Listen to this Post
Introduction: The Power of a Filename in Malware Detection
In the ongoing cat-and-mouse game between malware authors and cybersecurity professionals, every little detail can tip the scales. One of the often-overlooked elements is the name of the executable file. While malware detection systems rely on behavioral and static analysis, malware authors have learned to weaponize simple tricks like checking the file’s name to detect if it’s under scrutiny. This article dives into a subtle yet powerful evasion technique used by modern malware: self-identification through filename analysis. Inspired by Xavier Mertensâ proof-of-concept, weâll explore how adversaries exploit basic Windows APIs to identify when their code is being analyzed and quietly disappear before doing any damage.
Filename-Based Evasion: A Subtle but Effective Tactic
Modern malware doesnât just aim to infect systems â it also strives to evade detection and reverse engineering. One of the first steps in malware analysis is placing the suspicious file into a sandbox or automated analysis pipeline. In this process, analysts often rename the file to something obvious, like sample.exe, malware.exe, or test.exe. This habit, while practical for tracking purposes, can actually alert the malware that itâs under analysis.
To detect its own filename, malware can use the GetModuleFileName API function in Windows. This function retrieves the full path of the current executable. If the filename matches a list of common analysis filenames, the malware can simply exit without executing any malicious behavior.
A sample program in C demonstrates this evasion technique. It imports the Windows API function GetModuleFileName, extracts the filename of its own process, and compares it against a predefined list of suspicious filenames. If a match is found, the program exits silently. If not, it proceeds with its payload â in the proof-of-concept, a cheeky message simulating infection.
This trick is simple yet effective. Even if the filename check seems minor, it can derail automated malware detection, especially in environments where analysis is batch-processed. The proof-of-concept includes names like malicious.exe, submitted_sample.exe, and virus.exe, which are commonly used in sandboxes or by researchers.
The potential doesnât stop there. Real malware can implement more advanced logic. It could:
Load the list of bad filenames from remote servers
Obfuscate the comparison logic
Perform fuzzy matching (e.g., partial strings or case-insensitive checks)
Combine this tactic with other anti-debugging and anti-VM strategies
Such small-scale evasion logic represents a growing challenge for static and dynamic analysis tools. While this technique isn’t groundbreaking on its own, its ease of implementation and high effectiveness make it a favorite among malware authors looking for stealth.
What Undercode Say:
A Tactical Shift Toward Smarter Evasion
This proof-of-concept is a clear indicator of a broader trend in modern malware: smarter evasion without complexity. By using well-documented APIs like GetModuleFileName, malware authors exploit features designed for legitimate use to outmaneuver defenders. They donât need exotic exploits when simple logic gates can stop a sandbox dead in its tracks.
Misuse of API Calls for Stealth
Microsoftâs extensive API ecosystem provides everything developers need to build powerful software â but this power is often repurposed for malicious ends. In this case, an API that helps retrieve a module’s file path becomes a stealth sensor. With just a few lines of code, malware can detect whether it’s running under a suspicious filename and adjust its behavior accordingly. This is an example of dual-use functionality, where legitimate tools are co-opted by attackers.
Automation Meets Its Match
Most cybersecurity operations today rely heavily on automated pipelines for triage. These systems are fast, efficient, and standardized. However, this standardization is also a weakness. When malware expects a filename like malware.exe and sees it, that uniformity becomes a red flag. This is where automation hits its limit â and where manual, stealth-aware analysis becomes essential.
Impact on Sandbox Efficacy
This trick also undermines sandbox environments. If malware identifies itâs in a controlled environment based on the filename alone, it may suppress any malicious behavior, leading analysts to mistakenly believe itâs harmless. As a result, these sandboxes return false negatives â and dangerous files slip through.
Obfuscation & Remote Lists: The Next Level
In real-world attacks, threat actors wouldnât hardcode filename lists. Theyâd likely obfuscate the logic, encrypt the comparison list, or even pull it dynamically from command-and-control servers. This adds layers of difficulty for reverse engineers who are trying to understand how the malware operates.
Importance of Context in Filename Assignment
Security teams need to be cautious about naming conventions. While itâs convenient to name a file suspicious.exe, doing so could trigger premature exit conditions in malware. Even better would be to randomize filenames or use benign-sounding names during analysis to avoid early detection mechanisms built into malware.
Simple Doesnât Mean Ineffective
This technique proves that complexity isnât always required to bypass security measures. Simple evasion strategies like filename checking can effectively neutralize even sophisticated security setups. And because it relies on Windows-native functionality, thereâs no need for shellcode, obfuscation, or additional files.
The Psychological Warfare of Malware
The ironic console message âLooks good! Letâs infect this host!â isnât just code humor. It illustrates the confidence malware authors have when they know theyâve dodged detection. The war isnât just technical â itâs psychological. They know what defenders expect, and they design their malware to appear âsafeâ when under a microscope.
Countermeasures Are Feasible
Defenders can fight back by:
Randomizing filenames during analysis
Using runtime analysis tools that hook into process behavior regardless of filename
Modifying sandbox behavior to avoid default naming
Running multiple rounds of analysis under different names
These countermeasures must become standard to stay ahead of basic but deadly tricks like this.
đ Fact Checker Results:
â
The use of GetModuleFileName for filename detection is documented and valid.
â
File-based evasion is a known and effective tactic in modern malware.
â
Sandbox environments often use predictable filenames, aiding detection evasion.
đ Prediction:
As malware continues to evolve, filename-based evasion will become more sophisticated. Expect future variants to integrate machine learning to detect not only filenames but file metadata and environment cues. Security teams will need to rethink how they label and analyze files, focusing on dynamic behavioral triggers rather than static analysis alone. Filename tricks like this wonât go away â theyâll become even harder to detect. đĄđĄď¸
References:
Reported By: isc.sans.edu
Extra Source Hub:
https://www.digitaltrends.com
Wikipedia
OpenAi & Undercode AI
Image Source:
Unsplash
Undercode AI DI v2
