2026-02-04 18:00:26
In a world where ransomware strikes, attempts at data manipulation, and system-crippling intrusions against hospitals are on the rise, the healthcare sector has reached a breaking point. These are no longer attacks on mere data; they are on ventilators, on imaging machines, on diagnostic algorithms, and on the very systems that sustain patients’ lives. On a national level across the United States, even a single breach has the power to halt surgeries, divert ambulances, and put lives in peril.
\ It is precisely in this high-stakes environment that Nayeem has risen to prominence, challenging and redefining what protecting human life would come to mean in the Digital Age. Where traditional models of cybersecurity fell short, Nayeem introduced innovative models that today are quite rightly referred to as the yardstick for the security of clinical infrastructures around the country. His work has been a direct response to the structural vulnerabilities that plague modern hospitals and acts as a blueprint for resilience in a domain where minutes can determine outcomes.
\ The vulnerabilities of the healthcare industry are singular. MRI machines operate next to cloud-based patient portals. Legacy diagnostic tools interact with modern AI engines. One corrupted data point can lead to a misdiagnosis. Mindful of these realities, Nayeem applied his craft to the development of cybersecurity systems engineered for the clinical environment, not the corporate office. This commitment catalyzed two landmark innovations that have redefined the defensive posture of the contemporary healthcare institution.
\ The first was an AI-driven anomaly detection platform born from the aftermath of a 2022 ransomware attack that forced a regional hospital to cancel surgeries and suspend emergency services. Nayeem identified a critical flaw: manual response mechanisms and generic alerting systems were far too slow and unfocused. He spearheaded development of an AI-driven anomaly detection platform, able to identify abnormal activity in imaging, suspicious access to medication records, and deviations in device behavior. Most importantly, it could trigger automated containment protocols in real time. This system made a crucial shift by taking response times down from hours to minutes, one which the leadership of the hospital characterized as “the difference between containing an incident and facing a catastrophic clinical shutdown.”
\ The second was a healthcare-specific cybersecurity framework that broke away from the traditional corporate governance model. Instead of making clinical workflows adapt to inflexible IT structures, Nayeem built a framework that was designed around how medicine is actually practiced in the real world. It featured tamper-proof logs for laboratory data, role-based access pathways optimized for surgical teams, automated compliance safeguards for cloud-hosted medical systems, and security protocols for aging diagnostic devices running older software. Facilities that adopted this framework immediately started to report a stabilization of operations; one facility credited it with securing neonatal equipment against possible acts of sabotage—a development that soon drew interest from national healthcare networks.
\ Recognized industry leaders have publicly acknowledged the transformational nature of Nayeem’s work. “Before this framework, we were constantly patching vulnerabilities reactively,” noted a senior clinical technology director.
Mohammed re-engineered our entire approach. His work created an invisible shield that lets clinicians focus on patients while the system deflects threats silently in the background.
Reflecting on his mission, Nayeem has said, “In healthcare, cybersecurity is not an IT function— it is patient safety. Every safeguard we build protects someone’s life.”
\ Besides hands-on innovation, Nayeem has influenced the academic and regulatory understanding of healthcare security. His collaborative research on “hybrid data corruption” attacks-threats where hackers subtly manipulate clinical information to trigger harmful treatment decisions-has reshaped the industry’s view of integrity-based threats. His findings led to the widespread adoption of automated data-validation protocols for clinical integrity threats now recommended by national cybersecurity agencies. What sets Nayeem’s leadership apart is that he does not consider future threats as hypothetical. By the time many institutions considered quantum-era attacks and AI-generated intrusions as threats over the horizon, he had already embedded forward-resilient design in today’s hospital architectures. His segmentation models minimise the blast radius of IoT intrusions, and today his frameworks are being referenced by medical device manufacturers seeking to build security into their next generation of equipment.
\ As AI diagnostics, robotic systems, and interconnected devices expand in use within hospitals, the threat landscape will only continue to evolve. But Nayeem’s work offers a proactive model—one that weaves security into the fabric of clinical operations rather than an added layer. Through his breakthroughs in detection, governance, and predictive security, Mohammed Nayeem established himself as a distinguished leader whose contributions safeguard not just data but the lives, trust, and well-being of patients from around the world.
\
:::tip This story was distributed as a release by Jon Stojan under HackerNoon’s Business Blogging Program.
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2026-02-04 18:00:07
Ghulam Murtaza emerged from Khairpur, a rural town in Pakistan, where his first interactions with technology began at age ten by digitizing his father’s construction ledgers. While most viewed technology as recreational, Murtaza saw computers as tools to solve real problems.
His academic path later included a Master of Science in Robotics from the University at Buffalo, and he is currently a Control System Lead at Amazon’s CBRE in Oregon. Murtaza’s expertise spans robotics, industrial automation, custom data engineering, and AI-driven process optimization, with work that is now documented across the Amazon supply chain.
The growing trend of operational technology (OT) modernization inside industrial giants like Amazon is reshaping the way fulfillment centers operate. Murtaza represents a new generation of engineers driving this change—those whose careers are defined by self-taught resilience and scalable, measurable solutions rather than credentials alone.
\
Murtaza’s first exposure to technology stemmed from necessity rather than abundance. “When a computer first came to our house in Khairpur, my younger brother used it to play GTA, but I was only ten and used it to help my father with his construction business,” he reflects. Translating handwritten notes into organized Word documents became his early project, laying the foundation for future engineering endeavors.
The limitations of a small town created a mindset oriented toward self-reliance.
Growing up in a small town gave me hunger, patience, and depth. I learned early that if I wanted something, I’d have to build it myself.
This attitude would eventually support his transition beyond local boundaries. This translates that backgrounds like his often produce engineers with stronger on-the-ground problem-solving skills.
\
Resource constraints forced Murtaza to improvise in learning programming. “The biggest challenge wasn’t access—it was the lack of guidance. In ninth grade, I was introduced to GW-BASIC, and while others treated it as just a subject, I became obsessed.” He invested hours in self-study, developing projects that exceeded standard curriculum expectations, including a train ticketing system and robotics competition entries.
“Later, I entered a national robotics competition, built an Arduino-based robot in C++, and won first place. That $1,200 prize changed my confidence forever.” This experience underlined a lesson now echoed in industrial innovation: consistency and curiosity can outperform pedigree when resources are scarce. Industry analysis by Tom White affirms that engineers who learn by doing—often in less resourced regions—can apply technical problem-solving in complex real-world environments.
\
Murtaza’s career path was not linear. “Choosing civil engineering wasn’t my dream, but I did it out of respect for my father,” he says. Even while adhering to his family’s wishes, he maintained a personal commitment to coding and robotics, managing to balance obligation with personal goals.
“Over time, my father saw that the version of me doing what I loved was stronger and happier,” Murtaza recalls. The duality of his experience—balancing family duty with technical ambition—mirrors the realities faced by many engineers from emerging economies.
“When relatives mocked me for chasing robotics and the U.S., I chose belief over validation. I realized I’d rather risk failure than live with regret.” This internal resilience serves as a crucial element in innovation teams, where persistence under pressure can determine technological adoption success.
\
Upon joining Amazon, Murtaza identified “silent slowdowns”—minor process inefficiencies with significant impact as fulfillment centers scaled. “At Amazon’s PDX8 launch, I saw technicians losing 45 minutes just finding parts and engineers walking miles to troubleshoot equipment.” Rather than launching into coding, he adopted an ethnographic approach: “Instead of rushing to code, I listened. I shadowed technicians, asked questions, and learned their workflows.”
This method, rooted in deep observation, reflects his upbringing: “When you grow up with little, you learn to respect people’s time. My goal was simple—to remove friction and give time back to those keeping the system alive.” Such principles of lean workflow are now central to modern automation at scale, as detailed in recent field reports.
\
Among his most cited solutions is the Parts Lookup App. “It started when a technician spent nearly 40 minutes searching for a single part. I scraped over 1,200 pages of vendor data, merged it with Amazon inventory and drawings, and built a searchable system that shows exact part locations instantly.”
The app, running on cloud infrastructure for just $0.35 per month, supports over 150 technicians and is live across multiple buildings. “It saves 15–20 minutes per search, translating into hundreds of hours saved monthly. That’s the kind of impact I live for.” The application represents a breakthrough in workflow automation, reducing technician search time, and is projected to save 300–600 hours monthly across 10 Amazon sites.
Murtaza’s approach involved acting as a data engineer, software developer, and UX designer. The tool bridges operational technology and information technology, embodying a multidisciplinary ethos increasingly sought in industrial automation.
\
Murtaza’s toolbox extends beyond inventory lookup. “I built a real-time VFD monitoring system at PDX8 with 2-second checks and Slack alerts for overloads. I included a trend app to predict motor burnout early, saving ~2 hours of downtime per incident and preventing $5K+ in potential motor damage per event,” he details.
Another critical system, the Ambaflex Trend Tracker, enables technicians to monitor spiral conveyors and troubleshoot faults without engineer intervention. These real-time sensor dashboard solutions, as validated by sector research, have led to over $25,000 in annual labor savings and up to $5.76 million in avoided downtime annually across three sites.
His AI-powered CtrlG assistant, trained on hundreds of technical manuals, has reduced troubleshooting time from up to an hour to under a minute per issue, with industry estimates suggesting that preventing a single downtime incident can save between $750,000 and $1 million at Amazon fulfillment centers, according to published data.
\
Advancing these tools came during one of the most challenging eras for tech professionals. “Solitude became my advantage. I broke complex problems into small steps and stayed structured,” Murtaza says about the period when widespread tech layoffs and visa pressures dominated headlines.
“What fueled me most was hearing technicians say, ‘You saved us today.’ I wasn’t hired to build tools—but I did it anyway, because value creates security.” Such mindsets, documented in operations literature, are increasingly relevant in environments where job functions shift rapidly and independent innovation bridges organizational gaps.
Recognition, including the Amazon RME ‘Raise the Bar’ Award for scalable, low-cost solutions, followed his consistent delivery of measurable impact as outlined by Tom White. Murtaza’s case demonstrates how outcomes, not job titles, become defining features of engineering legacy.
\
Reflecting on his journey, Murtaza’s message is both pragmatic and optimistic. “Your background is not a weakness—it’s your edge. If you give something your full effort, the universe meets you halfway. Don’t wait for permission to solve problems.”
He stresses direct engagement: “Shadow users, obsess over workflows, and build things that genuinely help people. I didn’t chase titles—I chased impact. Focus on creating value, and recognition will follow.”
This outlook aligns with the growing discourse on workforce transformation, where unconventional backgrounds and lived experience are increasingly recognized as assets in shaping automation’s future within industry leaders such as Amazon.
Murtaza’s path underscores how self-education, creative persistence, and proximity to real operational challenges can drive progress regardless of origin. His story, set against the backdrop of shifting industry and societal expectations, highlights that today’s most valuable engineering solutions begin where lived experience and technical ingenuity intersect.
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:::tip This story was distributed as a release by Jon Stojan under HackerNoon’s Business Blogging Program.
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2026-02-04 15:10:57
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2026-02-04 14:00:03
The quick rise of Large Language Models has given hackers a new and profitable way to attack. LLMjacking, which is the illegal hijacking of self-hosted LLM infrastructure for bad purposes, is an immediate threat. A lot of people in the security community have talked about this. Recent news stories have come out about "Operation Bizarre Bazaar," a complicated plan that searches the internet for exposed LLM and Model Context Protocol endpoints, takes control of them, and sells the stolen resources on dark web marketplaces. There have been more than 35,000 attack sessions, with an average of 972 attacks per day. This threat is not just a theory; it is a clear and present danger to any organization that uses its own LLM infrastructure.
This article goes into great detail about how LLMjacking works, what weaknesses it takes advantage of, and most importantly, the code-level solutions you can use right now to protect your self-hosted LLMs from this growing threat.
LLMjacking is not merely a singular exploit, but rather a complex, multi-stage operation characterized by a systematic approach. Comprehending the mechanisms behind these attacks constitutes the initial phase in formulating effective defenses against them.
Bots that work automatically are always looking for open ports and services that are linked to popular self-hosted LLM frameworks on the internet. The main goals are:
Once a potential target is identified, the infrastructure of an entity, lets say, "silver.inc" evaluates the endpoint within 8 hours or even less. The validation procedure then involves dispatching a closely orchestrated sequence of API requests to:
Validated endpoints are added to "The Unified LLM API Gateway," a marketplace that offers access to over 30 different LLMs—all running on hijacked infrastructure. This platform is hosted on bulletproof infrastructure in the Netherlands and marketed through Discord and Telegram channels. Customers pay via cryptocurrency or a payment facilitating applications to access these stolen resources.
The financial and security implications of an LLMjacking attack are severe. Running inference on big models can quickly add up to huge cloud bills because of the high cost of computing. Also, if the hijacked LLM can get to sensitive internal data through retrieval-augmented generation or function calling, the chances of data being stolen are very high. Finally, a hacked LLM endpoint can be used as a base for moving around your network.
The main reason LLMjacking works so well is that many self-hosted LLM frameworks are made for local, single-user development and aren't secure by default for production or web-exposed environments. The same tools that have made powerful AI available to everyone have also made it easier for people who aren't careful to get hacked.
Take a look at Ollama, which is one of the best tools for running LLMs on your own computer. Ollama is set up so that you don't have to log in when you go to http://localhost:11434. This makes sense for local development, but as soon as that port is open to the internet, either on purpose or by mistake, it becomes a major security hole.
The good news is that these problems can be fixed if you do things the right way. In the next part, we'll talk about the specific things you can do to protect your self-hosted LLM infrastructure.
You need to use a defense-in-depth approach to protect your self-hosted LLM infrastructure. You can't just put a firewall in front of your server; you need to add security controls to every layer of the stack. These are the most important things you need to do.
The most important thing you can do to keep your self-hosted LLM safe is to never let the raw, unauthenticated endpoint be seen by the internet. You should always put a reverse proxy, like Nginx or Caddy, in front of your LLM service instead. This adds an important layer of security, such as authentication, TLS encryption, and rate limiting.
Ollama is a great way to run LLMs on your own computer, but you shouldn't put it on the web. This is how to use Nginx to add a layer of API key authentication to your Ollama instance.
To start, you need to make a file to keep your valid API keys in. In this case, we'll call it apikeys.txt:
# apikeys.txt
my-secret-api-key-1
another-valid-key
Next, you will need to configure Nginx to act as a reverse proxy and check for a valid API key in the Authorization header of incoming requests. Here is a sample Nginx configuration file:
# /etc/nginx/sites-available/ollama.conf A map to see if the API key you gave is valid.
default 0; "Bearer my-secret-api-key-1" 1; "Bearer another-valid-key" 1;
server { listen 80; server_name your-llm-domain.com;
location / {
# If the API key is not valid, return 401 'Unauthorized' and reject requests.
# Proxy requests to the Ollama backend proxy_pass http://localhost:11434; proxy_set_header Host $host; proxy_set_header X-Real-IP $remote_addr; proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for; proxy_set_header X-Forwarded-Proto $scheme;
# Raise the timeout for inference requests that take a long time to process. proxy_read_timeout 300s; proxy_connect_timeout 75s;
}
}
This does two important things:
This setup means that every request to your Ollama instance must include a valid API key in the Authorization header:
curl http://your-llm-domain.com/api/generate \-H "Authorization: Bearer my-secret-api-key-1" \
-d '{ "model": "llama2", "prompt": "Why is the sky blue?" }'
The Model Context Protocol is a great way to make AI agents, but it also makes them less safe. Attackers can get into your LLMs and any tools or APIs that the MCP server is set up to use if it is not protected. To protect an MCP server, you need to use more than one layer of security:
Your MCP server should not be directly connected to the public internet whenever possible. If only a small number of clients need to be able to access it, use a VPN or other network-level controls to limit access. Use security groups or network policies to make sure that only approved services can talk to your MCP server when you deploy it in the cloud.
MCP doesn't say which authentication method to use, so you have to choose one yourself. You should at least require a unique API key that can't be guessed for every request. If you need to keep things more private, think about using a stronger authentication method like OAuth 2.0 or mutual TLS.
You should also use a fine-grained permission model to control which clients can use which tools. For instance, you could have a "read-only" client that can only use tools that give you information and a "read-write" client that can use tools that change data.
You should never trust input from the client, just like you shouldn't trust input from any other web app. To stop injection attacks, you should carefully clean and check all input to your MCP server. Also, you should clean up the output from your tools before sending it back to the client to stop information from leaking.
Rate limiting is a good way to protect against denial-of-service attacks and the resource abuse that happened in Operation Bizarre Bazaar. Limiting the quantity of the requests a client can make within a specified timeframe serves as a safeguard against the potential for a single malicious actor to overload your system or incur significant costs.
Various strategies exist for the implementation of rate limiting:
In a production system, you would want to use a more robust and scalable solution, such as Redis with sliding window counters, or a dedicated rate-limiting library like flask-limiter.
In addition to the primary defenses outlined previously, consider implementing the following security measures:
Maintain comprehensive records of all API requests, documenting the API key utilized, the originating IP address, the requested model, and the total number of tokens generated. This will assist in identifying unusual behavior and investigating potential security issues. To facilitate analysis, implement a centralized logging system such as ELK Stack or Splunk.
Periodically scan your external attack surface to identify any open services. Utilize tools such as nmap or cloud-native security scanners to identify misconfigured endpoints prior to potential exploitation by attackers.
In addition to implementing rate limiting, it is advisable to establish strict usage limits for each API key. For example, it is possible to allocate a limit of 1 million tokens per key on a monthly basis. This provides an additional layer of security to prevent the misuse of resources.
Make sure to keep your LLM frameworks, reverse proxies, and operating systems upgraded at all times to patch known vulnerabilities.
The rise of LLMjacking should wake up the AI community. We can no longer afford to ignore security. We need to keep pushing the limits of what AI can do, but we also need to put security first and make systems that can withstand attacks.
The Operation Bizarre Bazaar campaign shows that attackers are actively looking for and using weak LLM infrastructure on a large scale. The good news is that the defenses in this article—putting your LLM behind a reverse proxy, using strong authentication and authorization, and enforcing strict rate limiting—are all well-known security measures that don't take a lot of work to put into place.
We have the tools we need to make AI systems safe. We, the engineers and developers who work on the front lines, are the ones who need to use them. You can keep your AI infrastructure safe and secure by taking these steps today. They will also protect your business from the growing threat of LLMjacking.
\ \ \ \
2026-02-04 13:52:49
Configure Kafka + Flink networking, package a Kafka consumer JAR, upload it, and run it as a scheduled Flink task node end-to-end.
2026-02-04 13:48:02
A simple project that uses AI to build a webpage that turns simple text into an index. html.
