Ignite & Activate: How Advanced WildFire is Disrupting Malware with Code Genes

[lizwang]

By: @lizwang

Co-Author: @tbolatiwa

Most software, whether benign or malicious, is built upon existing code. This inherent reuse has long given adversaries a powerful advantage, allowing them to replicate and adapt malicious techniques with ease.  That advantage has now been amplified by large language models (LLMs). Adversaries can use AI to automatically modify malware code by adding layers of obfuscation, renaming variables, or reordering instructions. The challenge is no longer identifying what malware looks like, but understanding how it’s built and how its code continues to evolve.

 

Malware Evolution at Machine Speed: Why Manual Detection Falls Behind

For many years, code reuse has been a cornerstone of efficient software development; however, malicious actors have also exploited it to their advantage. Instead of writing a new encryption routine or C2 communication module from scratch, adversaries simply copy, paste, or modify existing code to create new malware strains. Now, with the rise of large language models (LLMs), this advantage has been amplified, making it easier than ever to generate and evolve malicious code at unprecedented speed.

Figure 1: Disrupting Malware with Code Gense (Video)

Imagine a threat actor asking an AI: "Write a PowerShell script to download a file from a URL and execute it, but obfuscate it so it avoids detection." While the AI might not write novel malware, it excels at generating variations of existing code, changing variable names, reordering simple instructions, and adding layers of obfuscation. This rapidly generates:

• Variant Swarms: An unprecedented number of new malware samples, many sharing significant portions of code with their predecessors, but with just enough difference to bypass simple hash-based signatures.
• Faster Campaigns: Adversaries can spin up new, evasive campaigns in hours, not weeks.
• Elusive Threats: Malware becomes harder to detect through traditional means, as new variants constantly shift their superficial appearance.

In this environment, the industry’s dependence on human-driven detection has become a significant bottleneck. Dedicated malware researchers and analysts across the security community spend countless hours manually reverse-engineering individual samples to craft YARA rules. This process is:

• Too Slow: It can take hours, often days, to analyze a complex sample and validate a robust YARA rule. By the time a rule is deployed, a new variant might already be in the wild.
• Not Scalable: With over a million new unique files appearing daily, a human team simply cannot keep pace.
• Prone to Gaps: Highly obfuscated or "singleton" malware (rare, unique samples) can be exceptionally difficult and time-consuming to analyze manually, leading to detection gaps.

We will continue to fall behind if we rely solely on manual detection. The AI-driven malware assembly line requires an automated, intelligence-powered counterstrategy.

Introducing Code Gene: The Next Evolution in Advanced WildFire

Advanced WildFire is the industry's largest malware prevention engine, powered by Precisio AI to deliver unmatched accuracy and speed. It is engineered to enable detection and prevention at the speed and scale of the most advanced and evasive threats with no business interruption, leveraging a brand-new, cloud-delivered infrastructure.

Our system automates the traditionally time-consuming process of malware signature generation, turning the adversary’s greatest advantage—code reuse—into a powerful defense mechanism. Code Gene technology is one of several advanced capabilities that power Advanced WildFire, built on the groundbreaking concept of software genomics. 

Figure 2: Code Gene 

• Gene Extraction: We automatically disassemble and analyze executables. The code is transformed into searchable tokens, or 'Code Genes.' Similar to how a search engine breaks a document into keywords, our analysis engine dissects code into small fragments, stripping away noise like memory addresses and variable names. This creates an abstract, resilient fingerprint of the function's true intent.
• Genome Comparison: This 'Code Gene' is instantly compared against the massive Code Genome Database, a critical resource that catalogs both malicious and trusted software. This comparison allows us to rapidly classify the file's lineage, identify malicious functionality, and even recognize shared code from known threat groups.

The impact of this automated approach is profound:

• Superior Coverage for Elusive Threats: Many modern threats are designed to evade detection by stripping out identifiable strings, making traditional YARA rule generation nearly impossible. The Advanced WildFire Code Gene system solves this by analyzing the structural and logical patterns of code, creating resilient fingerprints that capture the core malicious behavior shared across multiple variants. This approach enables effective protection against heavily obfuscated samples and accelerates rule creation, often from as few as one or two malware samples. In one instance, a rule built from just two samples of a PE trojan injector successfully identified 19 additional variants within three days of deployment, proving its speed and effectiveness.

• Unmatched Efficiency and Speed: The reality of modern cyber threats is that the lifecycle of some malware families is incredibly short, turning manual rule creation into a critical bottleneck. A human analyst might take half a day or even one or two days to create and validate a single complex YARA rule; by the time that rule is deployed, a new variant may already be in the wild. 

Advanced WildFire significantly reduces this detection latency. With our automated Code Gene system, WildFire can generate, validate, and release more than 20 signatures within one hour. This represents a major leap in efficiency. This rapid, automated pipeline ensures that we deploy defenses in a timely manner to catch fast-evolving malware families and widespread campaigns, protecting our customers immediately rather than retrospectively.

Unrivaled Coverage Through Code Reuse Among Families:

The ultimate power of the Code Gene technology is its ability to link the unrelated. Adversaries frequently reuse malicious modules or obscure builders across different malware families, making manual attribution a nearly impossible task. 

Advanced WildFire excels at identifying these shared code genes and instantly turning that link into a detection rule. For instance, our system identified a single, unique Code Gene that invoked the Windows API function through an abnormal, indirect call, a technique specifically designed to evade detection. This single, resilient fingerprint proved to be a powerful indicator of code reuse among a wide array of threats, identifying over 30k+ malware samples and successfully linking ten major families, including LockBit, dacic, and systex, with a single rule. 

Step-by-Step Guide: Enabling Advanced WildFire

The essential steps for configuring an Advanced WildFire policy are outlined below to help you quickly enable protection across your network. Existing customers should also verify that Advanced WildFire is properly licensed and fully enabled to ensure they are getting continuous, real-time protection from the latest threats.

Activate on Panorama:

1. Verify License: Ensure that the Advanced WildFire service is licensed and active on the firewall.
2. Create a WildFire Analysis Profile: Select Objects -> Security Profiles -> WildFire Analysis and add or modify a WildFire Analysis profile. 
3. Enable Inline Cloud Analysis: Select your WildFire analysis profile and then go to Inline Cloud Analysis and enable cloud inline analysis. Specify a rule defining an action to take when Advanced WildFire Inline Cloud Analysis detects advanced malware.
4. Apply and Activate: Apply the WildFire Analysis profile to Security policy rules. Finally, commit this policy to activate the changes. 

Activate Strata Cloud Manager (SCM):

1. Verify License: Ensure that the Prisma Access subscription covers Advanced WildFire.
2. Navigate to WildFire Analysis and Antivirus Settings: 
• Go to Configuration -> NGFW and Prisma Access -> Security Services -> WildFire Analysis and Antivirus.
• Move between the Profiles, Settings, and Best Practices to explore the available Advanced WildFire features.
3. Create a WildFire Analysis and Antivirus profile: 
• On the WildFire Analysis and Antivirus page, add or modify a profile.  
4. Enable Real-Time Analysis: Select your WildFire analysis profile and then go to Inline Cloud Analysis and enable cloud inline analysis. Specify a rule defining an action to take when Advanced WildFire Inline Cloud Analysis detects advanced malware.
5. Apply and Activate: Apply the WildFire Analysis profile to Security policy rules. Finally, commit this policy to activate the changes. 

Winning the Arms Race with Automation

The era of AI-fueled code reuse demands a new approach to malware detection. Palo Alto Networks' Advanced WildFire is not just another tool; it's a strategic shift. Automating the identification of malicious code reuse and the creation of resilient YARA rules enables security teams to move more quickly, achieve broader coverage, and ultimately stay ahead in the ongoing malware arms race.

To learn more about how Advanced WildFire, powered by Precision AI, protects your organization from evolving threats, connect with a Palo Alto Networks representative or refer to the Advanced WildFire datasheet for additional information.