X Internet Unlimited Company have been required to suspend Grok’s functionality on the platform for as long as these violations continue.

Legal representatives for Offlimits emphasized that the so-called “undressing” feature cannot remain active anywhere in the world, not just within Dutch borders. The court further instructed xAI to submit written confirmation explaining the steps taken to comply. If this confirmation is not provided, the daily financial penalty will continue to apply.

Doubts Over Safeguards

A central question for the court was whether the companies had actually made it impossible for such content to be created, as they claimed. The judges concluded that this had not been convincingly demonstrated.

During a hearing on March 12, lawyers representing xAI argued that strong safeguards had been implemented starting January 20, 2026. They maintained that Grok no longer allowed the generation of non-consensual intimate imagery or content involving minors.

However, evidence presented by Offlimits challenged that claim. On March 9, the same day the companies denied any remaining risk, it was still possible to produce a sexualized video of a real person using only a single uploaded image. The system did not require any confirmation of consent. The court viewed this as a contradiction that cast doubt on the effectiveness of the safeguards.

The judges also pointed out inconsistency in xAI’s position regarding child sexual abuse material. The company argued both that such content could not be generated and that it was not technically possible to guarantee complete prevention.

Legal Responsibility and Framework

The court determined that creating non-consensual “undressing” images amounts to a violation of the General Data Protection Regulation. It also found that enabling the production of child sexual abuse material constitutes unlawful behavior under Dutch civil law.

Importantly, the court rejected the argument that responsibility should fall solely on users who input prompts. Instead, it concluded that the platform itself, which controls how the system functions, must take responsibility for preventing misuse.

This reasoning aligns with the Russmedia judgment issued by the Court of Justice of the European Union. That earlier ruling established that platforms can be treated as joint controllers of personal data and cannot rely on intermediary protections to avoid obligations under European data protection law. Applying this principle, the Dutch court found that xAI and X’s European entity are responsible for how personal data is processed within Grok’s image generation system.

The court went a step further by highlighting a key distinction. Unlike platforms that merely host user-generated content, Grok actively creates the material itself. Because xAI designed and operates the system, it was identified as the party responsible for preventing unlawful outputs, regardless of who initiates the request.

Jurisdictional Limits

The ruling applies differently across entities. X Corp, which is based in the United States, faces narrower restrictions because it does not directly provide services within the Netherlands. Its obligation is limited to suspending Grok’s functionality in relation to non-consensual imagery.

By contrast, X Internet Unlimited Company, which serves users within the European Union, must comply with both the ban on non-consensual sexualized content and the restrictions related to child abuse material.

Increasing Global Scrutiny

The case follows findings from the Center for Countering Digital Hate, which estimated that Grok generated around 3 million sexualized images within a ten-day period between late December 2025 and early January 2026. Approximately 23,000 of those images appeared to involve minors.

Regulatory pressure is also building internationally. Ireland’s Data Protection Commission has launched an investigation under GDPR rules, while the European Commission has opened proceedings under the Digital Services Act. In the United Kingdom, Ofcom has initiated action under its Online Safety framework. In the United States, legal challenges have also emerged, including lawsuits filed by teenagers in Tennessee and by the city of Baltimore.

At the policy level, the European Parliament has supported efforts to strengthen the AI Act by introducing an explicit ban on tools designed to digitally remove clothing from images.

A Turning Point for AI Accountability

Authorities are revising how they approach artificial intelligence systems. Earlier debates often treated platforms as passive intermediaries. However, systems like Grok actively generate content, which changes the question of responsibility.

The decision makes it clear that companies developing such technologies are expected to take active steps to prevent harm. Claims about technical limitations are unlikely to be accepted if evidence shows that misuse remains possible.

X and xAI have been given ten working days to provide written confirmation explaining how they have complied with the court’s order.

Chinese Tech Leaders See 66 Billion Erased as AI Pressures Intensify

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Threat Intelligence

AI governance Artificial Intelligence Cloud Security cybersecurity risks Digital Infrastructure emerging technologies Privacy Risk Management Threat Intelligence

Chinese Tech Leaders See 66 Billion Erased as AI Pressures Intensify

Throughout the past year, artificial intelligence has served more as a compelling narrative than a defined revenue stream – one that has steadily inflated expectations across global technology markets. As Alibaba Group Holdings Ltd and Tencent Holdings Ltd encountered an unexpected turn, the narrative was brought to an end.

During a single trading day, the combined market value of the companies declined by approximately $66 billion. There was no single operational error responsible for the abrupt reversal, but a growing sense of unease among investors who had aggressively positioned themselves to benefit from AI-driven profitability. However, they were instead faced with strategic ambiguity.

In spite of significant advancements and high-profile commitments to artificial intelligence, both companies have not been able to articulate a credible and concrete path for monetization despite significant advances and high-profile commitments.

A market reaction like this point to a broader shift in sentiment that suggests the era of rewarding ambition alone has given way to a more rigorous focus on execution, clarity, and measurable results in the rapidly evolving field of artificial intelligence. In spite of the pressure on fundamentals, the market’s skepticism has only grown.

Alibaba Group Holdings Ltd. reported a significant 67% contraction in net income in its latest quarterly results, reflecting a convergence of structural and strategic strains rather than a single disruption. In a time when underlying consumer demand remains uneven, the increased capital allocation towards artificial intelligence, including compute infrastructure, model development, and ecosystem expansion, is beginning to affect margins materially.

As a result of this dual burden, the company’s near-term profitability profile has been complicated, which reinforces analyst concerns that sentiment will not stabilize unless AI can be demonstrated to generate incremental, recurring revenue streams. Added to this, Alibaba has announced plans to invest over $53 billion in infrastructure, along with an aspirational target of generating $100 billion in combined cloud and AI revenues within five years.

Although this indicates scale, it lacks specificity. As a result of the absence of defined timelines, product roadmaps, and monetization mechanisms, markets are becoming increasingly reluctant to discount the degree of uncertainty created. It appears that investors are recalibrating their tolerance of long-term payoffs in a capital-intensive industry that is inherently back-loaded, putting more emphasis on visibility of execution and measurable milestones rather than long-term payoffs.

Without such alignment, the company's narrative on AI could be perceived as more of a budgetary expenditure cycle rather than a growth engine, further anchoring cautious sentiment. Tencent Holdings Ltd.'s market movements across China's technology sector demonstrate the rapid shift from optimism to recalibration.

Several days after the company's market value was eroded by approximately $43 billion in one trading session, Alibaba Group Holdings Ltd. recovered. In addition to an additional $23 billion decline in its US-listed stock, its Hong Kong-listed stock also suffered a 7.3% decline. It would appear that these movements echo a broader re-evaluation of valuation assumptions that had been boosted by heightened expectations regarding artificial intelligence-driven growth, until recently.

Among the factors contributing to this reversal are the rapid unwinding of the speculative surge that occurred earlier in the month, sparked by the viral adoption of OpenClaw, an agentic artificial intelligence platform that captured public imagination with its promises of automating mundane, time-consuming tasks such as managing emails and coordinating travel arrangements.

Following the Lunar New Year, consumers' enthusiasm increased following the holiday season, resulting in an acceleration in product releases across the sector. Emerging players, such as MiniMax Group Inc., and established incumbents, such as Baidu Inc., introduced competing products and services rapidly, reinforcing the narrative of imminent transformation based on artificial intelligence.

Tencent's shares soared by over 10% during this period as investor enthusiasm surrounded its own OpenClaw-related initiatives propelled its share price. However, as initial excitement faded, it became increasingly apparent that the rapid proliferation of products was not consistent with clearly defined monetization pathways.

Markets seem to be beginning to differentiate between technological momentum and sustainable economic value as a consequence of the pullback, an inflection point which continues to influence the trajectory of China's leading technology companies within an ever-evolving artificial intelligence environment.

As a result of the intense competition underpinning China’s AI expansion, the investment narrative has been further complicated. In addition to emerging companies such as MiniMax Group Inc., there are established incumbents such as Baidu Inc.

As a result of the surge in demand, Tencent Holdings Ltd. was the fastest company to roll out AI-based services and applications. With its extensive user database and its control over a vast digital ecosystem, WeChat emerges as a perceived structural beneficiary. Such positioning is widely considered advantageous in the development of agentic AI systems, which rely heavily on access to granular user-level data, such as communication patterns and behavioral signals, to achieve optimal performance.

Although these inherent advantages exist, investor confidence has been tempered by a lack of operational clarity, despite these inherent advantages. Tencent's management did not articulate specific monetization frameworks, capital allocation thresholds, or product roadmaps in the post-earnings discussions that could translate its ecosystem strengths into scalable revenue streams after earnings.

Consequently, institutional sentiment has been influenced by the lack of detail, which has prompted valuation models to be recalibrated. A significant downward revision was made by Morgan Stanley, which cited expectations that front-loaded AI investments will continue to put pressure on margins, with profit growth likely to trail revenue growth in the medium term.

Similarly, Alibaba Group Holding Ltd. is experiencing a parallel dynamic, where strategic imperatives to lead artificial general intelligence development are increasingly intertwining with operational challenges. It has been aggressively deploying capital in order to position itself at the forefront of China's artificial intelligence race, committed to committing more than $53 billion to infrastructure and aiming to generate $100 billion in cloud and AI revenues within the next five years.

However, it is also experiencing a deceleration in its traditional e-commerce segment as domestic competition intensifies. The company has responded to this by operationalizing aspects of its artificial intelligence portfolio, which have included the introduction of enterprise-focused agentic solutions, such as Wukong, as well as pricing adjustments across its cloud and storage services, resulting in a 34% increase in cloud and storage prices. However, escalating costs remain a barrier to sustainable returns.

The recent Lunar New Year period has seen major technology firms, including Alibaba, Tencent, ByteDance Ltd., and Baidu, engage in aggressive user acquisition campaigns, distributing billions of dollars in subsidies and incentives in order to stimulate adoption of consumer-facing AI software.

Although such measures have contributed to short-term engagement gains, they also indicate a trend in which customer acquisition and retention are being subsidized at scale, raising questions about the longevity of unit economics.

In light of the increasing capital intensity across both infrastructure and user growth fronts, it is becoming increasingly necessary for the sector to exercise discipline and demonstrate tangible financial results in order to transition from experimentation to monetization. A key objective of this episode is not to collapse the AI thesis, but rather to reevaluate the way in which its value is assessed and realized.

A transition from capability building to disciplined commercialization will likely be required for China's leading technology firms in the future, where technical innovation is closely coupled with viable business models and measurable financial outcomes. The investor community is increasingly focused on metrics such as revenue attribution from artificial intelligence services, margin resilience as computing costs rise, and the scalability of enterprise-focused and consumer-facing deployments.

The importance of strategic clarity will be as strong as technological leadership in this environment. As a result of transparent investment timelines, product differentiation, and sustainable unit economics, companies that are able to articulate coherent monetization frameworks are more apt to restore confidence and justify continued capital inflows.

As global markets adopt a more selective approach to AI-driven growth narratives, prolonged ambiguity is also likely to extend valuation pressure. Thus, the future will not be determined solely by innovation pace, but also by the ability of the industry to convert its innovations into durable, repeatable sources of value for the industry as a whole.

Cybersecurity Faces New Threats from AI and Quantum Tech

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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.

Government Remains Primary Target as Cyberattacks Grow in 2025

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Artificial Intelligence Cyber Attacks Government Telecommunication Telegram

Government Remains Primary Target as Cyberattacks Grow in 2025

Government institutions were the most heavily targeted sector in 2025, according to newly published research from HPE Threat Labs, which documented 1,186 active cyberattack campaigns throughout the year. The dataset reflects activity tracked between January 1 and December 31, 2025, and spans a wide range of industries and attack techniques, offering a broad view of how threat actors are operating at scale.

Out of all industries analyzed, government bodies accounted for the largest share, with 274 recorded campaigns. The financial services sector followed with 211, while technology companies experienced 179 campaigns. Defense-related organizations were targeted in 98 cases, and manufacturing entities saw 75. Telecommunications and healthcare sectors each registered 63 campaigns, while education and transportation sectors reported 61 incidents each. The distribution shows a clear trend: attackers are prioritizing sectors responsible for sensitive information, essential services, and large operational systems.

Researchers also observed a growing reliance on automation and artificial intelligence to accelerate cyber operations. Some threat groups have adopted highly organized workflows resembling production lines, enabling faster execution of attacks. These operations are often coordinated through platforms such as Telegram, where attackers can manage tasks and extract compromised data in real time.

In addition to automation, generative artificial intelligence is being actively used to enhance social engineering techniques. Cybercriminals are now creating synthetic voice recordings and deepfake videos to carry out vishing attacks and impersonate senior executives with greater credibility. In one identified case, an extortion group conducted detailed research into vulnerabilities in virtual private networks, allowing them to refine and improve their methods of gaining unauthorized access.

When examining the types of threats, ransomware emerged as the most prevalent, making up 22 percent of all campaigns. Infostealer malware followed at 19 percent, with phishing attacks accounting for 17 percent. Remote Access Trojans represented 11 percent, while other forms of malware comprised 9 percent of the total activity.

The scale of malicious infrastructure uncovered during the analysis further underscores the intensity of the threat environment. Investigators identified 147,087 harmful domains and 65,464 malicious URLs. In addition, 57,956 malicious files and 47,760 IP addresses were linked to cybercriminal operations. Over the course of the year, attackers exploited 549 distinct software vulnerabilities.

Insights from a global deception network revealed 44.5 million connection attempts originating from 372,800 unique IP addresses. Among these, 36,600 requests matched known attack signatures and were traced to 8,200 distinct source IPs targeting five specific destination systems.

A closer examination of attack patterns shows that cybercriminals frequently focus on exposed systems and known weaknesses. Remote code execution vulnerabilities in digital video recorders were triggered approximately 4,700 times. Exploitation attempts targeting Huawei routers were observed 3,490 times, while misuse of Docker application programming interfaces occurred in about 3,400 cases.

Other commonly exploited weaknesses included command injection vulnerabilities in PHPUnit and TP-Link systems, each recorded around 3,100 times. Printer-related enumeration attacks using Internet Printing Protocol, along with Realtek UPnP exploitation, were each observed roughly 2,700 times.

The vulnerabilities most frequently targeted during these campaigns included CVE-2017-17215, CVE-2023-1389, CVE-2014-8361, CVE-2017-9841, and CVE-2023-26801, all of which have been widely documented and continue to be exploited in systems that remain unpatched.

Beyond the raw data, the findings reflect a dynamic development in cybercrime. Attackers are combining automation, artificial intelligence, and well-known vulnerabilities to increase both the speed and scale of their operations. This shift reduces the time required to identify targets, exploit weaknesses, and generate impact, making modern cyberattacks more efficient and harder to contain.

The report points to the crucial need for organizations to strengthen their defenses by continuously monitoring systems, addressing known vulnerabilities, and adapting to rapidly evolving threat techniques. As attackers continue to refine their methods, proactive security measures are becoming essential to limit exposure and reduce risk across all sectors.

Cyber Operations Expand as Iran Conflict Extends into Digital Warfare

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Iran

APT Group Artificial Intelligence CISA Cyber Attacks Cyber Breach Hacktivism Iran

Cyber Operations Expand as Iran Conflict Extends into Digital Warfare

Cyberattacks are increasingly being used alongside conventional military actions in the ongoing conflict involving Iran, with both state-linked actors and loosely organised hacker groups targeting systems in the United States and Israel.

A recent incident involving Stryker illustrates the scale of this activity. On March 11, the company confirmed that a cyberattack had disrupted parts of its global network. Employees across several offices reportedly encountered login screens displaying the symbol of Handala, a group believed to have links to Iran. The attack affected systems within Microsoft’s environment, although the full extent of the disruption and the timeline for recovery remain unclear.

Handala has claimed responsibility for the operation, stating that it exploited Microsoft’s cloud-based device management platform, Intune. According to data from SOCRadar, the group alleged it remotely wiped more than 200,000 devices across 79 countries. These claims have not been independently verified, and attempts have been made to seek confirmation from Microsoft. The group described the attack as retaliation for a missile strike in Minab, Iran, which reportedly killed more than 160 people at a girls’ school.

This breach is part of a broader surge in cyber activity following Operation Epic Fury, with multiple pro-Iranian actors directing attacks against American and Israeli systems.

State-linked groups target essential systems

A cybersecurity assessment indicates that several groups associated with Iran’s Islamic Revolutionary Guard Corps, including CyberAv3ngers, APT33, and APT55, are actively targeting critical infrastructure in the United States.

These operations focus on industrial control systems, which are specialised computers used to manage essential services such as electricity grids, water treatment plants, and manufacturing processes. In some instances, attackers have gained access by using unchanged default passwords, allowing them to install malicious software capable of interfering with or taking control of these systems.

CyberAv3ngers has reportedly accessed industrial machinery in this way, while APT33 has used commonly reused passwords to infiltrate accounts at US energy companies. After gaining entry, the group attempts to weaken safety mechanisms by inserting malware into operational systems. APT55, meanwhile, has focused on cyber-espionage, targeting individuals connected to the energy and defence sectors to gather intelligence for Iranian operations.

Other groups linked to Iran’s Ministry of Intelligence and Security, including MuddyWater and APT34, are also involved in these campaigns. MuddyWater has targeted telecommunications providers, oil and gas companies, and government organisations. It functions as an initial access broker, meaning it breaks into networks, collects login credentials, and then passes that access to other attackers.

Handala has also claimed additional operations beyond the Stryker incident. These include deleting more than 40 terabytes of data from servers at the Hebrew University of Jerusalem and breaching systems linked to Verifone in Israel. However, Verifone has stated that it found no evidence of any compromise or service disruption.

Cyber operations are also being carried out by the United States and Israel.

General Dan Caine stated on March 2 that US Cyber Command was one of the first operational units involved in Operation Epic Fury. He said these efforts disrupted Iran’s communication and sensor networks, leaving it with reduced ability to monitor, coordinate, or respond effectively. He did not provide further operational details.

On March 13, Pete Hegseth confirmed that the United States is using artificial intelligence alongside cyber tools as part of its military approach in the conflict.

Separate reporting suggests that Israeli intelligence agencies may have used data obtained from compromised traffic cameras across Tehran to support planning related to Iran’s leadership, including Ayatollah Ali Khamenei.

Hacktivist networks operate with fewer constraints

Alongside state-backed actors, hacktivist groups have played a significant role. More than 60 such groups reportedly mobilised in the early hours of Operation Epic Fury, forming a coalition known as the Cyber Islamic Resistance.

This network coordinates its activity through Telegram channels described as an “Electronic Operations Room.” Unlike state-directed groups, these actors operate based on ideological motivations rather than central command structures. Analysts note that such groups tend to be less disciplined, more unpredictable, and more likely to act without regard for civilian impact.

Within the first two weeks of the conflict, the coalition claimed responsibility for more than 600 distinct cyber incidents across over 100 Telegram channels. These include attacks targeting Israeli defence-related systems, drone detection platforms such as VigilAir, and infrastructure affecting electricity and water services at a hotel in Tel Aviv.

The same group also claimed to have compromised BadeSaba Calendar, a widely used religious mobile application with more than five million downloads. During the incident, users reportedly received messages such as “Help is on the way” and “It’s time for reckoning,” based on screenshots shared online.

Some analysts assess that these groups may be using artificial intelligence tools to compensate for limited technical expertise, allowing them to scale operations more effectively.

Global actors join the conflict

Cyber intelligence findings suggest that participation in these operations is expanding geographically. Ongoing internet restrictions within Iran appear to be limiting the involvement of domestic hacktivists by disrupting Telegram-based coordination.

As a result, increased activity has been observed from pro-Iranian groups based in Southeast Asia, Pakistan, and other parts of the Middle East.

The Islamic Cyber Resistance in Iraq, also known as the 313 Team, has claimed responsibility for attacks on websites belonging to Kuwaiti government ministries, including defence-related institutions, according to a separate threat intelligence briefing. The group has also reportedly targeted websites in Romania and Bahrain.

Another group, DieNet, has claimed cyber operations affecting airport systems in Bahrain, Saudi Arabia, and the United Arab Emirates.

Russian-linked actors have also entered the landscape. NoName057(16), previously involved in cyber campaigns related to Ukraine, has launched distributed denial-of-service attacks, a technique used to overwhelm websites with traffic and render them inaccessible. Targets include Israeli municipal services, political platforms, telecommunications providers, and defence-related entities, including Elbit Systems, as noted by a threat intelligence monitoring platform.

The group is also reported to be collaborating with Hider-Nex, a North Africa-based collective that has claimed attacks on Kuwaiti government domains.

Some pro-Israeli hacktivist groups are active, including Anonymous Syria Hackers. One such group recently claimed to have breached an Iranian technology firm and released sensitive data, including account credentials, emails, and passwords.

However, these groups remain less visible. Analysts suggest that Israel primarily conducts cyber operations through state-controlled channels, reducing the role and visibility of independent actors. In addition, these groups often do not appear in alerts issued by agencies such as the US Cybersecurity and Infrastructure Security Agency, making their activities harder to track.

These developments suggest how cyber operations are becoming embedded in modern warfare. Such attacks are used not only to disrupt infrastructure but also to gather intelligence, impose financial strain, and influence perception.

The growing use of artificial intelligence, combined with the involvement of decentralised and ideologically driven groups, is making attribution more complex and the threat environment more difficult to manage. As a result, cyber capabilities are now a central component of how conflicts are conducted, extending the battlefield into digital systems that underpin everyday life.

Meta’s Smart Glasses Face Privacy Backlash as Experts Flag Legal and Ethical Risks

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Privacy

AI Data Governance Artificial Intelligence Data Regulation DPDP Act India Meta Privacy

Meta’s Smart Glasses Face Privacy Backlash as Experts Flag Legal and Ethical Risks

A whirlwind of concerns around Meta’s AI-enabled smart glasses are intensifying after reports suggested that human reviewers may have accessed sensitive user recordings, raising broader questions about privacy, consent, and data protection.

Online discussions have surged, with users expressing alarm over how much data may be visible to the company. Some individuals on forums have claimed that recorded footage could be manually reviewed to train artificial intelligence systems, while others raised concerns about the use of such devices in sensitive environments like healthcare settings, where patient information could be unintentionally exposed.

What triggered the controversy?

The debate gained momentum following an investigation by Swedish media outlets, which reported that contractors working at external facilities were tasked with reviewing video recordings captured through Ray-Ban Meta Smart Glasses. According to these findings, some of the reviewed material included highly sensitive content.

The issue has since drawn regulatory attention in multiple regions. Authorities in the United Kingdom, including the Information Commissioner's Office, have sought clarification on how such user data is processed. In the United States, the controversy has also led to legal action against Meta Platforms, with allegations that consumers were not adequately informed about the device’s privacy safeguards.

The timing is of essence here, as smart glasses are rapidly gaining popularity. Legal filings suggest that more than seven million units were sold in 2025 alone. Unlike smartphones, these glasses resemble regular eyewear but can discreetly capture images, audio, and video from the wearer’s perspective, often without others being aware.

Why are experts concerned?

Legal analysts highlight that such practices could conflict with India’s Digital Personal Data Protection Act, 2023 if data involving Indian individuals is collected.

According to legal experts, consent remains a foundational requirement. Any access to recordings involving identifiable individuals must be based on informed approval. If footage is reviewed without the knowledge or permission of those captured, it could constitute a violation of Indian data protection law.

Beyond legality, specialists argue that wearable AI devices introduce a deeper structural issue. Unlike traditional data collection methods, these tools continuously capture real-world environments, making it difficult to define clear boundaries for data usage.

Experts also point out that although Meta includes visible indicators such as LED lights to signal recording, these measures do not fully address how the data of bystanders is processed. There are concerns about the absence of strict limitations on why such data is collected or how much of it is retained.

Additionally, outsourcing the review of user-generated content introduces further complications. Apart from the risk of misuse or unauthorized sharing, there are also ethical concerns regarding the working conditions and psychological impact on individuals tasked with reviewing potentially distressing material.

Cross-border and systemic risks

Another key concern is international data handling. If recordings involving Indian users are accessed by contractors located overseas, companies are still expected to maintain the same standards of security and confidentiality required under Indian regulations.

Experts emphasize that these devices are part of a much larger artificial intelligence ecosystem. Data captured through smart glasses is not simply stored. It may be uploaded to cloud servers, processed by machine learning systems, and in some cases, reviewed by humans to improve system performance. This creates a chain of data handling where highly personal information, including facial features, voices, surroundings, and behavioral patterns, may circulate beyond the user’s direct control.

What is Meta’s response?

Meta has stated that protecting user data remains a priority and that it continues to refine its systems to improve privacy protections. The company has explained that its smart glasses are designed to provide hands-free AI assistance, allowing users to interact with their surroundings more efficiently.

It also acknowledged that, in certain cases, human reviewers may be involved in evaluating shared content to enhance system performance. According to the company, such processes are governed by its privacy policies and include steps intended to safeguard user identity, such as automated filtering techniques like face blurring.

However, reports citing Swedish publications suggest that these safeguards may not always function consistently, with some instances where identifiable details remain visible.

While recording must be actively initiated by the user, either manually or through voice commands, experts note that many users may not fully understand that their captured content could be subject to human review.

The Ripple Effect

This controversy reflects a wider shift in how personal data is generated and processed in the age of AI-driven wearables. Unlike earlier technologies, smart glasses operate in real time and in shared environments, raising complex questions about consent not just for users, but for everyone around them.

As adoption runs rampant, regulators worldwide are likely to tighten scrutiny on such devices. The challenge for companies will be to balance innovation with transparent data practices, especially as public awareness around digital privacy continues to rise.

For users, this is a wake up call to not rely on new age technology blindly and take into account that convenience-driven technologies often come with hidden trade-offs, particularly when it comes to control over personal data.

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Two modes of AI validation

Scoring by evidence, not by single matches

Built to run against real systems

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Two modes of AI validation

Scoring by evidence, not by single matches

Built to run against real systems