Nvidia, headquartered in Santa Clara, California, has emerged as a beacon of technological innovation, much as the industrial giants of a bygone era reshaped their worlds. Its latest creations—the Hopper GPU and Blackwell systems—are not merely advancements in computing; they are the tools of a new industrial revolution, their influence stretching across industries and into the lives of millions. As measured by its astonishing financial results, the company’s trajectory reflects the unparalleled demand for these tools.

The latest quarter’s revenue, a staggering $35.08 billion, represents a 94% leap from the $18.12 billion of a year prior—a figure that would have seemed fantastical not long ago. Its net income soared to $19.31 billion, more than double last year’s third-quarter figure of $9.24 billion. Even after accounting for adjustments, earnings reached 81 cents per share, outpacing Wall Street’s expectations of 75 cents per share on projected revenues of $33.17 billion, according to FactSet.

This is no mere coincidence of market forces or transient trends. Nvidia’s success is rooted in the astonishing versatility of its Hopper GPU and Blackwell systems. Their applications span a broad spectrum—from artificial intelligence to cybersecurity—each deployment, which is a testament to their transformative power. These are not simply tools but harbingers of a future where the limits of what machines can do are redrawn with each passing quarter.

The Hopper and Blackwell systems are not isolated achievements; they are central to Nvidia’s rise as a leader in innovation, its vision ever fixed on the horizon. The technology reshapes industries as varied as medicine, entertainment, finance, and autonomous systems, weaving a thread of progress through all it touches. Like the significant advancements of earlier eras, these creations do not merely answer existing questions; they pose new ones, unlocking doors to realms previously unimagined.

Thus, Nvidia’s record-breaking quarter is a financial milestone and a marker of its place in history. As it shapes the future of computing, the company’s influence extends far beyond the confines of Silicon Valley. It is, in a sense, a reflection of our age—a testament to human ingenuity and the ceaseless drive to innovate, explore, and create.

By Skeeter Wesinger

November 20, 2024

In the age of relentless digital transformation, software security remains both a bulwark and a vulnerability. The deployment of Large Language Models (LLMs) as tools to fortify this critical frontier marks a turning point, one that evokes the blend of promise and peril characteristic of technological revolutions. Like radar in the skies of the Second World War, these LLMs have the potential to detect threats unseen by the human eye, provided they are used judiciously and in concert with other defenses.

The power of LLMs lies in their unparalleled ability to analyze vast swaths of source code with a speed and thoroughness that human developers cannot match. From detecting the cracks in the foundation—buffer overflows, injection vulnerabilities, hardcoded credentials, and improper input validation—to recognizing subtle, non-obvious threats that arise from the interplay of complex systems, these models operate with an unrelenting vigilance. What might take a team of skilled engineers days or weeks to unearth, an LLM can flag in minutes, scouring line after line with mechanical precision.

This capability is most potent during the prerelease phase of development when the entire source code is laid bare. It is here, before a product sees the light of day, that LLMs can expose vulnerabilities lurking in the shadows, vulnerabilities that, if left unchecked, could later metastasize into full-blown breaches. The cost of such breaches is not merely financial but reputational, eroding the trust that underpins all digital enterprises.

Consider the subtle artistry of an LLM detecting insecure data handling in a function, not because the code itself appears flawed but because of the way it interacts with calls elsewhere in the codebase. This is no brute-force analysis; it is an exercise in pattern recognition, a demonstration of how machines are learning to see the forest as well as the trees.

Yet, as with radar, the promise of LLMs must be tempered by realism. They are not a standalone defense, nor do they obviate the need for more traditional measures. They complement fuzzing, which tests software by bombarding it with random inputs and identifying areas where such testing might be most fruitful. They serve as a first line of defense, flagging issues for human reviewers who can then apply their judgment and experience to resolve them.

Moreover, LLMs can act as vigilant assistants during development itself, offering real-time suggestions for secure coding practices. In doing so, they become not merely tools of analysis but instruments of prevention, guiding developers away from insecure practices before they become embedded in the code.

What sets LLMs apart is their scalability. Unlike manual reviews, which are labor-intensive and constrained by human resources, LLMs can analyze sprawling codebases or even multiple projects simultaneously. This scalability is nothing short of transformative for organizations tasked with securing complex software ecosystems.

Used in concert with fuzzing, manual reviews, and other security protocols, LLMs represent the new frontline in software security. They bring automation and scale to an arena that has long been constrained by the limitations of time and manpower. Their ability to access and analyze full source code during development ensures that the vulnerabilities they uncover are not only flagged but actionable.

The lessons of history remind us that no single technology, no matter how transformative, can operate in isolation. LLMs are tools of immense potential, but it is the interplay of man and machine, of automation and expertise, that will ultimately determine their success. In this emerging battle for the sanctity of our digital infrastructures, LLMs are an ally of immense promise, provided we deploy them wisely and with an understanding of their limitations.

By Skeeter Wesinger

November 18, 2024

https://www.linkedin.com/pulse/new-frontline-security-technology-skeeter-wesinger-olzbe

The advent of Generative AI (GenAI) has begun to transform the professional services sector in ways that are reminiscent of past industrial shifts. In pricing models, particularly, GenAI has introduced an undeniable disruption. Tasks once demanding hours of meticulous human effort are now being automated, ushering in a reduction of operational costs and a surge in market competition. Consequently, firms are being drawn towards new pricing paradigms—cost-plus and competitive pricing structures—whereby savings born of automation are, at least in part, relayed to clients.

GenAI’s influence is most visible in the routinized undertakings that have traditionally absorbed the time and energy of skilled professionals. Drafting documents, parsing data, and managing routine communications are now handled with remarkable precision by AI systems. This liberation of human resources allows professionals to concentrate on nuanced, strategic pursuits, from client consultation to complex problem-solving—areas where human intellect remains irreplaceable. Thus, the industry drifts from the conventional hourly billing towards a value-centric pricing system, aligning fees with the substantive outcomes delivered, not merely the hours invested. In this, GenAI has flattened the landscape: smaller firms, once marginalized by the resources and manpower of larger entities, can now stand as credible competitors, offering similar outputs at newly accessible price points.

Further, the rise of GenAI has spurred firms to implement subscription-based or tiered pricing models for services once bespoke in nature. Consider a client subscribing to a GenAI-powered tool that provides routine reports or documentation at a reduced rate, with options to escalate for human oversight or bespoke customization. This hybrid model—where AI formulates initial drafts and human professionals later refine them—has expanded service offerings, giving clients choices between an AI-driven product and one fortified by expert review. In this evolving terrain, firms are experimenting with cost structures that distinguish between AI-generated outputs and those augmented by human intervention, enabling clients to opt for an economical, AI-exclusive service or a premium, expert-reviewed alternative.

Investment in proprietary GenAI technology has become a distinguishing factor among leading firms. To some clients, these customized AI solutions—tailored for fields such as legal interpretation or financial forecasting—exude an allure of exclusivity, thereby justifying the elevated fees firms attach to them. GenAI’s inherent capacity to track and quantify usage has also paved the way for dynamic pricing models. Here, clients are billed in direct proportion to their engagement with GenAI’s capabilities, whether through the volume of reports generated or the features utilized. In this, professional services firms have crafted a usage-based pricing system, a model flexible enough to reflect clients’ actual needs and consumption.

However, with progress comes the shadow of regulation. As governments and regulatory bodies move to address GenAI’s ethical and data implications, professional service firms, particularly in sensitive sectors like finance, healthcare, and law, may find themselves bearing the weight of compliance costs. These expenses will likely be passed on to clients, especially where data protection and GenAI-driven decision-making demand rigorous oversight.

In the aggregate, GenAI’s integration is compelling professional services firms towards a dynamic, flexible, and transparent pricing landscape—one that mirrors the dual efficiencies of AI and the nuanced insights of human expertise. Firms willing to incorporate GenAI thoughtfully are poised not only to retain a competitive edge but also to expand their client offerings through tiered and value-based pricing. The age of GenAI, it seems, may well be one that redefines professional services, merging the best of human acumen with the swift precision of artificial intelligence.

Skeeter Wesinger

November 8, 2024

https://www.linkedin.com/pulse/age-generative-ai-skeeter-wesinger-oe7pe

The Ultra Ethernet Consortium (UEC) has delayed release of the version 1.0 of specification from Q3 2024 to Q1 2025, but it looks like AMD is ready to announce an actual network interface card for AI datacenters that is ready to be deployed into Ultra Ethernet datacenters. The new unit is the AMD Pensando Pollara 400, which promises an up to six times performance boost for AI workloads. In edge deployments, running a firewall directly on the NIC allows for more efficient security enforcement, where system resources may be limited. Using the NIC for firewall tasks frees up CPU cores, allowing your system to scale more efficiently without degrading performance as traffic volumes increase.

The AMD Pensando Pollara 400 is a 400 GbE Ultra Ethernet card based on a processor designed by the company’s Pensando unit. The network processor features a processor with a programmable hardware pipeline, programmable RDMA transport, programmable congestion control, and communication library acceleration. The NIC will sample in the fourth quarter and will be commercially available in the first half of 2025, just after the Ultra Ethernet Consortium formally publishes the UEC 1.0 specification. Businesses can implement NIC-based firewalling to manage traffic across VLANs or isolated network segments, enhancing network security without the need for dedicated firewall hardware.

Pollara 400

The AMD Pensando Pollara 400 AI NIC is designed to optimize AI and HPC networking through several advanced capabilities. One of its key features is intelligent multipathing, which dynamically distributes data packets across optimal routes, preventing network congestion and improving overall efficiency. The NIC also includes path-aware congestion control, which reroutes data away from temporarily congested paths to ensure continuous high-speed data flow.

The AMD Pensando Pollara 400 AI NIC supports advanced programmability and can be integrated with a development kit that is available for free. The AMD Pensando Software-in-Silicon Development Kit (SSDK) provides a robust environment for building and deploying applications directly on the NIC, allowing you to offload networking, firewall, encryption, and even AI inference tasks from the CPU.

The SSDK supports programming in P416 for fast path operations, as well as C and C++ for more traditional processing tasks. It provides full support for network and security functions like firewalling, IPsec, and NAT, allowing these to be handled directly by the NIC rather than the host CPU. Developers can use the provided reference pipelines and code samples to quickly get started with firewall implementations or other network services.

The SDK and related tools are open and accessible via GitHub and AMD’s official developer portals, enabling developers to experiment with and integrate Pensando’s NICs into their systems without licensing fees. Some repositories and tools are available directly on GitHub under AMD Pensando’s.

The delay in the release of the Ultra Ethernet Consortium’s (UEC) version 1.0 specification, initially expected in the third quarter of 2024 and now pushed to the first quarter of 2025, does little to shake the confidence of those observing AMD’s bold march forward. While others may have stumbled, AMD stands ready to unveil a fully realized network interface card (NIC) for AI datacenters—the AMD Pensando Pollara 400—an innovation poised to redefine the landscape of Ultra Ethernet data centers. This NIC, a formidable 400 GbE unit, embodies the very pinnacle of technological advancement. Designed by AMD’s Pensando unit, it promises no less than a sixfold increase in AI workload performance.

The Pollara 400’s impact goes beyond sheer processing power. At the edge, where resources are scarce and security paramount, the NIC performs firewall tasks directly, relieving the central processing unit from such burdensome duties. Herein lies its genius: by offloading these critical tasks, system scalability is enhanced, enabling traffic to flow unhindered and system performance to remain steady, even under mounting demands.

As we await the final specifications from the UEC, AMD has announced that the Pollara 400 will be available for sampling by the fourth quarter of 2024, with commercial deployment anticipated in early 2025. It is no mere stopgap solution—it is a harbinger of a new era in AI networking, built upon a programmable hardware pipeline capable of handling RDMA transport, congestion control, and advanced communication library acceleration.

Furthermore, the NIC’s intelligent multipathing is a feat of engineering brilliance. With its path-aware congestion control, this marvel dynamically directs data around congested network routes, ensuring that AI workloads are never hampered by the bottlenecks that so often plague high-performance computing.

The Pollara 400 is more than just hardware; it is an ecosystem supported by the AMD Pensando Software-in-Silicon Development Kit (SSDK), a free and versatile tool that allows developers to fully leverage its capabilities. Whether programming in P416 for high-speed operations or using C and C++ for more traditional tasks, developers can easily deploy firewalls, IPsec, and NAT directly onto the NIC itself, bypassing the need for traditional CPU involvement.

The SSDK provides not only the means but also the guidance to streamline development. From pre-built reference pipelines to comprehensive code samples, it invites developers to embrace the future of network security and AI processing, all while maintaining openness and accessibility via AMD’s repositories on GitHub. This is no longer just the work of a single company—it is a shared endeavor, opening new frontiers for those bold enough to explore them.

Thus, as AMD prepares to thrust the Pollara 400 into the spotlight, one thing becomes abundantly clear: the future of AI networking will not be forged in the server rooms of yesterday but at the cutting edge of what is possible, where firewalls, encryption, and AI tasks are handled in stride by a NIC that rewrites the rules.

Story By

Skeeter Wesinger

October 11, 2024

 

https://www.linkedin.com/pulse/amd-pensando-pollara-400-skeeter-wesinger-yulwe

In the ever-evolving landscape of cybersecurity, where every vulnerability is a potential chink in the armor, penetration testers, often known as “Tiger Teams,” are equipped with an array of sophisticated tools to expose the frailties of modern networks and systems. These tools, while small in stature, are formidable in function.


Take, for instance, the Plunder Bug. It is no larger than a thumb drive but operates with the efficiency of a seasoned spy. Its purpose is passive yet critical: network sniffing. When embedded between a device and a network connection, it quietly captures traffic without interfering, all while remaining undetected. Plugged into a mobile device via USB, it provides real-time insights into network vulnerabilities, offering testers a mobile command center from which they can dissect the data flow.
Then there’s the Shark Jack, a sleek, portable penetration tool that embodies the speed and stealth of its namesake. This tool connects swiftly to a network, scanning it for weaknesses with a precision akin to a predator stalking its prey. Whether it’s identifying vulnerable devices or launching automated attacks, such as exploiting open ports, the Shark Jack serves as an efficient reconnaissance agent, laying bare the weak points of a wired network with ease.
The Bash Bunny is another versatile tool in the Tiger Team’s arsenal, designed to mimic trusted devices. Disguised as a simple USB device, it is a shape-shifter in the realm of penetration testing. Plugged into a target system, it becomes whatever the system desires—be it a keyboard or a mass storage device. But underneath this guise, it executes pre-written scripts, harvesting credentials, exfiltrating data, and injecting malicious payloads with surgical precision. It performs its tasks swiftly, leaving no trace save the evidence it seeks to uncover.
And who could overlook the infamous USB Rubber Ducky that appears to be innocuous enough, resembling the average USB drive one might carry in a pocket with a rubber ducky on the side. However, it is as dangerous as a loaded 44 magnum in the right hands. When connected to an unlocked system, it transforms into a virtual keyboard, inputting keystrokes at a speed no human could rival. A simple script loaded onto the Ducky can compromise a system in seconds, launching commands, creating backdoors, or altering configurations—all with the rapidity of a few automated keystrokes.
However, these tools are not limited to devices inserted by hand. There are Implants for Stealthy Access hardware planted within target environments for long-term, covert observation. Like an embedded spy within a fortified city, these implants lurk unnoticed in routers or servers, conducting surveillance, launching tests, and communicating remotely with their controllers. In the right hands, these hidden devices provide persistent access, gathering intelligence and launching attacks with impunity.
The Land Turtle is another clandestine agent designed for covert penetration. Small and unassuming, it plugs into an Ethernet port, immediately granting access to the network. Remotely controlled, it allows testers to move through the system undetected, pivoting to different points and exploiting vulnerabilities in real-time. Its low profile belies its formidable capabilities, which range from reconnaissance to remote control.
The Packet Squirrel performs its tasks in a similarly understated manner, manipulating packets of data with ease. Like its forest-dwelling counterpart, it is quick and nimble, placed between network connections where it sniffs packets, analyzing traffic for weaknesses or manipulating data to launch attacks like the dreaded Man-in-the-Middle (MitM).
Not to be forgotten is the OMG Cable, a wolf in sheep’s clothing if ever there was one. To the untrained eye, it is indistinguishable from an ordinary USB or Lightning cable. Yet inside this innocent facade lies a powerful weapon capable of injecting keystrokes and remotely controlling a target system. Its very design is its greatest strength—appearing harmless until the moment of attack, it can be deployed in environments where traditional tools might be too conspicuous.
Of course, in the world of wireless networks, the WiFi Pineapple reigns supreme. It is the master of deception, impersonating legitimate access points to lure unsuspecting devices into its web. Once connected, the Pineapple enables testers—or attackers—to intercept data, manipulate traffic, and launch MitM attacks. It is a tool that is both feared and respected, and it is able to compromise entire networks from a single-entry point.
And finally, we must acknowledge fufAI, a cutting-edge example of how artificial intelligence is revolutionizing penetration testing. This tool marries AI’s computational might with the time-honored practice of file fuzzing, probing for vulnerabilities with an intelligence and speed beyond that of its human counterparts. It is a tool of the future, yet its mission remains timeless: to uncover and exploit the weaknesses that others miss.
These are just a few of the tools in the Tiger Team’s ever-expanding toolbox. Each one plays its role in the grander strategy of penetration testing, revealing the vulnerabilities that lie hidden beneath the surface, waiting for the unwary to stumble.

By Skeeter Wesinger

September 30, 2024

References:
Jabbour, Kamal, and Jenny Poisson. “Cyber Risk Assessment in Distributed Information Systems.” The Cyber Defense Review 1, no. 1 (2016): 91–112.
http://www.jstor.org/stable/26267301.

The latest in a long line of cyber offensives against the United States, codenamed “Salt Typhoon,” once again lays bare the persistent vulnerability of American infrastructure to foreign adversaries, this time originating from China. These incursions are not isolated events but part of a calculated and multi-pronged campaign by advanced persistent threat (APT) groups whose very names, such as Volt Typhoon, reverberate with a chilling consistency. Each operation, carefully designed to probe the fault lines of U.S. cybersecurity, highlights the expanding ambitions of these foreign actors.


In the Salt Typhoon incident, the specter of compromised systems looms large. The focus falls on internet service providers (ISPs)—the backbone of American digital life—whose very arteries were reportedly infiltrated. Experts investigating the breach raise concerns that core infrastructure, specifically Cisco Systems routers, might have been involved. Though Cisco has vigorously denied that its equipment has succumbed to these attacks, the strategic intent of such operations is unmistakable. The threat of an enemy having unfettered access to sensitive networks, able to intercept data, disrupt services, and perhaps even surveil at will, constitutes nothing less than a significant peril to national security.

Yet, as is often the case in the field of cyber warfare, the public remains woefully unaware of the depth and frequency of these intrusions. The U.S., it seems, is forever on the defensive, scrambling to patch vulnerabilities while its adversaries, undeterred, press on. Beijing’s vast cyber apparatus, ever stealthy and insidious, demonstrates an ability to penetrate America’s most vital systems without firing a single shot. The implications, like so many moments in history, may only become clear after the damage has been done.

By Skeeter Wesinger

September 26, 2024

If it sounds like a spy novel, then it might just be true. Living off the Land (LotL) has become the first weapon in the new Cold War, this time between the United States and the People’s Republic of China. This modern battlefield is fought not with tanks or missiles but through the subtle, insidious operations of cyber espionage. It is a war where the battlefield is the internet, and the combatants are not soldiers but bots—small, autonomous programs acting as the foot soldiers of nation-state-sponsored operations.

These bots infiltrate corporate networks with surgical precision, using disguised communications to siphon off critical data and metadata. Unlike overt attacks that trigger alarms and demand immediate responses, these bots slip under the radar, blending seamlessly into the everyday digital traffic of a company. Their presence is not felt, their actions not seen, often for long stretches of time—weeks, months, or even years—until the damage is done.

And the damage, when it finally becomes clear, is catastrophic. Intellectual property is stolen, financial systems are compromised, and sensitive data leaks into the hands of foreign adversaries. The consequences of these attacks stretch far beyond individual companies, threatening the security and economic stability of nations. This new cold war is not fought on the ground but in the unseen spaces of cyberspace, where vigilance is the only defense.

A bot, once embedded within a company’s systems, begins its covert mission. It is a malicious program, programmed with a singular purpose: to relay the company’s most guarded secrets to its unseen master. But its greatest weapon is not brute force or direct confrontation; it is stealth. These bots conceal their communication within the very lifeblood of corporate networks—normal, everyday traffic. Disguised as benign emails, mundane web traffic, or encrypted transmissions that mimic legitimate corporate exchanges, they send stolen information back to their creators without raising suspicion. What appears to be routine data passing through the system is, in fact, a betrayal unfolding in real time.

Their quarry is not just the obvious treasures—financial records, intellectual property, or proprietary designs. The bots are after something less tangible but no less valuable: metadata. The seemingly trivial details about the data—who sent it, when, from where—might appear inconsequential at first glance. But in the hands of a skilled adversary, metadata becomes a road map to the company’s inner workings. It reveals patterns, weaknesses, and, critically, the pathways to deeper infiltration.

For the corporation targeted by such an attack, the consequences are manifold. There is, of course, the potential loss of intellectual property—the crown jewels of any enterprise. Plans, designs, and trade secrets—each a piece of the company’s competitive edge—can be stolen and replicated by rivals. Financial information, once in the wrong hands, can result in fraud, a hemorrhage of funds that can cripple a company’s operations.

Perhaps the most dangerous aspect of these attacks is that compromised security extends beyond the initial theft. Once attackers have a firm grasp of a company’s systems through stolen metadata, they possess a detailed map of its vulnerabilities. They know where to strike next. And when they do, the company’s defenses, having already been breached once, may crumble further. What begins as a single act of theft quickly escalates into a full-scale infiltration.

And then, of course, there is the reputation damage. In the modern marketplace, trust is currency. When customers or clients discover their data has been stolen, they do not hesitate to seek alternatives. The collapse of faith in a company’s ability to safeguard its information can lead to long-term harm, far more difficult to recover from than the financial blow. The loss of reputation is a slow bleed, often fatal.

In short, these disguised communications are the perfect cover for botnet activities, allowing attackers to slip past defenses unnoticed. And when the theft is finally uncovered—if it is ever uncovered—it is often too late. The stolen data has already been transferred, the secrets already sold. The damage, irreversible.

I am reminded of a particular case, an incident that unfolded with a certain sense of inevitability. A seemingly reputable bank auditor, entrusted with sensitive client documents, calmly removed them from the premises one afternoon, claiming a simple lunch break. Upon returning, security, perhaps acting on an inkling of suspicion, inspected the bag. Inside, the documents—marked confidential—lay exposed. The auditor, caught red-handed, was promptly denied further access, and the documents seized. But, alas, the harm had already been done. Trust had been violated, and in that violation, the company learned a hard lesson: Never trust without verifying.

Such is the nature of modern-day espionage—not just a battle of information, but of vigilance. And in this game, those who are too trusting, too complacent, will find themselves outmatched, their vulnerabilities laid bare.

Story by Skeeter Wesinger

September 23, 2024

A Large corporation with a well-funded cyber security team recently found out they’d been hacked! Their opponents used the combination of Living off the Land (LotL) techniques, fileless malware, legitimate credentials, and disguised communication makes these types of botnet activities incredibly difficult to detect, even for their expert tiger teams. Without the right focus on behavioral analysis, memory forensics, and network monitoring, even highly skilled teams could miss the subtle signs of this advanced form of attack.

If your teams are looking for traditional malware or malicious executables, they might not have focused on monitoring the activities of legitimate tools. Attackers are now using these tools can camouflage their actions to blend in with normal system administration tasks, so even if your tiger teams were monitoring system processes, the malicious use of these tools could easily go unnoticed.

One of the core advantages of LotL is the use of fileless techniques, which means that the attackers often don’t drop detectable malware on the system’s disk. Instead, they execute code directly in memory or utilize scripting environments like PowerShell. This method leaves behind little to no trace that traditional malware-detection tools or endpoint security would recognize.

The teams may have been conducting disk-based or signature-based analysis, which would be ineffective against fileless malware. Without leaving artifacts on the disk, the attackers bypass traditional endpoint detection, which would have been a major focus of the teams.
Since most of the activity occurs in memory, it would require deep memory forensics to uncover these types of attacks. If the tiger teams didn’t perform real-time memory analysis or use sophisticated memory forensics tools, they could miss the attack entirely.

Story By Skeeter Wesinger

September 19, 2024

U.S. authorities said on Wednesday that Flax Typhoon was used to infiltrate networks by exploiting known vulnerabilities and would then use existing system tools to perform filching.
The bots bypassed traditional security solutions like antivirus and intrusion detection systems because these systems were designed to detect known “malware signatures” or unusual file activity.

Therefore, the state-sponsored actor, in this case, the PRC, would avoid dropping large or sophisticated malware packages as these would increase the likelihood of triggering these defenses by relying on these stealth techniques of using legitimate system tools. They would minimize the use of any detectable malware. Therefore, attackers would avoid detection by the standard signature-based systems. After gaining initial access, the attackers dump user credentials from memory or password stores, allowing them to elevate privileges and move laterally across the network, accessing more sensitive systems and data.

Story By Skeeter Wesinger

September 19, 2024

Phishing attacks on LinkedIn are becoming increasingly sophisticated. State-sponsored actors are posing as recruiters from major headhunting firms like Korn Ferry, based in Los Angeles. These attackers aim to trick professionals into revealing sensitive information or downloading malware by creating profiles that closely resemble those of legitimate recruiters.

The process begins with attackers setting up fake LinkedIn profiles using stolen or fabricated information. A key red flag is the number of LinkedIn connections; if the profile has fewer than 10, it’s often a fake. These profiles frequently use company logos, professional headshots, and detailed job descriptions to appear credible. They may claim to represent well-known firms or major corporations like Google, Microsoft, or top-tier recruitment agencies to target professionals who aspire to work at such companies.

Once the profile is in place, the phishing attempt usually starts with a connection request or a direct message (InMail). The message will likely include a job offer or a unique career opportunity crafted to appeal to the recipient. The attacker might claim they’ve reviewed your profile and believe you are an excellent candidate for a prestigious, high-paying job—tactics often enhanced using AI to generate convincing content.

In the message, the fake recruiter may include a link, supposedly leading to a job portal, a document with more details, or a form to submit your CV. However, these links usually redirect to a malicious site designed to steal login credentials and personal information or install malware. Always hover over any links to inspect them before clicking. If the link looks suspicious, reconsider engaging.

Some of the most sophisticated attackers even create fake LinkedIn login pages or corporate websites to capture your username and password. It’s critical never to reuse passwords, as this could expose you to further attacks down the line. Additionally, they might request personal information such as your phone number, home address, or social security number under the pretense of a job application.

Remember, these attackers are not amateurs—they are state-sponsored actors. Be vigilant and cautious when interacting with unsolicited job offers on LinkedIn. Always verify the legitimacy of any recruiter before providing any information, and stay aware of the signs that an offer may be too good to be true.

 

Article by Skeeter Wesinger

September 16, 2024

 

 

https://www.linkedin.com/pulse/phishing-attacks-linkedin-skeeter-wesinger-5newe