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Anthropic Delays Claude Fable 5 Usage Credit Requirement Until July 19

A number of Anthropic's flagship AI model, Claude Fable 5, has been extended to eligible paid subscribers until July 19, 2026 for free access. This extension provides customers with another week of access while the company continues to expand its available computing capacity. This extension follows two previous extension of the deadline.

As part of their initial announcement, Anthropic announced that Fable 5 would be available to subscribers through July 7, but that offer has since been extended to July 12. According to Anthropic, promotional access to the Claude Code system will now be available until 11:59:59 PM PT on July 19. Along with this extension, Anthropic has also continued to increase Claude Code weekly usage limits by 50%.

The Fable 5 subscription model allows eligible subscribers to use up to 50% of their weekly allowance at no additional charge. It draws upon the same weekly usage pool as other Claude models, however Anthropic notes that Fable 5 consumes these limits more rapidly as a result of its greater computational requirements. When enabled by their organization, this promotion is available to Claude Pro, Max, Team, and premium seat-based Enterprise subscribers.

The promotion does not apply to Free users, standard Enterprise seats, usage-based Enterprise plans, or API customers. Anthropic's ecosystem includes Claude Web, Mobile, Desktop, Claude Code, Claude Cowork, Claude Design, Claude for Microsoft 365, and Claude Tag, among others. Users can choose "Fable 5" from the model picker on Claude's web, desktop and mobile applications in order to begin using the model.

For Claude Code, Fable 5 requires version 2.1.170 or later, while Claude Cowork users need the latest Claude Desktop application to access the feature. Versions 2.1.170 and later are required for Claude Code, while version 2.1.170 and higher are required for Claude Cowork. Upon reaching their complimentary Fable 5 allocation, users may elect to purchase usage credits to continue using the model or to switch to another Claude model that remains available under their current subscription limitations.

According to Anthropic, this process is consistent across all versions of Claude Web, Mobile, Desktop, Claude Work, and Claude Code. If a user exceeds the complimentary allocation for Fable 5, they may purchase usage credits, which are billed separately from their subscription, or choose to make use of another Claude model without incurring additional charges in accordance with their remaining plan limits.

In addition, Anthropic has assured its customers that current restrictions will only last for a short period of time. According to the company, Fable 5 will not be permanently removed from subscription plans and will be restored as soon as sufficient computing resources are available. It is evident that the demand for Claude Fable 5 continues to exceed the computational resources available to Anthropic.

Anthropic is continuing to expand its infrastructure while offering premium subscribers access to its most advanced AI model without immediate additional costs by extending its temporary promotion. Once sufficient computing capacity is available, Fable 5 will be available as a standard subscription benefit once adequate computing capacity has been reached.

Anthropic's latest extension reflects the increased demand for advanced generative AI models, as well as the challenges associated with rapid adoption of these models. While the temporary offer ensures continued access for eligible subscribers, it emphasizes the importance of scalable computing resources when AI companies attempt to strike a balance between innovation, performance, and user expectation.

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

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Technology

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.

Chinese Open AI Models Rival US Systems and Reshape Global Adoption

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Open AI

AI Models AI Systems AI technology AI tools ChatGPT China Cyber Security Open AI

Chinese Open AI Models Rival US Systems and Reshape Global Adoption

Chinese artificial intelligence models have rapidly narrowed the gap with leading US systems, reshaping the global AI landscape. Once considered followers, Chinese developers are now producing large language models that rival American counterparts in both performance and adoption. At the same time, China has taken a lead in model openness, a factor that is increasingly shaping how AI spreads worldwide.

This shift coincides with a change in strategy among major US firms. OpenAI, which initially emphasized transparency, moved toward a more closed and proprietary approach from 2022 onward. As access to US-developed models became more restricted, Chinese companies and research institutions expanded the availability of open-weight alternatives. A recent report from Stanford University’s Human-Centered AI Institute argues that AI leadership today depends not only on proprietary breakthroughs but also on reach, adoption, and the global influence of open models.

According to the report, Chinese models such as Alibaba’s Qwen family and systems from DeepSeek now perform at near state-of-the-art levels across major benchmarks. Researchers found these models to be statistically comparable to Anthropic’s Claude family and increasingly close to the most advanced offerings from OpenAI and Google. Independent indices, including LMArena and the Epoch Capabilities Index, show steady convergence rather than a clear performance divide between Chinese and US models.

Adoption trends further highlight this shift. Chinese models now dominate downstream usage on platforms such as Hugging Face, where developers share and adapt AI systems. By September 2025, Chinese fine-tuned or derivative models accounted for more than 60 percent of new releases on the platform. During the same period, Alibaba’s Qwen surpassed Meta’s Llama family to become the most downloaded large language model ecosystem, indicating strong global uptake beyond research settings.

This momentum is reinforced by a broader diffusion effect. As Meta reduces its role as a primary open-source AI provider and moves closer to a closed model, Chinese firms are filling the gap with freely available, high-performing systems. Stanford researchers note that developers in low- and middle-income countries are particularly likely to adopt Chinese models as an affordable alternative to building AI infrastructure from scratch. However, adoption is not limited to emerging markets, as US companies are also increasingly integrating Chinese open-weight models into products and workflows.

Paradoxically, US export restrictions limiting China’s access to advanced chips may have accelerated this progress. Constrained hardware access forced Chinese labs to focus on efficiency, resulting in models that deliver competitive performance with fewer resources. Researchers argue that this discipline has translated into meaningful technological gains.

Openness has played a critical role. While open-weight models do not disclose full training datasets, they offer significantly more flexibility than closed APIs. Chinese firms have begun releasing models under permissive licenses such as Apache 2.0 and MIT, allowing broad use and modification. Even companies that once favored proprietary approaches, including Baidu, have reversed course by releasing model weights.

Despite these advances, risks remain. Open-weight access does not fully resolve concerns about state influence, and many users rely on hosted services where data may fall under Chinese jurisdiction. Safety is another concern, as some evaluations suggest Chinese models may be more susceptible to jailbreaking than US counterparts.

Even with these caveats, the broader trend is clear. As performance converges and openness drives adoption, the dominance of US commercial AI providers is no longer assured. The Stanford report suggests China’s role in global AI will continue to expand, potentially reshaping access, governance, and reliance on artificial intelligence worldwide.

OpenAI Warns Future AI Models Could Increase Cybersecurity Risks and Defenses

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OpenAI

AI Model AI Models AI Systems ChatGPT ChatGPT. OpenAI Cyber Defenses Cyber Security cybersecurity risks OpenAI

OpenAI Warns Future AI Models Could Increase Cybersecurity Risks and Defenses

Meanwhile, OpenAI told the press that large language models will get to a level where future generations of these could pose a serious risk to cybersecurity. The company in its blog postingly admitted that powerful AI systems could eventually be used to craft sophisticated cyberattacks, such as developing previously unknown software vulnerabilities or aiding stealthy cyber-espionage operations against well-defended targets. Although this is still theoretical, OpenAI has underlined that the pace with which AI cyber-capability improvements are taking place demands proactive preparation.

The same advances that could make future models attractive for malicious use, according to the company, also offer significant opportunities to strengthen cyber defense. OpenAI said such progress in reasoning, code analysis, and automation has the potential to significantly enhance security teams' ability to identify weaknesses in systems better, audit complex software systems, and remediate vulnerabilities more effectively. Instead of framing the issue as a threat alone, the company cast the issue as a dual-use challenge-one in which adequate management through safeguards and responsible deployment would be required.

In the development of such advanced AI systems, OpenAI says it is investing heavily in defensive cybersecurity applications. This includes helping models improve particularly on tasks related to secure code review, vulnerability discovery, and patch validation. It also mentioned its effort on creating tooling supporting defenders in running critical workflows at scale, notably in environments where manual processes are slow or resource-intensive.

OpenAI identified several technical strategies that it thinks are critical to the mitigation of cyber risk associated with increased capabilities of AI systems: stronger access controls to restrict who has access to sensitive features, hardened infrastructure to prevent abuse, outbound data controls to reduce the risk of information leakage, and continuous monitoring to detect anomalous behavior. These altogether are aimed at reducing the likelihood that advanced capabilities could be leveraged for harmful purposes.

It also announced the forthcoming launch of a new program offering tiered access to additional cybersecurity-related AI capabilities. This is intended to ensure that researchers, enterprises, and security professionals working on legitimate defensive use cases have access to more advanced tooling while providing appropriate restrictions on higher-risk functionality. Specific timelines were not discussed by OpenAI, although it promised that more would be forthcoming very soon.

Meanwhile, OpenAI also announced that it would create a Frontier Risk Council comprising renowned cybersecurity experts and industry practitioners. Its initial mandate will lie in assessing the cyber-related risks that come with frontier AI models. But this is expected to expand beyond this in the near future. Its members will be required to offer advice on the question of where the line should fall between developing capability responsibly and possible misuse. And its input would keep informing future safeguards and evaluation frameworks.

OpenAI also emphasized that the risks of AI-enabled cyber misuse have no single-company or single-platform constraint. Any sophisticated model, across the industry, it said, may be misused if there are no proper controls. To that effect, OpenAI said it continues to collaborate with peers through initiatives such as the Frontier Model Forum, sharing threat modeling insights and best practices.

By recognizing how AI capabilities could be weaponized and where the points of intervention may lie, the company believes, the industry will go a long way toward balancing innovation and security as AI systems continue to evolve.

Genesis Mission Launches as US Builds Closed-Loop AI System Linking National Laboratories

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DOE

AI Advancements AI cybersecurity AI Models AI Systems AI technology critical infrastructures Cyber Security DOE

Genesis Mission Launches as US Builds Closed-Loop AI System Linking National Laboratories

The United States has announced a major federal scientific initiative known as the Genesis Mission, framed by the administration as a transformational leap forward in how national research will be conducted. Revealed on November 24, 2025, the mission is described by the White House as the most ambitious federal science effort since the Manhattan Project. The accompanying executive order tasks the Department of Energy with creating an interconnected “closed-loop AI experimentation platform” that will join the nation’s supercomputers, 17 national laboratories, and decades of research datasets into one integrated system.

Federal statements position the initiative as a way to speed scientific breakthroughs in areas such as quantum engineering, fusion, advanced semiconductors, biotechnology, and critical materials. DOE has called the system “the most complex scientific instrument ever built,” describing it as a mechanism designed to double research productivity by linking experiment automation, data processing, and AI models into a single continuous pipeline. The executive order requires DOE to progress rapidly, outlining milestones across the next nine months that include cataloging datasets, mapping computing capacity, and demonstrating early functionality for at least one scientific challenge.

The Genesis Mission will not operate solely as a federal project. DOE’s launch materials confirm that the platform is being developed alongside a broad coalition of private, academic, nonprofit, cloud, and industrial partners. The roster includes major technology companies such as Microsoft, Google, OpenAI for Government, NVIDIA, AWS, Anthropic, Dell Technologies, IBM, and HPE, alongside aerospace companies, semiconductor firms, and energy providers. Their involvement signals that Genesis is designed not only to modernize public research, but also to serve as part of a broader industrial and national capability.

However, key details remain unclear. The administration has not provided a cost estimate, funding breakdown, or explanation of how platform access will be structured. Major news organizations have already noted that the order contains no explicit budget allocation, meaning future appropriations or resource repurposing will determine implementation. This absence has sparked debate across the AI research community, particularly among smaller labs and industry observers who worry that the platform could indirectly benefit large frontier-model developers facing high computational costs.

The order also lays the groundwork for standardized intellectual-property agreements, data governance rules, commercialization pathways, and security requirements—signaling a tightly controlled environment rather than an open-access scientific commons. Certain community reactions highlight how the initiative could reshape debates around open-source AI, public research access, and the balance of federal and private influence in high-performance computing. While its long-term shape is not yet clear, the Genesis Mission marks a pivotal shift in how the United States intends to organize, govern, and accelerate scientific advancement using artificial intelligence and national infrastructure.

Hacker Exploits AI Chatbot Claude in Unprecedented Cybercrime Operation

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Ransomwares

AI Chatbots AI Models AI Techniques Cyber Crime Cyber Extortion Cyber Hackers Data Extortion international cybercrime operation Ransomwares

Hacker Exploits AI Chatbot Claude in Unprecedented Cybercrime Operation

A hacker has carried out one of the most advanced AI-driven cybercrime operations ever documented, using Anthropic’s Claude chatbot to identify targets, steal sensitive data, and even draft extortion emails, according to a new report from the company.

It Anthropic disclosed that the attacker leveraged Claude Code — a version of its AI model designed for generating computer code — to assist in nearly every stage of the operation. The campaign targeted at least 17 organizations across industries including defense, finance, and healthcare, making it the most comprehensive example yet of artificial intelligence being exploited for cyber extortion.

Cyber extortion typically involves hackers stealing confidential data and demanding payment to prevent its release. AI has already played a role in such crimes, with chatbots being used to write phishing emails. However, Anthropic’s findings mark the first publicly confirmed case in which a mainstream AI model automated nearly the entire lifecycle of a cyberattack.

The hacker reportedly prompted Claude to scan for vulnerable companies, generate malicious code to infiltrate systems, and extract confidential files. The AI system then organized the stolen data, analyzed which documents carried the highest value, and suggested ransom amounts based on victims’ financial information. It also drafted extortion notes demanding bitcoin payments, which ranged from $75,000 to more than $500,000.

Jacob Klein, Anthropic’s head of threat intelligence, said the operation was likely conducted by a single actor outside the United States and unfolded over three months. “We have robust safeguards and multiple layers of defense for detecting this kind of misuse, but determined actors sometimes attempt to evade our systems through sophisticated techniques,” Klein explained.

The report revealed that stolen material included Social Security numbers, bank records, medical data, and files tied to sensitive defense projects regulated by the U.S. State Department. Anthropic did not disclose which companies were affected, nor did it confirm whether any ransom payments were made.

While the company declined to detail exactly how the hacker bypassed safeguards, it emphasized that additional protections have since been introduced. “We expect this model of cybercrime to become more common as AI lowers the barrier to entry for sophisticated operations,” Anthropic warned.

The case underscores growing concerns about the intersection of AI and cybersecurity. With the AI sector largely self-regulated in the U.S., experts fear similar incidents could accelerate unless stronger oversight and security standards are enforced.

Hackers Use DNS Records to Hide Malware and AI Prompt Injections

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Malware Attack

AI Models AI Prompt AI prompt injection attack Cyber Security cybercriminals DNS DNS attacks DNS Hacking DOH DoT Hacker attack Malware Attack

Hackers Use DNS Records to Hide Malware and AI Prompt Injections

Cybercriminals are increasingly leveraging an unexpected and largely unmonitored part of the internet’s infrastructure—the Domain Name System (DNS)—to hide malicious code and exploit security weaknesses. Security researchers at DomainTools have uncovered a campaign in which attackers embedded malware directly into DNS records, a method that helps them avoid traditional detection systems.

DNS records are typically used to translate website names into IP addresses, allowing users to access websites without memorizing numerical codes. However, they can also include TXT records, which are designed to hold arbitrary text. These records are often used for legitimate purposes, such as domain verification for services like Google Workspace. Unfortunately, they can also be misused to store and distribute malicious scripts.

In a recent case, attackers converted a binary file of the Joke Screenmate malware into hexadecimal code and split it into hundreds of fragments. These fragments were stored across multiple subdomains of a single domain, with each piece placed inside a TXT record. Once an attacker gains access to a system, they can quietly retrieve these fragments through DNS queries, reconstruct the binary code, and deploy the malware. Since DNS traffic often escapes close scrutiny—especially when encrypted via DNS over HTTPS (DOH) or DNS over TLS (DOT)—this method is particularly stealthy.

Ian Campbell, a senior security engineer at DomainTools, noted that even companies with their own internal DNS resolvers often struggle to distinguish between normal and suspicious DNS requests. The rise of encrypted DNS traffic only makes it harder to detect such activity, as the actual content of DNS queries remains hidden from most monitoring tools. This isn’t a new tactic. Security researchers have observed similar methods in the past, including the use of DNS records to host PowerShell scripts.

However, the specific use of hexadecimal-encoded binaries in TXT records, as described in DomainTools’ latest findings, adds a new layer of sophistication. Beyond malware, the research also revealed that TXT records are being used to launch prompt injection attacks against AI chatbots. These injections involve embedding deceptive or malicious prompts into files or documents processed by AI models.

In one instance, TXT records were found to contain commands instructing a chatbot to delete its training data, return nonsensical information, or ignore future instructions entirely. This discovery highlights how the DNS system—an essential but often overlooked component of the internet—can be weaponized in creative and potentially damaging ways.

As encryption becomes more widespread, organizations need to enhance their DNS monitoring capabilities and adopt more robust defensive strategies to close this blind spot before it’s further exploited.

Why Running AI Locally with an NPU Offers Better Privacy, Speed, and Reliability

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LLaMA

AI Chatbots AI Models AI technology Chatbots ChatGPT Cloud Cyber Security Data Privacy data security DeepSeek Gemini AI LLaMA

Why Running AI Locally with an NPU Offers Better Privacy, Speed, and Reliability

Running AI applications locally offers a compelling alternative to relying on cloud-based chatbots like ChatGPT, Gemini, or Deepseek, especially for those concerned about data privacy, internet dependency, and speed. Though cloud services promise protections through subscription terms, the reality remains uncertain. In contrast, using AI locally means your data never leaves your device, which is particularly advantageous for professionals handling sensitive customer information or individuals wary of sharing personal data with third parties.

Local AI eliminates the need for a constant, high-speed internet connection. This reliable offline capability means that even in areas with spotty coverage or during network outages, tools for voice control, image recognition, and text generation remain functional. Lower latency also translates to near-instantaneous responses, unlike cloud AI that may lag due to network round-trip times.

A powerful hardware component is essential here: the Neural Processing Unit (NPU). Typical CPUs and GPUs can struggle with AI workloads like large language models and image processing, leading to slowdowns, heat, noise, and shortened battery life. NPUs are specifically designed for handling matrix-heavy computations—vital for AI—and they allow these models to run efficiently right on your laptop, without burdening the main processor.

Currently, consumer devices such as Intel Core Ultra, Qualcomm Snapdragon X Elite, and Apple’s M-series chips (M1–M4) come equipped with NPUs built for this purpose. With one of these devices, you can run open-source AI models like DeepSeek‑R1, Qwen 3, or LLaMA 3.3 using tools such as Ollama, which supports Windows, macOS, and Linux. By pairing Ollama with a user-friendly interface like OpenWeb UI, you can replicate the experience of cloud chatbots entirely offline.

Other local tools like GPT4All and Jan.ai also provide convenient interfaces for running AI models locally. However, be aware that model files can be quite large (often 20 GB or more), and without NPU support, performance may be sluggish and battery life will suffer.

Using AI locally comes with several key advantages. You gain full control over your data, knowing it’s never sent to external servers. Offline compatibility ensures uninterrupted use, even in remote or unstable network environments. In terms of responsiveness, local AI often outperforms cloud models due to the absence of network latency. Many tools are open source, making experimentation and customization financially accessible. Lastly, NPUs offer energy-efficient performance, enabling richer AI experiences on everyday devices.

In summary, if you’re looking for a faster, more private, and reliable AI workflow that doesn’t depend on the internet, equipping your laptop with an NPU and installing tools like Ollama, OpenWeb UI, GPT4All, or Jan.ai is a smart move. Not only will your interactions be quick and seamless, but they’ll also remain securely under your control.

AI and the Rise of Service-as-a-Service: Why Products Are Becoming Invisible

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SaaS security

Adaptive Access Technologies advanced technologies AI Advancements ai agents AI Models AI productivity AI technology Cyber Security LLMs saaS SaaS security

AI and the Rise of Service-as-a-Service: Why Products Are Becoming Invisible

The software world is undergoing a fundamental shift. Thanks to AI, product development has become faster, easier, and more scalable than ever before. Tools like Cursor and Lovable—along with countless “co-pilot” clones—have turned coding into prompt engineering, dramatically reducing development time and enhancing productivity.

This boom has naturally caught the attention of venture capitalists. Funding for software companies hit $80 billion in Q1 2025, with investors eager to back niche SaaS solutions that follow the familiar playbook: identify a pain point, build a narrow tool, and scale aggressively. Y Combinator’s recent cohort was full of “Cursor for X” startups, reflecting the prevailing appetite for micro-products.

But beneath this surge of point solutions lies a deeper transformation: the shift from product-led growth to outcome-driven service delivery. This evolution isn’t just about branding—it’s a structural redefinition of how software creates and delivers value. Historically, the SaaS revolution gave rise to subscription-based models, but the tools themselves remained hands-on. For example, when Adobe moved Creative Suite to the cloud, the billing changed—not the user experience. Users still needed to operate the software. SaaS, in that sense, was product-heavy and service-light.

Now, AI is dissolving the product layer itself. The software is still there, but it’s receding into the background. The real value lies in what it does, not how it’s used. Glide co-founder Gautam Ajjarapu captures this perfectly: “The product gets us in the door, but what keeps us there is delivering results.” Take Glide’s AI for banks. It began as a tool to streamline onboarding but quickly evolved into something more transformative. Banks now rely on Glide to improve retention, automate workflows, and enhance customer outcomes.

The interface is still a product, but the substance is service. The same trend is visible across leading AI startups. Zendesk markets “automated customer service,” where AI handles tickets end-to-end. Amplitude’s AI agents now generate product insights and implement changes. These offerings blur the line between tool and outcome—more service than software. This shift is grounded in economic logic. Services account for over 70% of U.S. GDP, and Nobel laureate Bengt Holmström’s contract theory helps explain why: businesses ultimately want results, not just tools.

They don’t want a CRM—they want more sales. They don’t want analytics—they want better decisions. With agentic AI, it’s now possible to deliver on that promise. Instead of selling a dashboard, companies can sell growth. Instead of building an LMS, they offer complete onboarding services powered by AI agents. This evolution is especially relevant in sectors like healthcare. Corti’s CEO Andreas Cleve emphasizes that doctors don’t want more interfaces—they want more time. AI that saves time becomes invisible, and its value lies in what it enables, not how it looks.

The implication is clear: software is becoming outcome-first. Users care less about tools and more about what those tools accomplish. Many companies—Glean, ElevenLabs, Corpora—are already moving toward this model, delivering answers, brand voices, or research synthesis rather than just access. This isn’t the death of the product—it’s its natural evolution. The best AI companies are becoming “services in a product wrapper,” where software is the delivery mechanism, but the value lies in what gets done.

For builders, the question is no longer how to scale a product. It’s how to scale outcomes. The companies that succeed in this new era will be those that understand: users don’t want features—they want results. Call it what you want—AI-as-a-service, agentic delivery, or outcome-led software. But the trend is unmistakable. Service-as-a-Service isn’t just the next step for SaaS. It may be the future of software itself.

Security Teams Struggle to Keep Up With Generative AI Threats, Cobalt Warns

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LLM security

AI Models AI technology AI tools CISO Cyber Security Cybersecurity Researchers GenAI Generative AI generative AI tools LLM LLM Model LLM security

Security Teams Struggle to Keep Up With Generative AI Threats, Cobalt Warns

A growing number of cybersecurity professionals are expressing concern that generative AI is evolving too rapidly for their teams to manage.

According to new research by penetration testing company Cobalt, over one-third of security leaders and practitioners admit that the pace of genAI development has outstripped their ability to respond. Nearly half of those surveyed (48%) said they wish they could pause and reassess their defense strategies in light of these emerging threats—though they acknowledge that such a break isn’t realistic.

In fact, 72% of respondents listed generative AI-related attacks as their top IT security risk. Despite this, one in three organizations still isn’t conducting regular security evaluations of their large language model (LLM) deployments, including basic penetration testing.

Cobalt CTO Gunter Ollmann warned that the security landscape is shifting, and the foundational controls many organizations rely on are quickly becoming outdated. “Our research shows that while generative AI is transforming how businesses operate, it’s also exposing them to risks they’re not prepared for,” said Ollmann.

“Security frameworks must evolve or risk falling behind.” The study revealed a divide between leadership and practitioners. Executives such as CISOs and VPs are more concerned about long-term threats like adversarial AI attacks, with 76% listing them as a top issue. Meanwhile, 45% of practitioners are more focused on immediate operational challenges such as model inaccuracies, compared to 36% of executives.

A majority of leaders—52%—are open to rethinking their cybersecurity strategies to address genAI threats. Among practitioners, only 43% shared this view. The top genAI-related concerns identified by the survey included the risk of sensitive information disclosure (46%), model poisoning or theft (42%), data inaccuracies (40%), and leakage of training data (37%). Around half of respondents also expressed a desire for more transparency from software vendors about how vulnerabilities are identified and patched, highlighting a widening trust gap in the AI supply chain.

Cobalt’s internal pentest data shows a worrying trend: while 69% of high-risk vulnerabilities are typically fixed across all test types, only 21% of critical flaws found in LLM tests are resolved. This is especially alarming considering that nearly one-third of LLM vulnerabilities are classified as serious. Interestingly, the average time to resolve these LLM-specific vulnerabilities is just 19 days—the fastest across all categories.

However, researchers noted this may be because organizations prioritize easier, low-effort fixes rather than tackling more complex threats embedded in foundational AI models. Ollmann compared the current scenario to the early days of cloud adoption, where innovation outpaced security readiness. He emphasized that traditional controls aren’t enough in the age of LLMs. “Security teams can’t afford to be reactive anymore,” he concluded. “They must move toward continuous, programmatic AI testing if they want to keep up.”

Hackers Can Spy on Screens Using HDMI Radiation and AI Models

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HDMI

AI Models AI Security Cyber Security data security Device Hack Device Security Hacker attack Hackers HDMI

Hackers Can Spy on Screens Using HDMI Radiation and AI Models

You may feel safe behind your screen, but it turns out that privacy might be more of an illusion than a fact. New research reveals that hackers have found an alarming way to peek at what’s happening on your display—without ever touching your computer. By tapping into the faint electromagnetic radiation that HDMI cables emit, they can now “listen in” on your screen and reconstruct what’s being shown with startling accuracy.

Here’s how it works: when digital signals travel through HDMI cables from your computer to a monitor, they unintentionally give off tiny bursts of radiation. These signals, invisible to the naked eye, can be picked up using radio antennas or small, discreet devices planted nearby. Once captured, advanced AI tools get to work, decoding the radiation into readable screen content.

The results? Up to 70% accuracy in reconstructing text—meaning everything from passwords and emails to private messages could be exposed. This new technique represents a serious leap in digital espionage. It doesn’t rely on malware or breaking into a network. Instead, it simply listens to the electronic “whispers” your hardware makes. It’s silent, stealthy, and completely undetectable to the average user.

Worryingly, this method is already reportedly in use against high-profile targets like government agencies and critical infrastructure sites. These organizations often store and manage sensitive data that, if leaked, could cause major damage. While some have implemented shielding to block these emissions, not all are fully protected. And because this form of surveillance leaves virtually no trace, many attacks could be flying under the radar entirely.

Hackers can go about this in two main ways: one, by sneaking a signal-collecting device into a location; or two, by using specialized antennas from nearby—like the building next door. Either way, they can eavesdrop on what’s displayed without ever getting physically close to the device. This new threat underscores the need for stronger physical and digital protections.

As cyberattacks become more innovative, simply securing your data with passwords and firewalls isn’t enough. Shielding cables and securing workspaces might soon be as important as having good antivirus software. The digital age has brought us many conveniences—but with it comes a new breed of invisible spies.

DeepSeek AI Raises Data Security Concerns Amid Ties to China

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DeepSeek

AI Models AI technology AI Threat China Cyber Security Data Mining Data Privacy data security DeepSeek

DeepSeek AI Raises Data Security Concerns Amid Ties to China

The launch of DeepSeek AI has created waves in the tech world, offering powerful artificial intelligence models at a fraction of the cost compared to established players like OpenAI and Google.

However, its rapid rise in popularity has also sparked serious concerns about data security, with critics drawing comparisons to TikTok and its ties to China. Government officials and cybersecurity experts warn that the open-source AI assistant could pose a significant risk to American users.

On Thursday, two U.S. lawmakers announced plans to introduce legislation banning DeepSeek from all government devices, citing fears that the Chinese Communist Party (CCP) could access sensitive data collected by the app. This move follows similar actions in Australia and several U.S. states, with New York recently enacting a statewide ban on government systems.

The growing concern stems from China’s data laws, which require companies to share user information with the government upon request. Like TikTok, DeepSeek’s data could be mined for intelligence purposes or even used to push disinformation campaigns. Although the AI app is the current focus of security conversations, experts say that the risks extend beyond any single model, and users should exercise caution with all AI systems.

Unlike social media platforms that users can consciously avoid, AI models like DeepSeek are more difficult to track. Dimitri Sirota, CEO of BigID, a cybersecurity company specializing in AI security compliance, points out that many companies already use multiple AI models, often switching between them without users’ knowledge. This fluidity makes it challenging to control where sensitive data might end up.

Kelcey Morgan, senior manager of product management at Rapid7, emphasizes that businesses and individuals should take a broad approach to AI security. Instead of focusing solely on DeepSeek, companies should develop comprehensive practices to protect their data, regardless of the latest AI trend. The potential for China to use DeepSeek’s data for intelligence is not far-fetched, according to cybersecurity experts.

With significant computing power and data processing capabilities, the CCP could combine information from multiple sources to create detailed profiles of American users. Though this might not seem urgent now, experts warn that today’s young, casual users could grow into influential figures worth targeting in the future.

To stay safe, experts advise treating AI interactions with the same caution as any online activity. Users should avoid sharing sensitive information, be skeptical of unusual questions, and thoroughly review an app’s terms and conditions. Ultimately, staying informed and vigilant about where and how data is shared will be critical as AI technologies continue to evolve and become more integrated into everyday life.

AI Models at Risk from TPUXtract Exploit

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TOUXtract Exploits

AI Models Cyber Researchers Cyberattacks CyberCrime Cyberhackers Cyberthreats Technology TOUXtract Exploits

AI Models at Risk from TPUXtract Exploit

A team of researchers has demonstrated that it is possible to steal an artificial intelligence (AI) model without actually gaining access to the device that is running the model. The uniqueness of the technique lies in the fact that it works efficiently even if the thief may not have any prior knowledge as to how the AI works in the first place, or how the computer is structured.

According to North Carolina State University's Department of Electrical and Computer Engineering, the method is known as TPUXtract, and it is a product of their department. With the help of a team of four scientists, who used high-end equipment and a technique known as "online template-building", they were able to deduce the hyperparameters of a convolutional neural network (CNN) running on Google Edge Tensor Processing Unit (TPU), which is the settings that define its structure and behaviour, with a 99.91% accuracy rate.

The TPUXtract is an advanced side-channel attack technique devised by researchers at the North Carolina State University, designed to protect servers from attacks. A convolutional neural network (CNN) running on a Google Edge Tensor Processing Unit (TPU) is targeted in the attack, and electromagnetic signals are exploited to extract hyperparameters and configurations of the model without the need for previous knowledge of its architecture and software.

A significant risk to the security of AI models and the integrity of intellectual property is posed by these types of attacks, which manifest themselves across three distinct phases, each of which is based on advanced methods to compromise the AI models' integrity. Attackers in the Profiling Phase observe and capture side-channel emissions produced by the target TPU as it processes known input data as part of the Profiling Phase. As a result, they have been able to decode unique patterns which correspond to specific operations such as convolutional layers and activation functions by using advanced methods like Differential Power Analysis (DPA) and Cache Timing Analysis.

The Reconstruction Phase begins with the extraction and analysis of these patterns, and they are meticulously matched to known processing behaviours This enables adversaries to make an inference about the architecture of the AI model, including the layers that have been configured, the connections made, and the parameters that are relevant such as weight and bias. Through a series of repeated simulations and output comparisons, they can refine their understanding of the model in a way that enables precise reconstruction of the original model.

Finally, the Validation Phase ensures that the replicated model is accurate. During the testing process, it is subject to rigorous testing with fresh inputs to ensure that it performs similarly to that of the original, thus providing reliable proof of its success. The threat that TPUXtract poses to intellectual property (IP) is composed of the fact that it enables attackers to steal and duplicate artificial intelligence models, bypassing the significant resources that are needed to develop them.

The competition could recreate and mimic models such as ChatGPT without having to invest in costly infrastructure or train their employees. In addition to IP theft, TPUXtract exposed cybersecurity risks by revealing an AI model's structure that provided visibility into its development and capabilities. This information could be used to identify vulnerabilities and enable cyberattacks, as well as expose sensitive data from a variety of industries, including healthcare and automotive.

Further, the attack requires specific equipment, such as a Riscure Electromagnetic Probe Station, high-sensitivity probes, and Picoscope oscilloscope, so only well-funded groups, for example, corporate competitors or state-sponsored actors, can execute it. As a result of the technical and financial requirements for the attack, it can only be executed by well-funded groups. With the understanding that any electronic device will emit electromagnetic radiation as a byproduct of its operations, the nature and composition of that radiation will be affected by what the device does.

To conduct their experiments, the researchers placed an EM probe on top of the TPU after removing any obstructions such as cooling fans and centring it over the part of the chip emitting the strongest electromagnetic signals. The machine then emitted signals as a result of input data, and the signals were recorded. The researchers used the Google Edge TPU for this demonstration because it is a commercially available chip that is widely used to run AI models on edge devices meaning devices utilized by end users in the field, as opposed to AI systems that are used for database applications. During the demonstration, electromagnetic signals were monitored as a part of the technique used to conduct the demonstration.

A TPU chip was placed on top of a probe that was used by researchers to determine the structure and layer details of an AI model by recording changes in the electromagnetic field of the TPU during AI processing. The probe provided real-time data about changes in the electromagnetic field of the TPU during AI processing. To verify the model's electromagnetic signature, the researchers compared it to other signatures made by AI models made on a similar device - in this case, another Google Edge TPU. Using this technique, Kurian says, AI models can be stolen from a variety of different devices, including smartphones, tablets and computers.

The attacker should be able to use this technique as long as they know the device from which they want to steal, have access to it while it is running an AI model, and have access to another device with similar specifications According to Kurian, the electromagnetic data from the sensor is essentially a ‘signature’ of the way AI processes information. There is a lot of work that goes into pulling off TPUXtract. The process not only requires a great deal of technical expertise, but it also requires a great deal of expensive and niche equipment as well. To scan the chip's surface, NCSU researchers used a Riscure EM probe station equipped with a motorized XYZ table, and a high-sensitivity electromagnetic probe to capture the weak signals emanating from it.

It is said that the traces were recorded using a Picoscope 6000E oscilloscope, and Riscure's icWaves FPGA device aligned them in real-time, and the icWaves transceiver translated and filtered out the irrelevant signals using bandpass filters and AM/FM demodulation, respectively. While this may seem difficult and costly for a hacker to do on their own, Kurian explains, "It is possible for a rival company to do this within a couple of days, regardless of how difficult and expensive it will be.

Taking the threat of TPUXtract into account, this model poses a formidable challenge to AI model security, highlighting the importance of proactive measures. As an organization, it is crucial to understand how such attacks work, implement robust defences, and ensure that they can safeguard their intellectual property while maintaining trust in their artificial intelligence systems. The AI and cybersecurity communities must learn continuously and collaborate to stay ahead of the changing threats as they arise.

The Role of Confidential Computing in AI and Web3

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Technology

AI Ecosystem AI Models AI technology AI Tool Computing Data Privacy Data Sets DeFi privacy protection secure computing Technology

The Role of Confidential Computing in AI and Web3

The rise of artificial intelligence (AI) has amplified the demand for privacy-focused computing technologies, ushering in a transformative era for confidential computing. At the forefront of this movement is the integration of these technologies within the AI and Web3 ecosystems, where maintaining privacy while enabling innovation has become a pressing challenge. A major event in this sphere, the DeCC x Shielding Summit in Bangkok, brought together more than 60 experts to discuss the future of confidential computing.

Pioneering Confidential Computing in Web3

Lisa Loud, Executive Director of the Secret Network Foundation, emphasized in her keynote that Secret Network has been pioneering confidential computing in Web3 since its launch in 2020. According to Loud, the focus now is to mainstream this technology alongside blockchain and decentralized AI, addressing concerns with centralized AI systems and ensuring data privacy.

Yannik Schrade, CEO of Arcium, highlighted the growing necessity for decentralized confidential computing, calling it the “missing link” for distributed systems. He stressed that as AI models play an increasingly central role in decision-making, conducting computations in encrypted environments is no longer optional but essential.

Schrade also noted the potential of confidential computing in improving applications like decentralized finance (DeFi) by integrating robust privacy measures while maintaining accessibility for end users. However, achieving a balance between privacy and scalability remains a significant hurdle. Schrade pointed out that privacy safeguards often compromise user experience, which can hinder broader adoption. He emphasized that for confidential computing to succeed, it must be seamlessly integrated so users remain unaware they are engaging with such technologies.

Shahaf Bar-Geffen, CEO of COTI, underscored the role of federated learning in training AI models on decentralized datasets without exposing raw data. This approach is particularly valuable in sensitive sectors like healthcare and finance, where confidentiality and compliance are critical.

Innovations in Privacy and Scalability

Henry de Valence, founder of Penumbra Labs, discussed the importance of aligning cryptographic systems with user expectations. Drawing parallels with secure messaging apps like Signal, he emphasized that cryptography should function invisibly, enabling users to interact with systems without technical expertise. De Valence stressed that privacy-first infrastructure is vital as AI’s capabilities to analyze and exploit data grow more advanced.

Other leaders in the field, such as Martin Leclerc of iEXEC, highlighted the complexity of achieving privacy, usability, and regulatory compliance. Innovative approaches like zero-knowledge proof technology, as demonstrated by Lasha Antadze of Rarimo, offer promising solutions. Antadze explained how this technology enables users to prove eligibility for actions like voting or purchasing age-restricted goods without exposing personal data, making blockchain interactions more accessible.

Dominik Schmidt, co-founder of Polygon Miden, reflected on lessons from legacy systems like Ethereum to address challenges in privacy and scalability. By leveraging zero-knowledge proofs and collaborating with decentralized storage providers, his team aims to enhance both developer and user experiences.

As confidential computing evolves, it is clear that privacy and usability must go hand in hand to address the needs of an increasingly data-driven world. Through innovation and collaboration, these technologies are set to redefine how privacy is maintained in AI and Web3 applications.

Securing Generative AI: Tackling Unique Risks and Challenges

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Google Cloud

AI Models AI technology Cyber Security Data integrity data poisoning data security Generative AI Google Cloud

Securing Generative AI: Tackling Unique Risks and Challenges

Generative AI has introduced a new wave of technological innovation, but it also brings a set of unique challenges and risks. According to Phil Venables, Chief Information Security Officer of Google Cloud, addressing these risks requires expanding traditional cybersecurity measures. Generative AI models are prone to issues such as hallucinations—where the model produces inaccurate or nonsensical content—and the leaking of sensitive information through model outputs. These risks necessitate the development of tailored security strategies to ensure safe and reliable AI use.

One of the primary concerns with generative AI is data integrity. Models rely heavily on vast datasets for training, and any compromise in this data can lead to significant security vulnerabilities. Venables emphasizes the importance of maintaining the provenance of training data and implementing controls to protect its integrity. Without proper safeguards, models can be manipulated through data poisoning, which can result in the production of biased or harmful outputs. Another significant risk involves prompt manipulation, where adversaries exploit vulnerabilities in the AI model to produce unintended outcomes.

This can include injecting malicious prompts or using adversarial tactics to bypass the model’s controls. Venables highlights the necessity of robust input filtering mechanisms to prevent such manipulations. Organizations should deploy comprehensive logging and monitoring systems to detect and respond to suspicious activities in real time. In addition to securing inputs, controlling the outputs of AI models is equally critical. Venables recommends the implementation of “circuit breakers”—mechanisms that monitor and regulate model outputs to prevent harmful or unintended actions. This ensures that even if an input is manipulated, the resulting output is still within acceptable parameters. Infrastructure security also plays a vital role in safeguarding generative AI systems.

Venables advises enterprises to adopt end-to-end security practices that cover the entire lifecycle of AI deployment, from model training to production. This includes sandboxing AI applications, enforcing the least privilege principle, and maintaining strict access controls on models, data, and infrastructure. Ultimately, securing generative AI requires a holistic approach that combines innovative security measures with traditional cybersecurity practices.

By focusing on data integrity, robust monitoring, and comprehensive infrastructure controls, organizations can mitigate the unique risks posed by generative AI. This proactive approach ensures that AI systems are not only effective but also safe and trustworthy, enabling enterprises to fully leverage the potential of this groundbreaking technology while minimizing associated risks.

Zero-Trust Log Intelligence: Safeguarding Data with Secure Access

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Zero Trust

AI Models Cyber Security Data Encryption log files threats Zero Trust

Zero-Trust Log Intelligence: Safeguarding Data with Secure Access

Over the years, zero trust has become a popular model adopted by organisations due to a growing need to ensure confidential information is kept safe, an aspect that organisations view as paramount in cybersecurity. Zero-trust is a vital security framework that is fundamentally not like the traditional security perimeter-based model. Instead of relying on a robust boundary, zero-trust grants access to its resources after the constant validation of any user and every device they use, regardless of an individual's position within an organisation or the number of years since one first employed with the company. This "never trust, always verify" policy only grants minimum access to someone, even a long-tenured employee, about what is needed to fulfil their tasks. Because information for cybersecurity is often log file data, zero trust principles can provide better safeguarding of this sensitive information.

Log Files: Why They Are Both Precious and Vulnerable

Log files contain information that reflects all the digital interplay happening on the network, hence can indicate any vulnerability on a system for remediation purposes. For example, it's a good source where one will trace how companies' activities go regarding their performance by analysing log files for anything out of place or anomalies in systems' behaviours for speedy intervention for security lapses. At the same time, however, these log files can expose organisations to vulnerabilities when wrong hands gain access because of possible theft of confidential data or the intention of hacking or modification. The log files have to be strictly controlled and limited only for authorization, because the misuse has to be avoided for maintaining the network secure.

Collecting and Storing Log Data Securely

Zero trust can best be implemented only if gathering and storing of log file collection and storage are sound. It ensures that the real-time data is collected in an environment that has a tamper-resistant place that prevents data from unauthorised modification. Of late, there has been OpenTelemetry, which is gaining popularity due to its potential in the multiple data sources and secure integration with many databases, mostly PostgreSQL.

Secure log storage applies blockchain technology. A decentralised, immutable structure like blockchain ensures logs cannot be altered and their records will remain transparent as well as tamper-proof. The reason blockchain technology works through multiple nodes rather than one central point makes it nearly impossible to stage a focused attack on the log data.

Imposing Least Privilege Access Control

Least privilege access would be one of the greatest principles of zero-trust security, which means that end-users would have only access to what is required to achieve their task. However, it can be challenging when balancing this principle with being efficient in log analysis; traditional access control methods-such as data masking or classification-frequently fall short and are not very practical. One promising solution to this problem is homomorphic encryption, which enables analysis of data in its encrypted state. Analysts can evaluate log files without ever directly seeing the unencrypted data, ensuring that security is maintained without impacting workflow.

Homomorphic encryption is beyond the level of the analyst. This means other critical stakeholders, such as administrators, have access to permissions but are not allowed to read actual data. This means logs are going to be secure at internal teams and thus there is a lesser chance of accidental exposure.

In-House AI for Threat Detection

Companies can further secure log data by putting in-house AI models which are run directly within their database and hence minimise external access. For instance, the company can use a private SLM AI that was trained specifically to analyse the logs. This ensures there is safe and accurate threat detection without having to share any logs with third-party services. The other advantage that an AI trained on relevant log data provides is less bias, as all operations depend on only relevant encrypted log data that can give an organisation precise and relevant insights.

Organisations can ensure maximum security while minimising exposure to potential cyber threats by applying a zero-trust approach through strict access controls and keeping data encrypted all through the analysis process.

Zero-Trust for Optimal Log Security

One of the effective log file intelligence approaches appears to be zero trust security-a security approach that uses the technologies of blockchain and homomorphic encryption to ensure the integrity and privacy of information in management. It means one locks up logs, and it is a source for valuable security insights, kept well protected against unauthorised access and modifications.

Even if an organisation does not adopt zero-trust completely for its systems, it should still ensure that the protection of the logs is considered a priority. By taking the essential aspects of zero-trust, such as having minimal permissions and secured storage, it can help organisations decrease their vulnerability to cyber attacks while protecting this critical source of data.

Managing LLM Security Risks in Enterprises: Preventing Insider Threats

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RCE

AI Models Cross Site Scripting Cyber Security Enterprise security Jailbreak Tool Jailbreaking LLM LLM security RCE

Managing LLM Security Risks in Enterprises: Preventing Insider Threats

Large language models (LLMs) are transforming enterprise automation and efficiency but come with significant security risks. These AI models, which lack critical thinking, can be manipulated to disclose sensitive data or even trigger actions within integrated business systems. Jailbreaking LLMs can lead to unauthorized access, phishing, and remote code execution vulnerabilities. Mitigating these risks requires strict security protocols, such as enforcing least privilege, limiting LLM actions, and sanitizing input and output data. LLMs in corporate environments pose threats because they can be tricked into sharing sensitive information or be used to trigger harmful actions within systems.

Unlike traditional tools, their intelligent, responsive nature can be exploited through jailbreaking—altering the model’s behavior with crafted prompts. For instance, LLMs integrated with a company’s financial system could be compromised, leading to data manipulation, phishing attacks, or broader security vulnerabilities such as remote code execution. The severity of these risks grows when LLMs are deeply integrated into essential business operations, expanding potential attack vectors. In some cases, threats like remote code execution (RCE) can be facilitated by LLMs, allowing hackers to exploit weaknesses in frameworks like LangChain. This not only threatens sensitive data but can also lead to significant business harm, from financial document manipulation to broader lateral movement within a company’s systems.

Although some content-filtering and guardrails exist, the black-box nature of LLMs makes specific vulnerabilities challenging to detect and fix through traditional patching. Meta’s Llama Guard and other similar tools provide external solutions, but a more comprehensive approach is needed to address the underlying risks posed by LLMs. To mitigate the risks, companies should enforce strict security measures. This includes applying the principle of least privilege—restricting LLM access and functionality to the minimum necessary for specific tasks—and avoiding reliance on LLMs as a security perimeter.

Organizations should also ensure that input data is sanitized and validate all outputs for potential threats like cross-site scripting (XSS) attacks. Another important measure is limiting the actions that LLMs can perform, preventing them from mimicking end-users or executing actions outside their intended purpose. For cases where LLMs are used to run code, employing a sandbox environment can help isolate the system and protect sensitive data.

While LLMs bring incredible potential to enterprises, their integration into critical systems must be carefully managed. Organizations need to implement robust security measures, from limiting access privileges to scrutinizing training data and ensuring that sensitive data is protected. This strategic approach will help mitigate the risks associated with LLMs and reduce the chance of exploitation by malicious actors.

Want to Make the Most of ChatGPT? Here Are Some Go-To Tips

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Technology

AI Models Artifcial Intelligence ChatGPT Software Technology

Want to Make the Most of ChatGPT? Here Are Some Go-To Tips

Within a year and a half, ChatGPT has grown from an AI prototype to a broad productivity assistant, even sporting its text and code editor called Canvas. Soon, OpenAI will add direct web search capability to ChatGPT, putting the platform at the same table as Google's iconic search. With these fast updates, ChatGPT is now sporting quite a few features that may not be noticed at first glance but are deepening the user experience if one knows where to look.

This is the article that will teach you how to tap into ChatGPT, features from customization settings to unique prompting techniques, and not only five must-know tips will be useful in unlocking the full range of abilities of ChatGPT to any kind of task, small or big.

1. Rename Chats for Better Organisation

A new conversation with ChatGPT begins as a new thread, meaning that it will remember all details concerning that specific exchange but "forget" all the previous ones. This way, you can track the activities of current projects or specific topics because you can name your chats. The chat name that it might try to suggest is related to the flow of the conversation, and these are mostly overlooked contexts that users need to recall again. Renaming your conversations is one simple yet powerful means of staying organised if you rely on ChatGPT for various tasks.

To give a name to a conversation, tap the three dots next to the name in the sidebar. You can also archive older chats to remove them from the list without deleting them entirely, so you don't lose access to the conversations that are active.

2. Customise ChatGPT through Custom Instructions

Custom Instructions in ChatGPT is a chance to make your answers more specific to your needs because you will get to share your information and preferences with the AI. This is a two-stage personalization where you are explaining to ChatGPT what you want to know about yourself and, in addition, how you would like it to be returned. For instance, if you ask ChatGPT for coding advice several times a week, you can let the AI know what programming languages you are known in or would like to be instructed in so it can fine-tune the responses better. Or, you should be able to ask for ChatGPT to provide more verbose descriptions or to skip steps in order to make more intuitive knowledge of a topic.

To set up personal preferences, tap the profile icon on the upper right, and then from the menu, "Customise ChatGPT," and then fill out your preferences. Doing this will enable you to get responses tailored to your interests and requirements.

3. Choose the Right Model for Your Use

If you are a subscriber to ChatGPT Plus, you have access to one of several AI models each tailored to different tasks. The default model for most purposes is GPT-4-turbo (GPT-4o), which tends to strike the best balance between speed and functionality and even supports other additional features, including file uploads, web browsing, and dataset analysis.

However, other models are useful when one needs to describe a rather complex project with substantial planning. You may initiate a project using o1-preview that requires deep research and then shift the discussion to GPT-4-turbo to get quick responses. To switch models, you can click on the model dropdown at the top of your screen or type in a forward slash (/) in the chat box to get access to more available options including web browsing and image creation.

4. Look at what the GPT Store has available in the form of Mini-Apps

Custom GPTs, and the GPT Store enable "mini-applications" that are able to extend the functionality of the platform. The Custom GPTs all have some inbuilt prompts and workflows and sometimes even APIs to extend the AI capability of GPT. For instance, with Canva's GPT, you are able to create logos, social media posts, or presentations straight within the ChatGPT portal by linking up the Canva tool. That means you can co-create visual content with ChatGPT without having to leave the portal.

And if there are some prompts you often need to apply, or some dataset you upload most frequently, you can easily create your Custom GPT. This would be really helpful to handle recipes, keeping track of personal projects, create workflow shortcuts and much more. Go to the GPT Store by the "Explore GPTs" button in the sidebar. Your recent and custom GPTs will appear in the top tab, so find them easily and use them as necessary.

5. Manage Conversations with a Fresh Approach

For the best benefit of using ChatGPT, it is key to understand that every new conversation is an independent document with its "memory." It does recall enough from previous conversations, though generally speaking, its answers depend on what is being discussed in the immediate chat. This made chats on unrelated projects or topics best started anew for clarity.

For long-term projects, it might even be logical to go on with a single thread so that all relevant information is kept together. For unrelated topics, it might make more sense to start fresh each time to avoid confusion. Another way in which archiving or deleting conversations you no longer need can help free up your interface and make access to active threads easier is

What Makes AI Unique Compared to Other Software?

AI performs very differently from other software in that it responds dynamically, at times providing responses or "backtalk" and does not simply do what it is told to do. Such a property leads to some trial and error to obtain the desired output. For instance, one might prompt ChatGPT to review its own output as demonstrated by replacing single quote characters by double quote characters to generate more accurate results. This is similar to how a developer optimises an AI model, guiding ChatGPT to "think" through something in several steps.

ChatGPT Canvas and other features like Custom GPTs make the AI behave more like software in the classical sense—although, of course, with personality and learning. If ChatGPT continues to grow in this manner, features such as these may make most use cases easier and more delightful.

Following these five tips should help you make the most of ChatGPT as a productivity tool and keep pace with the latest developments. From renaming chats to playing around with Custom GPTs, all of them add to a richer and more customizable user experience.

Data Poisoning: The Hidden Threat to AI Models

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Technology

AI Models data poisoning JFrog Security Machine learning Technology

Data Poisoning: The Hidden Threat to AI Models

As ongoing developments in the realms of artificial intelligence and machine learning take place at a dynamic rate, yet another new form of attack is emerging, one which can topple all those systems we use today without much ado: data poisoning. This type of attack involves tampering with data used by AI models in training to make them malfunction, often undetectably. The issue came to light when recently, more than 100 malicious models were uncovered on the popular repository for AI, Hugging Face, by a software management company, JFrog.

What is Data Poisoning?

Data poisoning is an attack method on AI models by corrupting the data used for its training. In other words, the intent is to have the model make inappropriate predictions or choices. Besides, unlike traditional hacking, it doesn't require access to the system; therefore, data poisoning manipulates input data either before the deployment of an AI model or after the deployment of the AI model, and that makes it very difficult to detect.

One attack happens at the training phase when an attacker manages to inject malicious data into any AI model. Yet another attack happens post-deployment when poisoned data is fed to the AI; it yields wrong outputs. Both kinds of attacks remain hardly detectable and cause damage to the AI system in the long run.

According to research by JFrog, investigators found a number of suspicious models uploaded to Hugging Face, a community where users can share AI models. Those contained encoded malicious code, which the researchers believe hackers-those potentially coming from the KREOnet research network in Korea-might have embedded. The most worrying aspect, however, was the fact that these malicious models went undetected by masquerading as benign.

That's a serious threat because many AI systems today use a great amount of data from different sources, including the internet. In cases where attackers manage to change the data used in the training of a model, that could mean anything from misleading results to actual large-scale cyberattacks.

Why It's Hard to Detect

One of the major challenges with data poisoning is that AI models are built by using enormous data sets, which makes it difficult for researchers to always know what has gone into the model. A lack of clarity of this kind in turn creates ways in which attackers can sneak in poisoned data without being caught.

But it gets worse: AI systems that scrape data from the web continuously in order to update themselves could poison their own training data. This sets up the alarming possibility of an AI system's gradual breakdown, or "degenerative model collapse."

The Consequences of Ignoring the Threat

If left unmitigated, data poisoning could further allow attackers to inject stealth backdoors in AI software that enable them to conduct malicious actions or cause any AI system to behave in ways unexpected. Precisely, they can run malicious code, allow phishing, and rig AI predictions for various nefarious uses.

The cybersecurity industry must take this as a serious threat since more dependence occurs on generative AI linked together, alongside LLMs. If one fails to do so, widespread vulnerability across the complete digital ecosystem will result.

How to Defend Against Data Poisoning

The protection of AI models against data poisoning calls for vigilance throughout the process of the AI development cycle. Experts say that this may require oversight by organisations in using only data from sources they can trust for training the AI model. The Open Web Application Security Project, or OWASP, has provided a list of some best ways to avoid data poisoning; a few of these include frequent checks to find biases and abnormalities during the training of data.

Other recommendations come in the form of multiple AI algorithms that verify results against each other to locate inconsistency. If an AI model starts producing strange results, fallback mechanisms should be in place to prevent any harm.

This also encompasses simulated data poisoning attacks run by cybersecurity teams to test their AI systems for robustness. While it is hard to build an AI system that is 100% secure, frequent validation of predictive outputs goes a long way in detecting and preventing poisoning.

Creating a Secure Future for AI

While AI keeps evolving, there is a need to instil trust in such systems. This will only be possible when the entire ecosystem of AI, even the supply chains, forms part of the cybersecurity framework. This would be achievable through monitoring inputs and outputs against unusual or irregular AI systems. Therefore, organisations will build robust, and more trustworthy models of AI.

Ultimately, the future of AI hangs in the balance with our capability to race against emerging threats like data poisoning. In sum, the ability of businesses to proactively take steps toward the security of AI systems today protects them from one of the most serious challenges facing the digital world.

The bottom line is that AI security is not just about algorithms; it's about the integrity for the data powering those algorithms.

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Chinese Open AI Models Rival US Systems and Reshape Global Adoption

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Genesis Mission Launches as US Builds Closed-Loop AI System Linking National Laboratories

Hacker Exploits AI Chatbot Claude in Unprecedented Cybercrime Operation

Hackers Use DNS Records to Hide Malware and AI Prompt Injections

Why Running AI Locally with an NPU Offers Better Privacy, Speed, and Reliability

AI and the Rise of Service-as-a-Service: Why Products Are Becoming Invisible

Security Teams Struggle to Keep Up With Generative AI Threats, Cobalt Warns

Hackers Can Spy on Screens Using HDMI Radiation and AI Models

DeepSeek AI Raises Data Security Concerns Amid Ties to China

AI Models at Risk from TPUXtract Exploit

The Role of Confidential Computing in AI and Web3

Pioneering Confidential Computing in Web3

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Securing Generative AI: Tackling Unique Risks and Challenges

Zero-Trust Log Intelligence: Safeguarding Data with Secure Access

Managing LLM Security Risks in Enterprises: Preventing Insider Threats

Want to Make the Most of ChatGPT? Here Are Some Go-To Tips

Data Poisoning: The Hidden Threat to AI Models

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Pioneering Confidential Computing in Web3

Innovations in Privacy and Scalability