The Illusion of Cybersecurity: Promised Protection, Delivered Disaster - Oh Boy #1

Let’s get one thing straight: we’re playing a game we’re destined to lose. Why? Because in cybersecurity, the defenders only start moving when the attackers have already won. When a company gets hit by a ransomware attack or a data breach, they panic, throw money at the problem, and beg for new security features. And what do the vendors do? They swoop in with shiny new tools, slap on a 2x price tag, and call it “innovation.” Meanwhile, the attackers are already three steps ahead, laughing all the way to the bank. At the end of the day, there needs to be a market where everyone can make money, including those who attack and defend. I suggest you examine the question of why nations turn to war when they experience famine or economic distress.

This is the cat-and-mouse game of cybersecurity: the cat doesn’t move until the mouse has already eaten the cheese. And guess what? The mouse is getting smarter, faster, and hungrier every day. The tools we rely on—YARA rules, sandbox analysis, EDR, EPP, and even next-gen AI-powered buzzword-filled solutions—are becoming increasingly ineffective against the next generation of AI-driven malware. So, what are we doing wrong? Let’s break it down.

Warning: This article will start with my main area of interest, malware, then things will get a little more fun (depending on who and what). You’ll witness my twisted mind—imagine a buffer overflow in my brain, where thoughts boil over like a pot left on the stove too long. I can (and will) proceed interrupted, jumping from one idea to another like a caffeinated squirrel on a sugar rush. I’ll dive into extensive, over-detailed examples, share what I know and what I learned, and walk you through what I do like I’m hosting a show-and-tell session. Think of it as a mix of cybersecurity insights, personal anecdotes, a sprinkle of dark humor, a dash of brainrot from reading thousands of cybersecurity books, and way too much free time combined into one chaotic “thing”. I’ve done the research, but I do not accept responsibility for what happens next. If your brain melts or you start questioning the meaning of life, that’s on you. Proceed at your own risk. Oh, and if you’re expecting a happy ending, remember what Deadpool said: “Life is an endless series of trainwrecks with only brief, commercial-like breaks of happiness.” Consider this your commercial break.

Another Warning: If you’re new to cybersecurity and still hyped about saving the world from hackers, maybe don’t read this article just yet. Think of it like watching a horror movie before bed—it might make you hate the dark. Cybersecurity is an amazing field, but this article is like Deadpool breaking the fourth wall: it’s raw, unfiltered, and might ruin the illusion. Come back when you’ve got a few battle scars and a sense of humor. Until then, keep your optimism intact—you’ll need it. Maybe I can say more but think of it as a reality check with a side of dark humor. After all, if we can’t laugh at the chaos, what’s the point? I like dark humor and daddy jokes, whatever. Just to be clear, I’m not “the guy” with 10 years of experience, nor am I here to lead your career. I’m no one special—just a bored security researcher who reads too much, overthinks, and listens the rumors.

The Problem with YARA Rules: Static Signatures in a Dynamic World

YARA rules are a cornerstone of malware detection. They allow us to create signatures based on specific strings, functions, or artifacts in a malware sample. For example, a YARA rule might look for a unique string like "WorldDestroyer_1" or a specific API call sequence like VirtualAlloc -> WriteProcessMemory -> CreateRemoteThread. These rules are effective for detecting known malware families, but they have a critical flaw: they’re static.

How AI-Driven Malware Renders YARA Rules Useless

AI-driven malware is increasingly leveraging polymorphic techniques to evade detection. For instance, BlackMamba, a polymorphic malware, uses AI to rewrite its code dynamically, generating unique variants with each iteration. This makes traditional YARA rules ineffective, as the malware’s signature changes every time it is compiled or deployed [1][2].

Key techniques used by AI-driven malware include:

• Rewrite Functions: Randomly change variable names, control flow, or logic. For example, a function that encrypts files could be rewritten to use a different algorithm or even a different programming language.
• Generate Unique Artifacts: Create new strings, file names, or registry keys for each build. For instance, instead of using a hardcoded string like "C2_Server", the AI could generate a unique domain like "xyz123.malicious[.]com".
• Obfuscate Code: Introduce junk code, dead functions, or irrelevant logic to confuse static analysis tools.

Think about it: an AI-driven or assisted approach can completely change these steps or alter some of the known techniques. Why? Because AI tries to use all known and related information to generate an output that meets the user’s needs—just like any other software. Feed that AI with legitimate function names, the most trusted system calls, or artifacts that look normal, and it can produce something that flies under the radar.

For example:

• Educated Obfuscation: The AI can generate obfuscation techniques that are sophisticated yet blend in with normal code. It’s not just about randomizing variable names—it’s about making the code look like it belongs.
• Educated Junk Code Filling: The AI can insert junk code that doesn’t affect functionality but confuses static analysis tools. It’s like adding noise to a signal—the malware’s purpose remains intact, but the tools can’t make sense of it.
• Execution Flow Alteration: The AI can change the execution flow of functions, using methods that are known but not well-known. It’s not inventing something new; it’s just using what’s already out there in a way that hasn’t been widely exploited yet.

We know a lot—maybe even many things—about how malware works. But that AI model? It’s been fed with all known methods, plus a ton of other techniques that aren’t even related to malware. It’s like giving a chef every ingredient in the world and asking them to create a dish no one has ever tasted before.

So? Every time the malware is compiled, it produces a completely unique binary. The hash changes, the strings change, and even the functions YARA rules target are altered. The malware still performs the same malicious actions, but it’s now undetectable by existing YARA rules.

Think about it like this: the YARA rule is the guardian at the gate, tasked with watching and identifying malicious passengers. It has a list of suspicious traits—maybe a specific hat, a mustache, or a particular outfit. But here’s the problem: the malware passenger has already changed their outfit, swapped the mustache for a beard, and replaced the hat with sunglasses.

The guardian squints at its list, trying to match the metrics, rules, and patterns it knows. But the passenger? They’re already inside, sipping a latte and plotting their next move. The guardian shrugs and says, “No match!” while the malware casually walks past, waving hello.

Question: Static Unpackers, Deobfuscators, and ML Analysis—Aren’t These Solutions?

You might argue that static analysis isn’t dead yet. Tools like static unpackers, deobfuscators, and machine learning (ML) models can still detect obfuscated malware. Let’s break this down:

1. Byte Code and Opcode Analysis: These techniques analyze the low-level instructions of a program. However, they can be defeated by rewriting functions, changing execution sequences, or even compiling the malware with different settings. For example, ifthe malware author changes the order of instructions or inserts junk code, byte code and opcode analysis become less effective.
2. Machine Learning (ML): ML models rely on inputs extracted from static analysis, such as strings, API calls, and YARA rules. While ML can improve detection accuracy, it’s not immune to obfuscation. If the malware’s code is randomized or rewritten, the model’s inputs become unreliable.
3. Static Unpackers and Deobfuscators: These tools can sometimes reverse obfuscation, but they’re expensive to maintain and often provide diminishing returns. Security vendors rarely invest in them because they’re not cost-effective.

Side note: I’ve developed ML-based detection systems myself, including my senior project at university. The result? A tool with ~0.90 accuracy—which, let’s be clear, isn’t just “good for a student project.” It’s my scream that I hold that for a real long time, by the way I aimed ~0.70 but it was also surprise to me. But here’s the real kicker: I didn’t just use some pre-packaged, cookie-cutter dataset that everyone and their dog has already overfitted to death. Nope. I created the entire dataset myself, using my own static analysis tool and a ~totally unique~ technique (okay, fine, it’s not that unique—it’s actually pretty simple, but compared to other articles and projects, it’s kinda unique, so let’s just roll with it because it sounds cooler). This technique helped the ML model generate better relationships between features, because apparently, I like doing things the hard way.

And let’s talk about the dataset for a second. This wasn’t some toy dataset scraped together from a textbook or a sanitized academic playground where everything is sunshine and rainbows. Nope. It was a living, breathing collection of recent malware samples and real-world executables. And the benign samples? I didn’t just rely on some dusty old files from 2010. I spent days scraping software download websites to collect the most recent, trusted, legitimate executables. After one week I collected 10.000 unique benign samples, before deduplication there was ~20.000 samples, that was a real SSD HDD storage disaster. So, yeah, ~0.90 is impressive. But here’s the twist: even though I built this ML-supported static analysis tool, I’m not sure I’d fully trust it for malware detection. If the creator doesn’t trust it, should you?

In short, static analysis is becoming harder every day. New methodologies are emerging, but they’re often rendered useless by the time they’re published. It feels like researchers are starting to give up on static analysis altogether.

The Rise of RaaS and AI-Driven Customization

Let’s take this a step further with a real-world example. Suppose there’s a ransomware family called WorldDestroyer_1 (because why not?). The group behind it offers a Ransomware-as-a-Service (RaaS) model, allowing buyers to customize the malware through a desktop app. Buyers can:

• Select encryption algorithms (e.g., AES, RSA, or a custom algorithm).
• Choose which files to target (e.g., documents, databases, or backups).
• Set ransom amounts and payment instructions.
• Generate a unique seed for encryption.

So far, this is nothing new. RaaS platforms like LockBit and REvil already offer similar features. But here’s where things get interesting: the malware author integrates a basic AI model into the builder. With a single click, the AI rewrites parts of the malware’s code, generating a completely unique variant every time, if we exaggerate the situation further, it can write a malware that has the features you want and works in a way you specify. I’m just talking about the possibilities, it doesn’t hurt to be a little dreamy. We had things that were called dreams 10-20 years ago, and even things that were unimaginable back then have become normal today.

How AI Enhances RaaS Platforms

AI-driven RaaS platforms are not just a theoretical concept; they are already emerging in the wild. For example, BlackMamba, a polymorphic malware, uses AI to dynamically rewrite its code, ensuring that each variant is unique and harder to detect [1][2]. This approach allows attackers to:

• Automate Code Rewriting: The AI can generate new code variations for each build, ensuring no two samples are identical. This makes it nearly impossible for signature-based detection tools like YARA rules to keep up [3].
• Enhance Evasion Techniques: By analyzing the behavior of security products, the AI can identify and bypass specific detection mechanisms. For instance, it can rewrite functions to avoid triggering sandbox alerts or alter network traffic patterns to evade intrusion detection systems [4].
• Scale Operations: AI lowers the barrier to entry for less skilled attackers. With a few clicks, even a novice can generate sophisticated malware tailored to their needs, democratizing cybercrime and increasing the volume of attacks [5].

Real-World Implications

The integration of AI into RaaS platforms represents a significant shift in the malware landscape. For example, Prodaft’s research highlights how cybercriminals are already using AI to automate the creation of phishing campaigns, malware, and even deepfake-based social engineering attacks [6]. Similarly, Trend Micro has documented cases where ransomware authors abuse legitimate APIs and AI tools to enhance their malware’s capabilities, such as encrypting files more efficiently or evading detection [7].

A recent report by Check Point Research highlights the emergence of FunkSec, an alleged ransomware group that leverages AI to power its operations. FunkSec’s malware uses AI to optimize encryption routines, evade detection, and even negotiate ransom payments with victims. This level of sophistication demonstrates how AI is becoming a game-changer in the ransomware ecosystem [8].

The Ransomware Revolution

According to a study published in the World Journal of Advanced Engineering Technology and Science (WJAETS), AI is driving a ransomware revolution by enabling attackers to automate and scale their operations. The study highlights how AI-powered ransomware can adapt to different environments, making it more effective and harder to detect. For example, AI can analyze the victim’s system and customize the attack to maximize damage while minimizing the risk of detection [9].

However, it’s worth noting that this study was published in 2021, and its references are based on even older research. Fast forward to 2025, and the capabilities of AI have far surpassed what was anticipated just a few years ago. Today, AI is not only more powerful but also more unpredictable, even to its own developers. The rapid advancements in generative AI, large language models (LLMs), and adversarial machine learning have created a landscape where AI-driven malware can outpace traditional defenses in ways that were unimaginable just a few years ago. The study, while insightful, only scratches the surface of what AI is capable of today. The reality is that AI is now a force multiplier for cybercriminals, enabling them to innovate at a pace that often leaves defenders scrambling to catch up.

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Why We Don’t See These Features Everywhere (Yet)

You might wonder: if AI-driven malware is so powerful, why aren’t we seeing it everywhere? The answer lies in the economics of malware development. Right now, malware authors make more money by selling new malware with new approaches rather than investing in AI-driven innovation. For example, a group might sell a new ransomware variant with a unique encryption method or a novel persistence mechanism. This approach keeps their customers (other attackers) interested and willing to pay.

However, as security systems like antivirus software, EDR systems, and sandboxes continue to evolve, malware authors will be forced to innovate. When YARA rules and sandboxes become too effective, AI-driven techniques will become the norm. It’s not a question of if but when.

Sandboxes: The Predictable Guardians

(Or, “Why I Know Where the Bodies Are Buried”)

Sandboxes are a cornerstone of malware analysis. They run malware in a controlled environment, monitoring its behavior to identify malicious activity. But sandboxes have a critical weakness: they rely on predictable execution patterns.

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Now, before anyone questions my understanding of sandboxes, malware analysis solutions, or these kinds of security products, let’s just say this: I’ve spent enough time on the other side of the fence—building tools that analyze, dissect, monitor malicious behavior and do some cool stuff. I’ve seen the concept of how these systems work from the inside out. I’ve read their code, studied their design, and tried to figure out how to do better. I’ve explored various open-source projects, reverse-engineered their logic, and over time, I started to understand not just how they work, but why they work the way they do. Maybe, I have a little bit experience about this stuff, a little bit. I began to see the thought process behind the code—why the developer made certain choices, why certain trade-offs were made.

Lately, I’ve been researching security products one by one, trying to understand their role in the ecosystem. And let me tell you, it’s been a wild ride. I’ve seen exploits, bypasses, and—how can I put this politely—really messed up situations. And after all this, I’ve developed a strange empathy for the developers who made these mistakes. I can almost picture what happened:

Staging: “This works, right? Ship it!”

Production: “Oh no, it’s broken. Quick, patch it!”

Management: “We can fix the bugs in the mainstream. Just sell it!”

Customers: “This product is garbage!”

Management: “Shame on you, developer!”

Developer: Hates life, fixes bugs half-heartedly.

Cycle repeats: New bugs, new patches, new blame. And sure, sometimes it’s the developer’s fault. Maybe they’re using 10-year-old code without maintaining it (project leader’s fault), or maybe they’re just having a bad day. But let’s not forget the intern who saved a company’s product with shitposting (see this gem). Sometimes, the heroes wear hoodies and no cap fr fr.

And here’s the thing: once you understand how these systems are built, you also start to see where they can be outsmarted. When I point out their limitations, it’s not because I don’t understand them—it’s because I know exactly where the cracks are. By the way, I’m not the application security guy. I’m just a guy with good research skills, some experience, a good amount of reverse engineering and malware knowledge, and a defensive mindset that’s so hyper-focused it blows my own mind (approximately 7 days a week, 14-15 hours a day). Generic security researcher with twisted mind.

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For example, consider a Linux malware that uses wget or curl to download its second-stage payload. A sandbox might flag any execution of these commands as suspicious, especially if they’re followed by unusual parameters or URLs. But what if the AI rewrites the malware to use a different method for downloading files?

Let’s be clear: talking about AI as if it’s some kind of superior being irritates me as much as it probably irritates you. But here’s the thing—AI is just a tool. It’s not magic. It’s not sentient. It’s a means to an end, and in this case, it’s a means for malware authors to stay ahead of defenders. And yes, I’m using the term “AI” because, like it or not, it grabs attention. Whether people understand it or not, they’ll stop and read when they see “AI.”

Example: AI-Assisted Evasion Techniques

Instead of using wget or curl, the malware author could use AI to generate alternative methods for downloading files. Even chatting with some public AI chatbot, that chatbot will give you more accurate and maybe clever ideas to get around obstacles and get to your goal, rest of the story depends on which pill you choose. Here’s how:

1. Use a Custom HTTP Client

AI can help malware authors write custom HTTP clients in languages like Python or Go, which download payloads in chunks using non-standard headers or protocols. This makes the traffic look less suspicious and harder to detect. For example:

• Dynamic Protocol Selection: AI can analyze the target environment and choose the most effective protocol (e.g., HTTP/3, WebSockets) to blend in with normal traffic.
• Header Manipulation: AI can generate unique HTTP headers that mimic legitimate software updates or API requests, making it harder for intrusion detection systems (IDS) to flag the activity as malicious.

2. Leverage Legitimate Tools

Why reinvent the wheel when you can abuse tools that are already trusted by the system? AI can identify and exploit built-in utilities like certutil (on Windows) or openssl (on Linux) to download files in a way that looks benign. For instance:

• Living-off-the-Land Binaries (LOLBins): AI can automate the process of identifying and leveraging LOLBins, which are legitimate system tools often used by attackers to evade detection. For example, certutil can be used to download payloads while appearing to perform routine certificate management tasks.

I’m not attributing superhuman—superhuman is not the best word for this but you got it, capabilities to AI here—just highlighting how it can assist in developing targeted techniques that exploit trusted tools. By analyzing system behavior and security product responses, AI can suggest the most effective LOLBins to use in a given environment.

• Scripting with PowerShell: AI can generate PowerShell scripts that use trusted system commands to download and execute payloads, bypassing security tools that focus on blocking known malicious scripts.

Now, you might be thinking, “Are you serious?” But hear me out: if you’re developing malware, you’re essentially creating software designed to exploit others. You have access to the same tools and security products used by companies worldwide. You can easily test your PowerShell scripts against these products to see what gets blocked and what slips through. Running Powershell script is the fastest and most direct way to do whatever you want.

Now, imagine integrating an AI model into this process. You train it to understand what triggers alerts, what gets blocked, and what bypasses detection. The AI can then generate dozens of variations, methods, and even creative ideas to overcome these defenses. It’s not magic—it’s just leveraging AI to automate and optimize what attackers already do manually. Counter-AI to polished EDR/AV solutions, defenders use AI and why attackers can not?

3. Embed Payloads in Benign Traffic

AI can disguise payloads as harmless files or blend them into normal network traffic. For example:

• Fileless Malware: AI can generate scripts or commands that execute entirely in memory, leaving no trace on disk. This technique is particularly effective against traditional antivirus solutions that rely on file scanning.

Now, you might think this sounds too advanced, but consider this: AI isn’t just trained on malware code—it’s also trained on real-world legitimate applications. It understands how safe, trusted software allocates memory and executes commands. By leveraging this knowledge, AI can suggest ways to execute malicious code in memory without triggering alarms. Think of it as having an assistant fed by all possible resources, methods, ideas, codes, and techniques. It knows how security systems detect threats and how researchers find ways to bypass them. It speaks an whispers into your ears; sometimes it can directly give the code that yo cannot write.

• Traffic Obfuscation: AI can analyze network traffic patterns and blend malicious activity into normal traffic. For example, it can split a payload into multiple small chunks and send them over time, mimicking the behavior of a legitimate software update (let the AI watch your victim’s behaviour and decide the way how it goes).

The AI Twist: Endless Variations

These techniques aren’t groundbreaking—they’ve been around for years. But here’s the twist: AI makes it easier to generate endless variations of these methods. By automating the process of code rewriting, protocol selection, and traffic obfuscation, AI ensures that each malware sample is unique. This makes it nearly impossible for signature-based detection tools to keep up. For example:

• Polymorphic Malware: AI can generate new variants of malware with each build, changing everything from function names to encryption keys. This ensures that no two samples are identical, rendering static analysis tools like YARA rules ineffective. [10]

• Adaptive Evasion: AI can analyze the behavior of security products and adapt the malware’s tactics in real-time. For instance, if a sandbox is detected, the malware can delay execution or alter its behavior to avoid triggering alerts. But let’s think even more advanced: imagine a threat actor utilizing an AI-powered system, or even a chatbot-like interface, integrated into their Command and Control (C2) infrastructure. This C2 system doesn’t just send commands and files—it collects detailed telemetry from the malware, including how and where it was detected.

Here’s how it works: the malware sends back information about its execution environment, such as system configurations, running processes, and network activity. If the malware is blocked or detected, the C2 system logs the exact point of failure. Over time, this data builds a comprehensive picture of what works and what doesn’t. The AI system can then analyze this information to identify patterns—like which environments are likely sandboxes or which detection mechanisms are most effective.

For example, if multiple malware instances fail on systems with specific security products installed, the AI can infer that those products are likely blocking the attack. It can then suggest modifications to the malware’s behavior, such as changing its execution flow, using different obfuscation techniques, or even targeting different system vulnerabilities. In essence, the AI acts as a real-time advisor, helping the threat actor refine their techniques and stay ahead of defenders.

But here’s the kicker: while defenders have EDRs, AVs, sandboxes, SIEMs, and other tools enhanced with AI to detect malicious behavior, do you really think we’re the smartest ones in this game? Threat actors aren’t standing still. They’re innovating just as fast, if not faster. This is a war where tons of money are poured into both sides, each convinced they’re smarter than the other. Defenders think, “We’ve built the most advanced technology—our guns can shoot farther, our shields are impenetrable.” But when the day of the war comes, the opponent shows up with laser guns or gas that melts everything in a specific area.

This isn’t a regulated battlefield. There are no war crimes, no rules, no limits—only the constraints of current resources. Whether you’re on the good side or the bad side, when you develop a new capability and it becomes public, the other side will eventually have it too. It’s a never-ending cycle of escalation, where today’s cutting-edge defense becomes tomorrow’s exploited vulnerability.

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The Bitter-Sweet Reality of AI as a Tool

Here’s a bitter-sweet fun fact: even today, any script kiddie can develop malware-ish tools using public AI chatbots. And guess what? Some of these amateur creations aren’t detected by major antivirus software—yes, the ones that claim to protect the world. This isn’t because AI is inherently superior or magical; it’s because AI lowers the barrier to entry. It’s a tool, and like any tool, it can be used for good or bad. In the hands of a skilled threat actor, it’s a force multiplier. In the hands of a script kiddie, it’s a shortcut to chaos.

Speaking of chaos, let’s take a trip down memory lane to 2009—back when AI was just a sci-fi dream, blogs teaching malware coding were as rare as unicorns, and creativity was the only tool in a hacker’s toolbox. Enter “musallat.exe” . You’ve probably heard of WannaCry, but “musallat.exe” was a whole different kind of beast. This USB-spreading worm didn’t need fancy AI or tutorials—it was pure, unfiltered mischief, like a digital gremlin wreaking havoc in the wild west of cybersecurity. Ah, the good old days—no AI-powered chatbots, no GitHub repos full of malware code, just raw, chaotic energy.

Now fast forward to today. Script kiddies? Check. AI chatbots? Check. A wide-open internet filled with malware development blogs, bypass guides, and step-by-step tutorials on how to pwn security products? Double check. It’s like handing a toddler a flamethrower, a grenade launcher, and a fully-loaded AR-15, complete with an illustrated “How to Cause Maximum Chaos” guide. And then hoping they don’t burn the house down. Spoiler alert: they will. Fun times, right?

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But let’s be clear: I’m not talking about malware fully generated or modified by AI. That’s still science fiction for the most part, even the most generative AI model can not create a software at once, it won’t work probably if it does. What I’m talking about is the use of AI as a tool to enhance existing techniques, automate tedious tasks, and create variations that evade detection. It’s not about replacing human creativity—it’s about augmenting it.

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The Limitations of Sandbox Analysis

Sandboxes are powerful, but they’re not infallible. They rely on known patterns of malicious behavior, which makes them vulnerable to AI-driven obfuscation and evasion techniques. Here’s why:

1. Behavioral Analysis Blind Spots: Sandboxes look for specific sequences of actions, like “download file → execute file” or “create registry key → persist in system.” But if the malware changes its execution flow—say, by splitting the download into multiple stages or using a less common persistence mechanism—the sandbox may miss it. It’s like a bouncer at a club who only knows to look for people wearing red hats. If the troublemaker swaps their hat for a scarf, they’re in.
2. Resource Constraints: Sandboxes can’t run malware indefinitely. If the malware uses time-based triggers (e.g., “wait 30 minutes before executing”) or waits for user interaction (e.g., “only run after the user clicks this button”), it can easily evade detection. It’s like a thief who waits for the security guard to take a coffee break before making their move.
3. Evasion Techniques Are Public Knowledge: Sandbox evasion techniques are well-documented, and malware authors are constantly innovating. For example, sandboxes often manipulate time settings to speed up sleep timers, but malware authors know this and use more sophisticated methods to bypass it. It’s a never-ending game of cat and mouse, and the mouse is always one step ahead.

Check this out, it is really fun when you know something, at least for me, oh boy… Let’s take this a step further.

Sandboxes typically run a virtual operating system in the background, consuming resources like RAM and CPU. Some sandboxes have limited resources—imagine a sandbox with 1.5-2 CPUs allocated per session on a 16-core machine.

Now, picture this: malware detects it’s in a sandbox (they are also smart) and decides to throw a tantrum. It starts hogging all the resources, trying to crash the system. For one sample, this isn’t a big deal—you just reboot the machine and move on with your life. But what if the same malicious file keeps getting dropped into a company’s network, triggering sandbox analysis over and over? Suddenly, you’ve got 8 sessions running simultaneously, each trying to analyze the file, and the malware is doing its best to exhaust the system. Chaos ensues. The sandbox starts struggling, the CPUs are maxed out, and the IT guy goes “huh?!”. It’s like malware throwing a rave in your sandbox, and you’re the one stuck cleaning up the mess.

Fyi, this is configuration problem, if you think you or your company may have this problem, let the IT guys or system engineers check it. Maybe you do not have this problem but your product, just a maybe, have this problem, it is good to test the limits of the product you will pay tons of money, run stress tester in all sessions of sandbox, maybe they can not launch 4. session at all then the pipeline goes deadlock, then let the chaos rules the company network. It is fun for the teller, but if you experience this, wow, it is gonna be the hell on earth. By the way this part is not related to AI but I wanted to mention this.

Question: Can’t Heuristic Analysis Fix These Blind Spots?

Heuristic analysis is often touted as the solution to sandbox evasion. But let’s be honest: heuristic and holistic are just fancy words. At the end of the day, heuristic analysis is still deterministic code written by humans. It’s not magic.

Here’s the plot twist: if the software runs deterministically, how can it act “heuristic” or “holistic”? The answer is simple: it can’t . What’s really happening is that vendors are using AI or ML models behind the scenes. They feed these models with massive amounts of data, and the models find patterns that humans can’t. But let’s not kid ourselves—this isn’t magic. It’s just statistics.

Think about it: a security product vendor develops and trains the “best” AI solution for your needs. Sure, it might be the best solution for the attacks it was trained on, but what about new threats that emerge weeks or months later? The system becomes outdated faster than you can say “zero-day.” And let’s not even get started on the costs—storage, RAM, GPU power, and, most importantly, time. Training these systems with new data isn’t just expensive; it’s a never-ending cycle.

There will be always more solutions for most common problems like continuous learning problem like in here, but let’s ignore it. What if the new data makes the model perform worse? Now you’re back to square one, tweaking the technique and starting over. This is the real cost of research and development—endless hours, piles of cash, and the constant fear that your shiny AI solution might turn into a glorified paperweight. By the way, if money flow continues (like subscription), this situation will not be a problem because if there is enough money, they will figure it out; money is honey.

And here’s the kicker: vendors often overhype these capabilities to justify higher prices. They’ll say things like, “Our EDR product works in a way that even we can’t understand!” But in reality, it’s just a well-trained ML model. Don’t fall for the marketing hype.

Let’s Investigate a Case: The Never-Ending Game of Evasion Techniques

Sandboxes have come a long way in combating evasion techniques, including sleep evasion, USB/PCI device emulation, CPU information manipulation, but let’s be real—malware authors are like that one friend who always finds a way to cheat at board games. No matter how many rules you add, they’ll figure out a loophole. For example, sandboxes now manipulate time configurations to speed up sleep timers. It’s like setting the clock forward to trick your lazy teenager into getting out of bed—except the “teenager” is malware, and it’s not fooled for long. In fact, it’s probably laughing at you while sipping a virtual latte.

The problem is, everyone knows how these tricks work. Malware authors, security researchers, and even cybersecurity newbies who are just dipping their toes into malware analysis are aware of how sandboxes handle evasion. Sandbox developers spend weeks, maybe months, fine-tuning virtualization code to overwrite sleep behavior, only for malware authors to shrug and say, “Cool story, bro,” before switching to a new evasion tactic. It’s like spending months building a master key for a specific lock, only for the thief to replace the lock with a biometric scanner.

Why Focus on Evasion Techniques? Because It’s a Mess

Evasion techniques like sleep timers, USB/PCI device emulation, CPU cycle manipulation, and power status simulation are fascinating because they highlight just how ridiculously complex virtualization software is. These techniques exploit subtle differences between real hardware and emulated environments, forcing sandbox developers to make incredibly precise modifications to their systems. However, these changes are not trivial—they require a deep understanding of the underlying architecture and can introduce unintended side effects.

For example:

• USB/PCI Device Emulation : To make a sandbox more realistic, developers might emulate specific hardware devices, like branded mice or keyboards. Because, you know, malware really cares if you’re using a Logitech or a generic mouse—but not the mouse with “QEMU” on the label. This requires deep integration with the virtualization software to ensure the emulated devices behave like their real-world counterparts. Spoiler: They rarely do. It’s like trying to convince a picky toddler that store-brand mac and cheese is just as good as the name-brand stuff. Good luck.
• CPU Cycle Manipulation : Malware often checks CPU cycles or timing to detect virtualized environments. Sandboxes must carefully manage CPU emulation to avoid tipping off the malware, which involves tweaking low-level virtualization code. It’s like trying to convince a paranoid conspiracy theorist that the government isn’t watching them—good luck with that. And no, the malware isn’t buying your “trust me, bro” explanation about CPU cycles.
• Power Status Simulation : Some malware checks whether a system is running on battery or plugged in. Simulating these states requires modifying the emulation software to accurately reflect power status, adding another layer of complexity. Because apparently, malware has strong opinions about your laptop’s charging habits. At least malware will check your battery information, metrics, etc. Maybe it’s just trying to help you optimize your battery life. How thoughtful.

The Difficulty of Changing Complex Systems

These changes are made at the emulation software level, where every modification requires a deep understanding of the underlying architecture. For instance, QEMU’s codebase is a sprawling maze of interdependent modules, and even small changes can introduce unexpected bugs or performance issues. It’s like trying to fix a leaky faucet in a house made entirely of Jenga blocks. Similarly, Xen’s hypervisor-level modifications require careful consideration of how they interact with guest operating systems and hardware emulation. One wrong move, and your sandbox might start emulating a potato instead of a PC.

Here’s why making changes is so challenging:

• Interdependencies: Virtualization software is built on layers of abstraction, each of which interacts with the others in complex ways. Changing one part of the system can inadvertently affect other parts, leading to unexpected bugs. For example, modifying CPU emulation might impact memory management or device emulation, requiring developers to test and debug across multiple subsystems. It’s like playing whack-a-mole, except the moles are bugs, and they’re all interconnected.
• Debugging Complexity: Debugging virtualization software is a nightmare wrapped in a riddle, dipped in a vat of caffeine. Issues can pop up at every level—guest OS, hypervisor, hardware emulation—and tracing the root cause feels like trying to find a needle in a stack of needles. Developers spend what feels like lifetimes analyzing logs, stepping through code, and running tests, all while muttering, “Why won’t you just work?” under their breath. It’s a process that demands the patience of a saint, the persistence of a caffeinated squirrel, and a deep understanding of the system that borders on obsessive.

And let’s talk about the fuel that keeps this madness going: coffee. Oh, the coffee. Days start with a 4-shot espresso, followed by a Red Bull Peach Edition, and then—just to make sure you’re still alive—another 4-shot espresso. Or maybe some other cold drink, depending on how much work needs to be done and how much encouragement is being generously provided by a certain someone in a leadership role. Because let’s be honest, who willingly signs up for this level of self-inflicted chaos? It’s not exactly a spa day, but hey, sometimes the universe decides you haven’t suffered enough. And by “sometimes,” we mean “all the time minus a few times.” It’s not a great work dynamic, but it’s the one we’ve got.

So, grab your caffeine, your sugar, and maybe a stress ball, because big sister/brother is watching, and they’ve got opinions about your productivity, their dinner plans, and their secret recipe for turning you into the perfect after-dinner dessert. What’s wrong with a little power play here and there, right? It’s not like anyone’s keeping score… HR, hello!? Someone, please…

By the way, I’m not talking about every cybersecurity company—actually, it’s not even just about cybersecurity. This is a universal experience across most tech fields, and let’s be real, even other industries. So, here’s a thought for the folks in charge: why not make work a little less The Office and a little more Parks and Recreation?

How about this: instead of treating your team like cogs in a machine, treat them like, well, people. Give them time to breathe, to think, and to not feel like they’re constantly under surveillance. Let them have fun—yes, fun!—because happy workers are productive workers. And no, a ping-pong table in the break room doesn’t count if they’re too stressed to use it.

Oh, and here’s a revolutionary idea: pay them fairly. Like, actually fairly. Not “just enough to survive” but “enough to thrive.” Because guess what? If you treat your team well, they’ll stick around, work harder, and maybe even help you make more money. Imagine that: earning money with your team instead of off them. What a concept!

So, to all the big sisters and big brothers out there: stop being the villain in your team’s story. Be the hero. Or at least the mildly tolerable side character. Your team—and your bottom line—will thank you.

Yeah, we kinda drifted into workplace philosophy, caffeine-fueled suffering, and corporate shenanigans. But let’s snap back to our topic. (I said twisted mind, literally.)

• Performance Considerations: Every change to virtualization software must be carefully evaluated for its impact on performance. Emulating additional hardware or modifying CPU behavior can introduce overhead, slowing down the entire system. Developers must strike a delicate balance between functionality and performance, often resorting to clever optimizations to minimize the impact. It’s like trying to run a marathon while carrying a backpack full of bricks—except the bricks are your code, and the marathon is your sanity.
• Open-Source Challenges: We all love the open-source community—truly, we do—but let’s be real: open source is the double-edged sword of cybersecurity. Tools like QEMU and Xen are open-source, which means attackers get a front-row seat to your codebase. It’s like handing your opponent the blueprint to your fortress and saying, “Good luck storming the castle!” Developers not only have to fix vulnerabilities but also predict how attackers might weaponize their changes. That shiny new feature you just implemented? Yeah, it’s not so shiny anymore when the malware author reverse-engineers it over their morning coffee. Open-source is great for collaboration, but it’s also great for giving malware authors a cheat sheet.

Then again, if I didn’t use these tools so much, maybe I wouldn’t care. But where’s the fun in that? If someone kept all the secrets to themselves, what would we even talk about? The cybersecurity world would be a lot less interesting without open source. More open-source malware techniques? Bring it on. More bypass methods? Sure, why not. Unsolved vulnerabilities? Absolutely. Let’s make it chaotic, let’s make it messy, and let’s make it fun. After all, what’s life without a little chaos to keep us on our toes? I love open source, and I love the chaos it brings. Let’s embrace it—code, vulnerabilities, and all.

Maybe someday we’ll figure out what we’re doing wrong. Cybersecurity is still a relatively new field, and let’s be honest, there’s definitely something wrong with how we’re approaching it. But will we figure it out before the world collapses around us? Probably not. And honestly, that’s part of the charm. So, let’s keep tinkering, keep breaking things, and keep laughing as the world burns. What else can we do?

This complexity is why evasion techniques are such a headache. It’s like playing chess against someone who’s read all your strategy books—except they’re also cheating. I liken malware writers to raccoons in this regard; no matter how many locks you put on your trash cans or how high you build your fences, they always find a way in. They’re clever, persistent, and have an uncanny ability to sniff out your secrets, no matter how well you think you’ve hidden them. Just when you think you’ve outsmarted them, you wake up to find your garbage strewn across the yard and a smug raccoon sitting there, silently mocking you.

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The Hit-and-Run Cycle of Malicious Activities

This dynamic isn’t unique to evasion techniques—it’s a pattern that repeats across the cybersecurity landscape. Whether it’s sandboxes, EDR, EPP, or the latest “next-gen AI-powered magical unicorn solution,” the story is always the same. This hit-and-run cycle applies to most of the features in malicious activities—whether it’s malware development, phishing campaigns, or advanced persistent threats (APTs). Here’s how it works:

1. Sandbox/EDR/EPP/Some Other Security Products Are Public (or Accessible)

Many security products are publicly available, or threat actors can simply buy them. For example:

• A malware author who’s stolen thousands of dollars (or made it through their RaaS business) can easily purchase a commercial sandbox package.
• A phishing group that successfully scammed a company can use the profits to buy the same EDR product their target uses.
• An APT group with state-sponsored funding can acquire any security product they want to test their tools.

Why? Because they can. They’ll test their malware, phishing kits, or attack methods in that “cutting-edge, feature-packed, AI-driven, next-gen security product” without anyone noticing. And let’s not forget the good-intentioned researchers who poke these products with a stick just for fun. They post their findings on social media, saying, “Look, I bypassed this EDR/sandbox!” It’s all in the name of science, right? But while researchers do it for curiosity or clout, threat actors do it for profit, power, or political gain. And guess who’s more motivated?

2. Test, Tweak, Repeat

The threat actor runs their creation through the EDR/sandbox/EPP/whatever, observes how it’s detected, and tweaks their approach to evade detection. They repeat this process until their attack is completely undetectable.

Here’s the kicker: security products are predictable. They rely on known patterns, signatures, and behaviors. If the attack changes its approach—even slightly—the security product is left scratching its head. For example:

• EDR systems might flag a process that injects code into another process as suspicious. But if the attacker uses a legitimate process (like svchost.exe) or a novel injection technique, the EDR won’t catch it.
• EPP systems might block a file if it matches a known malware signature. But if the payload is packed, obfuscated, or fileless, the EPP is useless.
• Email security tools might flag phishing emails with suspicious links. But if the attacker uses a legitimate service (like Google Docs) to host their phishing page, the email slips through.

It’s like playing hide-and-seek, but the seeker is blindfolded and the hider has a cloaking device.

3. Spread and Profit

Once the attack is ready, it’s time to launch it. The threat actor infects systems, steals data, or compromises networks. Meanwhile, security companies are completely unaware of the new threat.

And here’s the funny part: security vendors don’t even know they’ve been bypassed until it’s too late. By the time they catch on, the threat actor has already cashed out, exfiltrated the data, or moved on to the next target. It’s like a bank robber who leaves a thank-you note for the security guard after emptying the vault.

4. Wait for the Reaction

The threat actor sits back and waits for security companies to react. When the defenders finally catch on and release updates or new detection methods, the threat actor analyzes what they did and starts the cycle all over again.

And what do the vendors do? They roll out a new “cutting-edge feature” (read: a slightly tweaked version of the old one) and charge 2x the price for it. Meanwhile, the threat actor is already working on their next masterpiece.

It’s a Hit-and-Run-and-Repeat Cycle

This cycle is incredibly effective. The threat actor is always one step ahead, while the defenders are stuck playing catch-up. And let’s be honest: if you were a threat actor, wouldn’t you do the same? It’s like playing a video game where you have unlimited respawns, and your opponent has to start from scratch every time.

Security vendors love to talk about their “proactive defense” and “real-time protection,” but let’s call it what it is: reactive chaos. They’re not stopping threats—they’re just cleaning up the mess after the fact. And by the time they’ve mopped up one mess, the attackers are already making another.

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The Illusion of EDR and EPP: Next-Gen Buzzwords, Same Old Problems

Alright, let’s dive into the world of Endpoint Detection and Response (EDR) and Endpoint Protection Platforms (EPP). These tools are the tech industry’s equivalent of that friend who promises to fix your Wi-Fi, clean your gutters, and teach you how to meditate—all in one afternoon. Spoiler alert: they usually just end up eating all your snacks and leaving you with a half-finished PowerPoint presentation about “synergy.”

EDR and EPP are marketed as the superheroes of cybersecurity, actually EPP becomes a part of EDR lately, complete with shiny sprinkles of AI, machine learning, and real-time threat detection. They’re supposed to be the Gandalf of your endpoints, standing on the bridge and shouting, “You shall not pass!” to every malicious actor out there. But let’s be real: sometimes Gandalf takes a coffee break, and the Balrog just waltzes right in.

Now, before I go full-on skeptic mode, let me just say this: if you’re a developer or seller of EDR solutions, please don’t ban me. I’m just a humble security researcher trying have fun while writing this article and let the readers understand what I read and researched in funny way, maybe not funny but funny. I’m not here to burn bridges—just to gently roast them over a low flame.

Here’s the thing: I have a natural aversion to anything that’s hyped up like it’s the second coming of sliced bread. If everyone’s raving about a movie, I’ll wait until it’s on a streaming service no one subscribes to. If everyone’s obsessing over a trendy food, I’ll pay extra to avoid it. And if everyone’s polishing a product like it’s the Holy Grail of cybersecurity, I’m going to poke it with a stick and see if it’s actually made of plastic.

So, let’s talk about EDR and EPP. Are they useful? Absolutely. But are they the magical, all-in-one solution that will solve every cybersecurity problem ever? Well, not exactly.

The truth is, while EDR and EPP have their strengths, they’re not immune to flaws. False positives, resource hogging, and the occasional “oops, we missed that massive breach” moment are all part of the package. It’s like buying a self-driving car that occasionally forgets to stop at red lights. Sure, it’s cool, but you’re still going to keep your hands near the wheel.

Why I used reality check in the rest of this part? Because like every area, every shiny industry, here he are talking about cybersecurity, is full of shiny promises, and it’s easy to get lost in the hype. The Reality Check is here to ground us, reminding you that no tool is perfect, and every solution has its trade-offs. It’s the voice that says, “Hey, let’s not forget the basics while chasing the next big thing.”

The Problem with EDR

Ah, stepping into the world of EDR is like walking into a minefield wearing clown shoes—you know it’s risky, but hey, at least you’ll go out with a bang and maybe a few laughs. EDR is the golden child of cybersecurity right now, the Beyoncé of endpoint protection, and its marketing game is stronger than a double-shot espresso. (Though, let’s be honest, a double-shot espresso is just a warm-up for me. I’m over here sipping on a black-eye with extra shots after a RedBull. Maybe that’s why I’m not easily impressed by all the hype.) But let’s be real: just because something’s shiny doesn’t mean it’s bulletproof.

I’ve done my homework—studied EDR evasion techniques, read Matt Hand’s Evading EDR (because why not dive deeper into how the bad guys outsmart the good guys?, by the way I am still reading it, liked it), and spent enough time in the trenches to know that even the best tools have their blind spots. So, grab your popcorn and maybe a helmet, because today we’re running through that minefield. Spoiler alert: it’s going to be a bumpy ride. I may lose some of my limbs, but everything has a price. “I’ll be back,” said the Terminator, and so will I—hopefully with all my limbs intact.

Oh boy, there we go.

1. EDRs Are Reactive, Not Proactive

EDRs are the cybersecurity equivalent of a fire department—they arrive after the blaze has already started. By design, they rely on behavioral analysis and known malicious activity patterns, making them fundamentally ill-equipped to combat novel threats. Sophisticated attackers exploit this delay, crafting attacks that bypass traditional detection mechanisms entirely.

• The Fileless Malware Conundrum: Fileless malware operates entirely in memory, leaving no disk-based indicators for EDR to latch onto. For instance, attackers using Reflective DLL Injection bypass EDR by injecting code into a legitimate process, leaving minimal traces in the execution flow.
• Living-Off-the-Land (LOL) Techniques: Legitimate tools like PowerShell, WMI, or PsExec are double-edged swords. EDR struggles to differentiate between normal administrative use and malicious exploitation of these tools. As the Cyberoo article highlights, EDRs often fail to detect advanced threats that use these tools to carry out attacks.

Reality Check: By the time EDR flags an attack—if it does—the breach may have already succeeded in compromising sensitive data or infrastructure. This reactive nature leaves organizations perpetually one step behind attackers.

2. EDRs Are Resource-Intensive

The trade-off between robust detection and performance is a thorn in the side of EDRs. Endpoint devices often bear the brunt of this burden, resulting in diminished productivity and operational inefficiencies.

• Operational Impact: Real-world deployments see systems slowed to a crawl during EDR scanning, consuming 20-30% of CPU. This performance hit leads many organizations to disable resource-heavy features like heuristic scanning, opening the floodgates for sophisticated threats.
• Hidden Costs: Beyond hardware, the human capital needed to manage and tune these systems often exceeds initial estimates. As the CMIT Solutions article points out, many organizations struggle with the operational overhead of EDR systems, leading to gaps in coverage and missed threats.

Reality Check: The inherent inefficiency of EDR systems forces organizations to compromise between security and functionality, often leaving critical gaps that attackers can exploit.

3. False Positives and Alert Fatigue

EDR systems flood security teams with an overwhelming number of alerts—many of which are false positives. This deluge creates an environment of alert fatigue, where critical threats might slip through unnoticed.

• Example: A benign PowerShell script might trigger multiple alerts due to specific commands resembling malicious behavior. Over time, analysts become desensitized, glossing over genuine threats amidst the noise.
• Impact: According to the Securus360 article, the constant barrage of false positives can overwhelm security teams, reducing their effectiveness and increasing the risk of missing real threats.

Reality Check: False positives aren’t just an annoyance—they are a structural flaw that reduces EDR effectiveness, rendering even sophisticated setups vulnerable to attack.

4. EDRs Struggle Against Advanced Threats

The arms race between attackers and defenders tilts in favor of attackers who leverage techniques such as:

• Fileless Malware: EDRs focus on disk-based indicators but falter against in-memory threats.
• Stealth Tactics: Techniques like API hooking or direct syscalls bypass EDR instrumentation, reducing visibility.
• Polymorphic Malware: Constantly changing its code to evade signature-based detection, polymorphic malware can slip past EDR systems undetected.

Example: A PowerShell command to download and execute a payload might evade EDR entirely, especially if obfuscation techniques are used. As the Palo Alto Networks article notes, attackers increasingly use legitimate tools to blend into enterprise environments, exploiting EDR blind spots with precision.

Reality Check: Attackers wield tools designed to blend seamlessly into enterprise environments, exploiting EDR blind spots with precision. This makes EDRs a reactive tool rather than a preventive one.

5. Deployment and Management Challenges

Implementing EDR across hybrid infrastructures is no small feat. Misconfigurations and incomplete endpoint coverage further erode the efficacy of these systems.

• Case Study: A global organization deploying EDR may neglect remote endpoints or cloud instances, creating unmonitored entry points for attackers. As the Sangfor article highlights, misconfigurations and lack of integration with other security tools can leave gaps wide enough for attackers to exploit.
• Integration Woes: EDR systems often operate in silos, failing to communicate effectively with broader security frameworks like SIEMs or XDR platforms.

Reality Check: EDR is only as strong as its weakest link—deployment challenges often render even well-funded implementations inadequate.

6. The Cost of EDRs

The promise of EDR comes with a steep price tag—both in upfront costs and ongoing maintenance.

• Financial Burden: A mid-sized enterprise could spend upwards of $300,000 annually on EDR licenses, support contracts, and infrastructure upgrades. As the CMIT Solutions article notes, many organizations struggle with the operational overhead of these systems, leading to gaps in coverage and missed threats.
• Human Capital: Expertise in tuning and managing these systems is rare and expensive. Many organizations are forced to rely on third-party providers to bridge the skill gap, inflating costs.

Reality Check: For smaller organizations, EDR costs are prohibitive, leaving them stuck with inadequate or outdated solutions.

7. MDR: A Band-Aid, Not a Cure

The rise of Managed Detection and Response (MDR) services underscores the challenges of managing EDR systems. While MDR providers offer 24/7 monitoring, they inherit the limitations of the underlying EDR technology.

• Dependency on EDR: If the EDR fails to detect a threat, MDR teams have no visibility or data to act upon.
• Cost Multiplication: MDR services add another layer of expense, pricing many organizations out of comprehensive protection.

Reality Check: MDR is a necessary crutch for EDR shortcomings, not a panacea. It’s a stopgap measure that doesn’t address the fundamental limitations of EDR technology.

8. The AI and Machine Learning Myth

EDR vendors market AI and machine learning as revolutionary, but these technologies are far from foolproof.

• Training Limitations: AI models are only as good as the data they’re trained on. Rapidly evolving threats like polymorphic malware outpace model updates, rendering them ineffective.
• Overreliance: Organizations often overestimate the capabilities of AI/ML, neglecting foundational security practices. As the Bootlabs Tech article emphasizes, AI in EDR is an incremental improvement, not the leap forward vendors claim.

Reality Check: AI in EDR is an incremental improvement, not the leap forward vendors claim. It’s a tool, not a solution.

9. The Case for Layered Defense

No single tool, including EDR, can provide complete protection. A robust cybersecurity strategy requires:

• Network Segmentation: Limits the spread of breaches within the network.
• Endpoint Protection Platforms (EPP): Complements EDR with preventive measures.
• User Training: Educates employees on recognizing phishing and social engineering attacks.
• Zero Trust Architecture: Ensures that no user or device is trusted by default, reducing the attack surface.

Reality Check: EDR is one cog in a much larger machine. Without complementary defenses, organizations are left exposed. As the Cyberoo article aptly puts it, “EDR is not enough anymore.”

Conclusion: EDR’s Place in the Bigger Picture

EDR systems are valuable but deeply flawed tools. Their reactive nature, resource intensity, and struggles with advanced threats expose organizations to significant risk. The illusion of comprehensive protection shatters under scrutiny, revealing a need for a layered, strategic defense framework.

In the evolving threat landscape, defenders must abandon the notion of silver bullets. EDR is not a panacea—it’s a tactical component that requires augmentation, vigilance, and constant innovation to keep pace with attackers. Organizations must adopt a holistic approach to cybersecurity, combining EDR with other tools, processes, and strategies to build a resilient defense.

Final Reality Check: EDR is a tool, not a solution. It’s time to stop treating it as the ultimate answer and start building a comprehensive, adaptive security posture that can withstand the relentless onslaught of modern cyber threats. Use this tool and build a really good solution for your company, orchestration is the answer maybe you need.

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The Problem with EPP

Endpoint Protection Platforms (EPPs) were once the cornerstone of endpoint security, designed to prevent malware from executing in the first place. However, the rise of Endpoint Detection and Response (EDR) has pushed EPP into the shadows, with vendors focusing on EDR’s flashy features like behavioral analysis and threat hunting . But here’s the problem: EPP’s core limitations remain, and EDR’s marketing-driven consolidation hasn’t solved them.

1. EPP’s Core Flaws

EPPs rely on signature-based detection, heuristic analysis, and application whitelisting to block threats. While these methods work against known malware, they struggle with:

• Obfuscated Malware: Packed or encrypted files easily bypass signature checks.
• Fileless Attacks: Malware running in memory leaves no trace for EPP to detect.
• Living-Off-the-Land (LOL): Attackers abuse legitimate tools like PowerShell or WMI, blending in with normal activity.

Reality Check: EPPs are also reactive by design, unable to stop advanced threats that don’t match known patterns.

2. EDR’s Marketing Over Substance

EDR vendors have absorbed EPP into their platforms, promising unified protection . But this consolidation often prioritizes EDR’s detection and response capabilities over EPP’s prevention strengths. The result? EPP becomes a forgotten feature , with organizations focusing on EDR’s post-breach capabilities rather than stopping attacks upfront.

• Example: An EDR solution might detect a PowerShell-based attack after it executes, but the integrated EPP fails to block it in the first place.

3. The Illusion of Unified Security

Combining EPP and EDR into a single platform sounds great, but it doesn’t fix EPP’s flaws. Misconfigurations, resource consumption, and gaps in coverage persist, especially in hybrid environments with remote or cloud endpoints.

• Resource Hog: Unified platforms often consume more CPU and memory, slowing down systems and forcing organizations to disable critical features.
• False Positives: Heuristic and behavioral analysis in EPP still generates noise, overwhelming security teams.

Reality Check: Unified solutions are a marketing gimmick, not a cure-all. EPP’s limitations remain, even when bundled with EDR.

4. The Cost of Neglecting EPP

By focusing on EDR’s post-breach capabilities, organizations risk neglecting prevention . This leaves them exposed to fileless malware , zero-day exploits , and other advanced threats that EDR struggles to detect in time.

• Example: A ransomware attack slips past the EPP, and the EDR only detects it after files are encrypted—too late to prevent damage

Reality Check: Prevention is better than cure, but EDR’s hype has made EPP an afterthought.

Conclusion: Don’t Forget EPP

EPPs may not be as flashy as EDR, but they remain a critical layer of defense. Organizations must resist the allure of EDR marketing and ensure their EPP capabilities are properly configured and actively maintained. A balanced approach that combines prevention (EPP) with detection and response (EDR) is the only way to build a resilient security posture.

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The Cat-and-Mouse Game: Why Security Vendors Are Always Behind

The cybersecurity industry is fundamentally reactive. It’s a classic cat-and-mouse game, but with a twist: the cat doesn’t move until the mouse has already caused damage. Here’s how it typically plays out:

1. A New Threat Emerges: A new malware family, phishing technique, or APT campaign gains traction.
2. Customers Get Hit: Organizations using the security product suffer breaches, data leaks, or ransomware attacks.
3. Pressure Builds: Customers demand better protection, threatening to switch to a competitor.
4. Features Are Added: The vendor finally implements new detection methods or analysis techniques.

This cycle means that security products are always one step behind. By the time a new feature is released, threat actors have already moved on to the next big thing. It’s like showing up to a gunfight with a slingshot—you’re just not equipped to handle what’s coming. We need more proactive solutions than reactive solutions, reactive solutions always needed but proactive solutions will make the difference. But still you need to sell that proactive solution, right?

Now, let’s make this fun. Imagine your company’s physical security system: you’ve got guards, cameras, laser motion detectors, and maybe even a moat filled with alligators (because why not?). If you come up with a brilliant, proactive trap to catch thieves—like a hidden net that drops from the ceiling—you don’t announce it to the world, right? You keep it secret so the thieves walk right into it. But in cybersecurity, the moment you call your product “solutions like nobody ever seen” and start selling it, you’re basically announce your cutting edge solution to public, or at least you mention it while selling it and that guys spills the beans. Suddenly, your slogan becomes “solutions like… somebody seen.”

“Hey, look! We’ve got a net here, lasers over there, and alligators in the moat. Good luck!”

So, how do you sell your product without giving away the game? Beats me. Figure it out—I’m just here to point out the absurdity of it all.

The Role of Security Product Vendors

Security vendors face a challenging balancing act. On one hand, they need to innovate and deliver cutting-edge solutions to stay competitive. On the other, they must ensure their products are profitable and marketable. This often leads to tough decisions about where to focus resources. For example:

• AI-Powered Detection: While many vendors tout “AI-powered” features, the reality is that these systems often rely on basic machine learning models. While these models can be effective, they’re not the magical, all-knowing solutions that marketing sometimes suggests.
• Compliance-Driven Features: Vendors often prioritize features that help organizations meet regulatory requirements (e.g., GDPR, HIPAA). While these are important, they can sometimes overshadow more proactive security improvements. (these are easier to sell to executives, you do the math)
• Reactive Innovation: Vendors may delay implementing advanced detection techniques until they’re absolutely necessary, often in response to customer demands or high-profile breaches. This reactive approach can leave gaps in protection.

The result? Security vendors are often caught between selling products and solving problems . While their intentions may be good, the pressure to meet market demands can sometimes lead to solutions that prioritize profitability over true innovation. This isn’t to say vendors are entirely to blame—they operate in a complex ecosystem where customer expectations, budget constraints, and evolving threats all play a role. However, it’s clear that the industry needs to find a better balance between selling and securing .

The question is: Are we ready to adapt, or will we keep doing the same things and expecting different results?

So, after this whole text, I felt the need to answer this question as it came to my mind, as with the entire text, these are my thoughts. And here’s the cold, hard truth: money runs the world. Security companies aren’t in the business of innovation—they’re in the business of profit. They won’t spend a dime on new technologies unless someone’s waving a check in their face. And even if there’s a brilliant engineer in the corner screaming, “Hey, I can build something amazing!” they’ll be told to sit down, shut up, and focus on whatever brings in the most cash.

Why? Because time equals money, and these companies are allergic to wasting either. They’ll squeeze every last drop of productivity out of their employees, making sure they’re churning out features that customers will pay for—not experimenting with risky, unproven ideas. And let’s be real: the customers don’t want the unknown. They want the familiar, the comfortable, the “proven” (even if it’s proven to fail).

# The Profit Equation (Not Just for Cybersecurity)
Profit = (Hype * Marketing) + (Fear * Sales) - (Innovation * Innovation_Importance)

# Where:
#   Hype = Buzzwords like "AI-powered," "next-gen," "holistic" (or whatever industry you're in)
#   Marketing = Overpromising and underdelivering
#   Fear = "If you don’t buy this, you’ll regret it!" (or "get hacked," "lose customers," etc.)
#   Sales = Convincing customers to pay 2x for the same old thing
#   Innovation = The cost of actually solving problems (spoiler: it’s expensive)
#   Innovation_Importance = 0 (because who cares about solving problems when you can just sell hype?)

# Example Calculation (Cybersecurity Edition):
Hype = 9001  # "Our EDR works in ways even we can’t understand!"
Marketing = 1000  # Fancy brochures and flashy demos
Fear = 9999  # "Ransomware is coming for YOU!"
Sales = 5000  # Upselling "premium features" that do nothing
Innovation = 1000000  # The cost of actually innovating (but we’re not spending this)
Innovation_Importance = 0.01  # "Innovation? What’s that? It costs money, no no no nooowaayy!!!"
Taxes = get_taxes_rate() # You need to pay your taxes


if customer_pays_for_innovation_or_losing_customer():
  Innovation_Importance = 0.1 # It's time to push the boat out
else:
  Innovation_Importance = 0.001 # You can think, it was 0.01, it is inflation 


Gross_Profit = (Hype * Marketing) + (Fear * Sales) - (Innovation * Innovation_Importance)
Net_Profit = calculate_net_profit(Gross_Profit, Taxes)

# After all, profit is good, investors are happy (because why bother solving problems when you can just sell fear?)

# Note: This formula works for ANY industry, but we’re using cybersecurity as the example
# because, well, it’s the one setting your data on fire while selling you the extinguisher.

So, what’s the result? A never-ending cycle of mediocrity. Security companies keep selling the same old stuff with a shiny new label or a little sprinkle on top, customers keep buying it because they don’t know any better, and the attackers are “how will we launder this much money”. It’s like selling umbrellas in a hurricane—sure, you’re making money, but everyone’s still getting soaked.

Final Thoughts: A Little Pessimism Goes a Long Way

Yes, this article is aggressive, pessimistic, and sarcastic—on purpose. While everyone’s high-fiving over buzzwords and shiny labels, I thought a little pessimism might help us see the truth. Or am I some angelic being who’d never think like that? Please. I’m just the guy pointing out the emperor has no clothes.

The cybersecurity industry (and most industries) is a profit-driven circus where innovation is the elephant in the room. If we don’t call out the nonsense, we’ll keep spinning in circles while attackers pop champagne and toast to our incompetence .

So, stay safe and be awake.

References:

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