Cyberattacks are becoming sophisticated, and how! Cybercriminals have moved beyond simple keystroke theft to mass identity harvesting on a global scale. In June 2025, researchers revealed a cache of 16 billion login records gathered by malware known as infostealers, showing how far today’s threats have outpaced older tools like keyloggers. While a keylogger quietly logs every typed character and, in some cases, grabs screenshots or clipboard content, its scope is limited.

Infostealers operate on a completely different level. Instead of waiting for you to type, they harvest everything at once: browser cookies, login tokens, email accounts, crypto wallets, and sensitive business files. Within seconds, an attacker can hijack entire digital identities, enabling large-scale account takeovers and targeted phishing campaigns.

This article breaks down the key differences between keyloggers and infostealers, showing how each threat operates at a different scale. Knowing the distinction is essential for building a defense strategy that stops both keystroke theft and large-scale data compromise.

Keyloggers record every keystroke a user makes, giving attackers raw text they must sift through to extract passwords, payment details, or personal information. These programs hide deep within the operating system, intercepting keyboard activity through system hooks. On Windows, they attach to functions that process input, while Mac and Linux use similar techniques.

Because keyloggers only need to capture text, they remain small and lightweight. Most stores encrypt logs locally and periodically send them to attackers through email, FTP, or web requests. The stolen data is then analyzed offline, either manually or with simple scripts that search for patterns such as “@,” “cc,” or “pwd=.”

This design offers both advantages and limits. Keyloggers often evade routine security scans because of their small footprint, yet they miss valuable information such as browser cookies, session tokens, and stored passwords that modern infostealers collect automatically. Still, in targeted campaigns, tools like Snake Keylogger remain dangerous.

A recent variant was distributed through phishing emails and used scripting to avoid detection, recording keystrokes and stealing browser credentials before sending the data to attackers through Telegram bots.

Infostealers represent a major leap forward from traditional keyloggers. While keyloggers quietly record what a user types, infostealers act like full-scale data harvesting machines. They are designed to capture far more than passwords, pulling together entire digital identities in a matter of seconds.

Unlike keyloggers that only track keystrokes, infostealers operate through multiple specialized modules. Each module is designed to target a different type of data, such as browser cookies, cryptocurrency wallets, screenshots, or application credentials.

This modular structure makes them flexible and adaptive. Attackers can remotely switch modules on or off, update them, or replace them based on the value of the target and the need to avoid detection. The result is a constantly evolving threat that traditional, signature-based defenses struggle to keep up with.

Infostealers are not limited to typed input. They can extract credentials from dozens of browsers and extensions, gather autofill data, capture clipboard contents, and even monitor active applications. Tools like Mystic Stealer demonstrate how extensive this can be, collecting data from platforms such Telegram and various cryptocurrency wallets.

Perhaps the most dangerous feature is session hijacking. By stealing authentication cookies and tokens, infostealers allow attackers to take over live sessions in the cloud. This lets them bypass even multi-factor authentication, granting instant access to accounts and services without needing a password.

The scope of infostealers goes well beyond simple credential theft. They target every corner of a device: files, applications, browser sessions, and sensitive business data. This approach turns what might have been an isolated theft into a full system compromise.

By maintaining persistent access across multiple platforms, infostealers pose a long-term risk that far outstrips traditional keyloggers. Think of it in this way: while keyloggers might capture a password, infostealers can capture a digital life.

Infostealers evade traditional defenses by blending in with normal system activity. They use techniques that hide malicious code, mimic trusted programs, and run entirely in memory. As a result, antivirus and endpoint detection tools often miss them until the data is already gone.

Here are the reasons why they are difficult to detect:

• Layered Obfuscation: Malware arrives in encrypted or encoded scripts padded with junk functions. Campaigns like Lumma Stealer hide malicious JavaScript inside fake installers, and each victim receives a unique build, making hash-based detection useless.

• Software Impersonation: Once active, the malware adopts familiar names like update.exe or uses stolen certificates. It executes through Windows utilities such as PowerShell or WMI, “living off the land” without dropping obvious files.
  Memory-Only Execution: Stealers decrypt and run directly in memory, steal data, and vanish when the process closes. Google’s PeakLight research highlighted this method, which leaves almost no trace behind.

• Environment Awareness: Malware checks for sandboxes, debugging tools, or virtual machines and shuts down if suspicious. It also looks at time zones, domains, and user activity to activate only on high-value targets.

• Community Development: Crime-as-a-service models speed up evolution. Developers ship constant updates, criminals test them in the wild, and defenders are left scrambling to adapt.

Infostealers succeed because every stage of the attack is engineered to look routine. Traditional, signature-based tools cannot keep up with their pace of change. Defenders need behavior-based visibility that can detect subtle deviations before data is stolen.

Infostealers are one of the most damaging threats facing enterprises today. They do not simply steal passwords; they harvest entire browser vaults, password managers, VPN credentials, cloud keys, and session tokens. This access allows attackers to impersonate executives, launch business email compromise schemes, and move laterally through networks without detection.

The most immediate danger comes from session hijacking. Authentication cookies and OAuth tokens give attackers the ability to bypass multifactor authentication and maintain access even after passwords are reset. This persistence turns a single infection into a long-term breach with an expanding scope.

Once stolen, credentials rarely stay with the original attacker. They are sold cheaply on underground markets, fueling ransomware, fraud, and espionage. Initial-access brokers amplify the risk by bundling stolen credentials for resale, transforming isolated infections into supply-chain attack vectors.

The stakes are highest for critical sectors such as energy, finance, and healthcare. In these environments, a single infection can escalate from quiet compromise to multimillion-dollar outages within days. The cascading operational and regulatory consequences make clear that traditional defenses cannot contain the business risks posed by infostealers.

Legacy defenses were designed to block known threats, but today they struggle against the agility and evasive techniques of modern infostealers. Antivirus and endpoint detection systems rely heavily on signatures, file hashes, and static rules. These methods are ineffective against credential-stealing malware that constantly changes its appearance and often operates without leaving files behind.

Malware kits generate unique binaries with every build, making hash-based detection obsolete. Additionally, simple encryption or encoding creates new artifacts that bypass scanners with ease. Even when detections are created, attackers typically update their code faster than defenders can adjust, leaving gaps that attackers can exploit.

The challenge grows when infostealers run entirely in memory. Attackers frequently abuse trusted Windows tools to launch their payloads. Since these utilities are critical for daily business operations, security teams cannot simply disable them. In this way, malware hides within normal processes and avoids inspection altogether.

Another issue is the lack of contextual awareness in traditional tools. Activities such as cookie harvesting, clipboard access, and rapid outbound data transfers may appear harmless in isolation. When combined, however, they reveal the behavior of an infostealer. Without the ability to correlate these signals, legacy systems fail to recognize the full attack pattern.

This gap highlights the need for defenses that go beyond static signatures. Infostealers succeed because they blend seamlessly with ordinary system behavior. Detecting them requires adaptive monitoring that looks for behavioral patterns across multiple signals, rather than relying on outdated indicators.

Behavioral AI detects the subtle activity shifts that reveal data theft long before traditional rules can respond. Abnormal builds a baseline of normal behavior for every user, device, and application, then highlights deviations and connects signals to uncover malicious intent.

Keyloggers may still appear in targeted campaigns, but infostealers present a far greater risk. They can harvest entire digital identities, hijack active sessions, and bypass multifactor authentication, threats that conventional defenses often fail to detect.

Abnormal addresses this gap by continuously analyzing identity, email, and cloud activity. Its behavioral AI identifies anomalies that point to credential theft and adapts as new threats emerge. This approach enables security teams to stop infostealers before stolen data can be misused.

Abnormal continuously profiles sender-recipient relationships, login geography, device fingerprints, and application access. This baseline allows it to recognize when malware attempts to steal browser cookies or launch scripted file activity that falls outside normal behavior.

Because models retrain on fresh data, the system adapts to new attack techniques without relying on known indicators. This reduces false positives while ensuring emerging threats are detected.

The platform also correlates signals across email, APIs, and network activity to uncover links that would otherwise remain hidden. A suspicious finance request may be tied to the compromised session cookie observed earlier, exposing the entire attack chain.

By piecing together these signals, Abnormal detects compromised identities early and stops attackers from moving laterally or monetizing stolen access.

Through continuous learning and multi-dimensional analysis, Abnormal detects anomalies across the entire attack lifecycle. This approach stops credential-stealing threats that signature-based tools routinely overlook and ensures defenses evolve alongside attacker techniques.

Want to see how Abnormal can protect your organization from advanced credential-stealing attacks? Book a personalized demo today.