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Showing posts with label AI cybersecurity. Show all posts

Webrat Malware Targets Students and Junior Security Researchers Through Fake Exploits

 

In early 2025, security researchers uncovered a new malware family dubbed Webrat, which at that time was predominantly targeting ordinary users through fake distribution methods. The first propagation involved masking malware as cheats for online games-like Rust, Counter-Strike, and Roblox-but also as cracked versions of some commercial software. By the second half of that year, though, the Webrat operators had indeed widened their horizons, shifting toward a new target group that covered students and young professionals seeking careers in information security. 

This evolution started to surface in September and October 2025, when researchers discovered a campaign spreading Webrat through open GitHub repositories. The attackers embedded the malicious payloads as proof-of-concept exploits of highly publicized software vulnerabilities. Those vulnerabilities were chosen due to their resonance in security advisories and high severity ratings, making the repositories look relevant and credible for people searching for hands-on learning materials.  

Each of the GitHub repositories was crafted to closely resemble legitimate exploit releases. They all had detailed descriptions outlining the background of the vulnerability, affected systems, steps to install it, usage, and the most recommended ways of mitigation. Many of the repository descriptions have a similar or almost identical structure; the defensive advice offered is often strikingly similar, adding strong evidence that they were generated through automated or AI-assisted tools rather than various independent researchers. Inside each repository, users were instructed to fetch an archive with a password, labeled as the exploit package. 

The password was hidden in the name of one of the files inside the archive, a move intended to lure users into unzipping the file and researching its contents. Once unpacked, the archive contains a set of files meant to masquerade or divert attention from the actual payload. Among those is a corrupted dynamic-link library file meant as a decoy, along with a batch file whose purpose was to instruct execution of the main malicious executable file. The main executable, when run, executed several high-risk actions: It tried to elevate its privileges to administrator level, disabled the inbuilt security protections such as Windows Defender, and then downloaded the Webrat backdoor from a remote server and started it.

The Webrat backdoor provides a way to attackers for persistent access to infected systems, allowing them to conduct widespread surveillance and data theft activities. Webrat can steal credentials and other sensitive information from cryptocurrency wallets and applications like Telegram, Discord, and Steam. In addition to credential theft, it also supports spyware functionalities such as screen capture, keylogging, and audio and video surveillance via connected microphones and webcams. The functionality seen in this campaign is very similar to versions of Webrat described in previous incidents. 

It seems that the move to dressing the malware up as vulnerability exploits represents an effort to affect hobbyists rather than professionals. Professional analysts normally analyze such untrusted code in a sandbox or isolated environment, where such attacks have limited consequences. 

Consequently, researchers believe the attack focuses on students and beginners with lax operational security discipline. It ranges in topic from the risks in running unverified code downloaded from open-source sites to the need to perform malware analysis and exploit testing in a sandbox or virtual machine environment. 

Security professionals and students are encouraged to be keen in their practices, to trust only known and reputable security tools, and to bypass protection mechanisms only when this is needed with a clear and well-justified reason.

AI-Assisted Cyberattacks Signal a Shift in Modern Threat Strategies and Defense Models

 

A new wave of cyberattacks is using large language models as an offensive tool, according to recent reporting from Anthropic and Oligo Security. Both groups said hackers used jailbroken LLMs-some capable of writing code and conducting autonomous reasoning-to conduct real-world attack campaigns. While the development is alarming, cybersecurity researchers had already anticipated such advancements. 

Earlier this year, a group at Cornell University published research predicting that cybercriminals would eventually use AI to automate hacking at scale. The evolution is consistent with a recurring theme in technology history: Tools designed for productivity or innovation inevitably become dual-use. Any number of examples-from drones to commercial aircraft to even Alfred Nobel's invention of dynamite-demonstrate how innovation often carries unintended consequences. 

The biggest implication of it all in cybersecurity is that LLMs today finally allow attackers to scale and personalize their operations simultaneously. In the past, cybercriminals were mostly forced to choose between highly targeted efforts that required manual work or broad, indiscriminate attacks with limited sophistication. 

Generative AI removes this trade-off, allowing attackers to run tailored campaigns against many targets at once, all with minimal input. In Anthropic's reported case, attackers initially provided instructions on ways to bypass its model safeguards, after which the LLM autonomously generated malicious output and conducted attacks against dozens of organizations. Similarly, Oligo Security's findings document a botnet powered by AI-generated code, first exploiting an AI infrastructure tool called Ray and then extending its activity by mining cryptocurrency and scanning for new targets. 

Traditional defenses, including risk-based prioritization models, may become less effective within this new threat landscape. These models depend upon the assumption that attackers will strategically select targets based upon value and feasibility. Automation collapses the cost of producing custom attacks such that attackers are no longer forced to prioritize. That shift erases one of the few natural advantages defenders had. 

Complicating matters further, defenders must weigh operational impact when making decisions about whether to implement a security fix. In many environments, a mitigation that disrupts legitimate activity poses its own risk and may be deferred, leaving exploitable weaknesses in place. Despite this shift, experts believe AI can also play a crucial role in defense. The future could be tied to automated mitigations capable of assessing risks and applying fixes dynamically, rather than relying on human intervention.

In some cases, AI might decide that restrictions should narrowly apply to certain users; in other cases, it may recommend immediate enforcement across the board. While the attackers have momentum today, cybersecurity experts believe the same automation that today enables large-scale attacks could strengthen defenses if it is deployed strategically.

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.

Clanker: The Viral AI Slur Fueling Backlash Against Robots and Chatbots

 

In popular culture, robots have long carried nicknames. Battlestar Galactica called them “toasters,” while Blade Runner used the term “skinjobs.” Now, amid rising tensions over artificial intelligence, a new label has emerged online: “clanker.” 

The word, once confined to Star Wars lore where it was used against battle droids, has become the latest insult aimed at robots and AI chatbots. In a viral video, a man shouted, “Get this dirty clanker out of here!” at a sidewalk robot, echoing a sentiment spreading rapidly across social platforms. 

Posts using the term have exploded on TikTok, Instagram, and X, amassing hundreds of millions of views. Beyond online humor, “clanker” has been adopted in real-world debates. Arizona Senator Ruben Gallego even used the word while promoting his bill to regulate AI-driven customer service bots. For critics, it has become a rallying cry against automation, generative AI content, and the displacement of human jobs. 

Anti-AI protests in San Francisco and London have also adopted the phrase as a unifying slogan. “It’s still early, but people are really beginning to see the negative impacts,” said protest organizer Sam Kirchner, who recently led a demonstration outside OpenAI’s headquarters. 

While often used humorously, the word reflects genuine frustration. Jay Pinkert, a marketing manager in Austin, admits he tells ChatGPT to “stop being a clanker” when it fails to answer him properly. For him, the insult feels like a way to channel human irritation toward a machine that increasingly behaves like one of us. 

The term’s evolution highlights how quickly internet culture reshapes language. According to etymologist Adam Aleksic, clanker gained traction this year after online users sought a new word to push back against AI. “People wanted a way to lash out,” he said. “Now the word is everywhere.” 

Not everyone is comfortable with the trend. On Reddit and Star Wars forums, debates continue over whether it is ethical to use derogatory terms, even against machines. Some argue it echoes real-world slurs, while others worry about the long-term implications if AI achieves advanced intelligence. Culture writer Hajin Yoo cautioned that the word’s playful edge risks normalizing harmful language patterns. 

Still, the viral momentum shows little sign of slowing. Popular TikTok skits depict a future where robots, labeled clankers, are treated as second-class citizens in human society. For now, the term embodies both the humor and unease shaping public attitudes toward AI, capturing how deeply the technology has entered cultural debates.

How Scammers Use Deepfakes in Financial Fraud and Ways to Stay Protected

 

Deepfake technology, developed through artificial intelligence, has advanced to the point where it can convincingly replicate human voices, facial expressions, and subtle movements. While once regarded as a novelty for entertainment or social media, it has now become a dangerous tool for cybercriminals. In the financial world, deepfakes are being used in increasingly sophisticated ways to deceive institutions and individuals, creating scenarios where it becomes nearly impossible to distinguish between genuine interactions and fraudulent attempts. This makes financial fraud more convincing and therefore more difficult to prevent. 

One of the most troubling ways scammers exploit this technology is through face-swapping. With many banks now relying on video calls for identity verification, criminals can deploy deepfake videos to impersonate real customers. By doing so, they can bypass security checks and gain unauthorized access to accounts or approve financial decisions on behalf of unsuspecting individuals. The realism of these synthetic videos makes them difficult to detect in real time, giving fraudsters a significant advantage. 

Another major risk involves voice cloning. As voice-activated banking systems and phone-based transaction verifications grow more common, fraudsters use audio deepfakes to mimic a customer’s voice. If a bank calls to confirm a transaction, criminals can respond with cloned audio that perfectly imitates the customer, bypassing voice authentication and seizing control of accounts. Scammers also use voice and video deepfakes to impersonate financial advisors or bank representatives, making victims believe they are speaking to trusted officials. These fraudulent interactions may involve fake offers, urgent warnings, or requests for sensitive data, all designed to extract confidential information. 

The growing realism of deepfakes means consumers must adopt new habits to protect themselves. Double-checking unusual requests is a critical step, as fraudsters often rely on urgency or trust to manipulate their targets. Verifying any unexpected communication by calling a bank’s official number or visiting in person remains the safest option. Monitoring accounts regularly is another defense, as early detection of unauthorized or suspicious activity can prevent larger financial losses. Setting alerts for every transaction, even small ones, can make fraudulent activity easier to spot. 

Using multi-factor authentication adds an essential layer of protection against these scams. By requiring more than just a password to access accounts, such as one-time codes, biometrics, or additional security questions, banks make it much harder for criminals to succeed, even if deepfakes are involved. Customers should also remain cautious of video and audio communications requesting sensitive details. Even if the interaction appears authentic, confirming through secure channels is far more reliable than trusting what seems real on screen or over the phone.  

Deepfake-enabled fraud is dangerous precisely because of how authentic it looks and sounds. Yet, by staying vigilant, educating yourself about emerging scams, and using available security tools, it is possible to reduce risks. Awareness and skepticism remain the strongest defenses, ensuring that financial safety is not compromised by increasingly deceptive digital threats.

Personal AI Agents Could Become Digital Advocates in an AI-Dominated World

 

As generative AI agents proliferate, a new concept is gaining traction: AI entities that act as loyal digital advocates, protecting individuals from overwhelming technological complexity, misinformation, and data exploitation. Experts suggest these personal AI companions could function similarly to service animals—trained not just to assist, but to guard user interests in an AI-saturated world. From scam detection to helping navigate automated marketing and opaque algorithms, these agents would act as user-first shields. 

At a recent Imagination in Action panel, Consumer Reports’ Ginny Fahs explained, “As companies embed AI deeper into commerce, it becomes harder for consumers to identify fair offers or make informed decisions. An AI that prioritizes users’ interests can build trust and help transition toward a more transparent digital economy.” The idea is rooted in giving users agency and control in a system where most AI is built to serve businesses. Panelists—including experts like Dazza Greenwood, Amir Sarhangi, and Tobin South—discussed how loyal, trustworthy AI advocates could reshape personal data rights, online trust, and legal accountability. 

Greenwood drew parallels to early internet-era reforms such as e-signatures and automated contracts, suggesting a similar legal evolution is needed now to govern AI agents. South added that AI agents must be “loyal by design,” ensuring they act within legal frameworks and always prioritize the user. Sarhangi introduced the concept of “Know Your Agent” (KYA), which promotes transparency by tracking the digital footprint of an AI. 

With unique agent wallets and activity histories, bad actors could be identified and held accountable. Fahs described a tool called “Permission Slip,” which automates user requests like data deletion. This form of AI advocacy predates current generative models but shows how user-authorized agents could manage privacy at scale. Agents could also learn from collective behavior. For instance, an AI noting a negative review of a product could share that experience with other agents, building an automated form of word-of-mouth. 

This concept, said panel moderator Sandy Pentland, mirrors how Consumer Reports aggregates user feedback to identify reliable products. South emphasized that cryptographic tools could ensure safe data-sharing without blindly trusting tech giants. He also referenced NANDA, a decentralized protocol from MIT that aims to enable trustworthy AI infrastructure. Still, implementing AI agents raises usability questions. “We want agents to understand nuanced permissions without constantly asking users to approve every action,” Fahs said. 

Getting this right will be crucial to user adoption. Pentland noted that current AI models struggle to align with individual preferences. “An effective agent must represent you—not a demographic group, but your unique values,” he said. Greenwood believes that’s now possible: “We finally have the tools to build AI agents with fiduciary responsibilities.” In closing, South stressed that the real bottleneck isn’t AI capability but structuring and contextualizing information properly. “If you want AI to truly act on your behalf, we must design systems that help it understand you.” 

As AI becomes deeply embedded in daily life, building personalized, privacy-conscious agents may be the key to ensuring technology serves people—not the other way around.

AI Agents Raise Cybersecurity Concerns Amid Rapid Enterprise Adoption

 

A growing number of organizations are adopting autonomous AI agents despite widespread concerns about the cybersecurity risks they pose. According to a new global report released by identity security firm SailPoint, this accelerated deployment is happening in a largely unregulated environment. The findings are based on a survey of more than 350 IT professionals, revealing that 84% of respondents said their organizations already use AI agents internally. 

However, only 44% confirmed the presence of any formal policies to regulate the agents’ actions. AI agents differ from traditional chatbots in that they are designed to independently plan and execute tasks without constant human direction. Since the emergence of generative AI tools like ChatGPT in late 2022, major tech companies have been racing to launch their own agents. Many smaller businesses have followed suit, motivated by the desire for operational efficiency and the pressure to adopt what is widely viewed as a transformative technology.  

Despite this enthusiasm, 96% of survey participants acknowledged that these autonomous systems pose security risks, while 98% stated their organizations plan to expand AI agent usage within the next year. The report warns that these agents often have extensive access to sensitive systems and information, making them a new and significant attack surface for cyber threats. Chandra Gnanasambandam, SailPoint’s Executive Vice President of Product and Chief Technology Officer, emphasized the risks associated with such broad access. He explained that these systems are transforming workflows but typically operate with minimal oversight, which introduces serious vulnerabilities. 

Further compounding the issue is the inconsistent implementation of governance controls. Although 92% of those surveyed agree that AI agents should be governed similarly to human employees, 80% reported incidents where agents performed unauthorized actions or accessed restricted data. These incidents underscore the dangers of deploying autonomous systems without robust monitoring or access controls. 

Gnanasambandam suggests adopting an identity-first approach to agent management. He recommends applying the same security protocols used for human users, including real-time access permissions, least privilege principles, and comprehensive activity tracking. Without such measures, organizations risk exposing themselves to breaches or data misuse due to the very tools designed to streamline operations. 

As AI agents become more deeply embedded in business processes, experts caution that failing to implement adequate oversight could create long-term vulnerabilities. The report serves as a timely reminder that innovation must be accompanied by strong governance to ensure cybersecurity is not compromised in the pursuit of automation.

AI in Cybersecurity Market Sees Rapid Growth as Network Security Leads 2024 Expansion

 

The integration of artificial intelligence into cybersecurity solutions has accelerated dramatically, driving the global market to an estimated value of $32.5 billion in 2024. This surge—an annual growth rate of 23%—reflects organizations’ urgent need to defend against increasingly sophisticated cyber threats. Traditional, signature-based defenses are no longer sufficient; today’s adversaries employ polymorphic malware, fileless attacks, and automated intrusion tools that can evade static rule sets. AI’s ability to learn patterns, detect anomalies in real time, and respond autonomously has become indispensable. 

Among AI-driven cybersecurity segments, network security saw the most significant expansion last year, accounting for nearly 40% of total AI security revenues. AI-enhanced intrusion prevention systems and next-generation firewalls leverage machine learning models to inspect vast streams of traffic, distinguishing malicious behavior from legitimate activity. These solutions can automatically quarantine suspicious connections, adapt to novel malware variants, and provide security teams with prioritized alerts—reducing mean time to detection from days to mere minutes. As more enterprises adopt zero-trust architectures, AI’s role in continuously verifying device and user behavior on the network has become a cornerstone of modern defensive strategies. 

Endpoint security followed closely, representing roughly 25% of the AI cybersecurity market in 2024. AI-powered endpoint detection and response (EDR) platforms monitor processes, memory activity, and system calls on workstations and servers. By correlating telemetry across thousands of devices, these platforms can identify subtle indicators of compromise—such as unusual parent‑child process relationships or command‑line flags—before attackers achieve persistence. The rise of remote work has only heightened demand: with employees connecting from diverse locations and personal devices, AI’s context-aware threat hunting capabilities help maintain comprehensive visibility across decentralized environments. 

Identity and access management (IAM) solutions incorporating AI now capture about 20% of the market. Behavioral analytics engines analyze login patterns, device characteristics, and geolocation data to detect risky authentication attempts. Rather than relying solely on static multi‑factor prompts, adaptive authentication methods adjust challenge levels based on real‑time risk scores, blocking illicit logins while minimizing friction for legitimate users. This dynamic approach addresses credential stuffing and account takeover attacks, which accounted for over 30% of cyber incidents in 2024. Cloud security, covering roughly 15% of the AI cybersecurity spend, is another high‑growth area. 

With workloads distributed across public, private, and hybrid clouds, AI-driven cloud security posture management (CSPM) tools continuously scan configurations and user activities for misconfigurations, vulnerable APIs, and data‑exfiltration attempts. Automated remediation workflows can instantly correct risky settings, enforce encryption policies, and isolate compromised workloads—ensuring compliance with evolving regulations such as GDPR and CCPA. 

Looking ahead, analysts predict the AI in cybersecurity market will exceed $60 billion by 2028, as vendors integrate generative AI for automated playbook creation and incident response orchestration. Organizations that invest in AI‑powered defenses will gain a competitive edge, enabling proactive threat hunting and resilient operations against a backdrop of escalating cyber‑threat complexity.