Enhancing Cortex XQL Threat Detection with Full Summary in Uncoder AI

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May 02, 2025 · 6 min read
Enhancing Cortex XQL Threat Detection with Full Summary in Uncoder AI

As attackers become more creative in bypassing traditional network defenses, analysts need fast, clear insight into the logic behind complex detection rules. That’s where Uncoder AI’s Full Summary feature becomes a game-changer—especially for teams working with Palo Alto Cortex XSIAM Query Language (XQL).

In a recent use case, Uncoder AI helped threat hunters break down an XQL detection rule that flags HTTP requests to low-reputation top-level domains (TLDs) or to suspicious file extensions, often used during the early stages of malware delivery or phishing campaigns.

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The Detection in Focus

The XQL rule performs dual filtering:

  1. Low-Reputation TLDs — The query identifies HTTP traffic to domains ending in TLDs like .ru , .su , .party , .xyz , .click , and others commonly linked to spam or malware infrastructure.

  2. Suspicious File Types — It also inspects URL patterns and MIME types for indicators of executable content, such as .exe , .js , .vbs , .ps1 , or application/x-msdownload.
Input we used (click to show the text)
datamodel dataset = *
| filter (host ~= “.*\.bid” or host ~= “.*\.by” or host ~= “.*\.cf” or host ~= “.*\.click” or host ~= “.*\.cm” or host ~= “.*\.ga” or host ~= “.*\.gq” or host ~= “.*\.ir” or host ~= “.*\.kp” or host ~= “.*\.loan” or host ~= “.*\.ml” or host ~= “.*\.mm” or host ~= “.*\.party” or host ~= “.*\.pw” or host ~= “.*\.ru” or host ~= “.*\.su” or host ~= “.*\.sy” or host ~= “.*\.tk” or host ~= “.*\.top” or host ~= “.*\.tv” or host ~= “.*\.ve” or host ~= “.*\.work” or host ~= “.*\.xyz”) and ((xdm.network.http.url ~= “.*\.bat” or xdm.network.http.url ~= “.*\.bin” or xdm.network.http.url ~= “.*\.cmd” or xdm.network.http.url ~= “.*\.cpl” or xdm.network.http.url ~= “.*\.dll” or xdm.network.http.url ~= “.*\.dylib” or xdm.network.http.url ~= “.*\.elf” or xdm.network.http.url ~= “.*\.exe” or xdm.network.http.url ~= “.*\.hta” or xdm.network.http.url ~= “.*\.iso” or xdm.network.http.url ~= “.*\.jar” or xdm.network.http.url ~= “.*\.js” or xdm.network.http.url ~= “.*\.lnk” or xdm.network.http.url ~= “.*\.msi” or xdm.network.http.url ~= “.*\.pif” or xdm.network.http.url ~= “.*\.ps1” or xdm.network.http.url ~= “.*\.py” or xdm.network.http.url ~= “.*\.reg” or xdm.network.http.url ~= “.*\.scr” or xdm.network.http.url ~= “.*\.sh” or xdm.network.http.url ~= “.*\.so” or xdm.network.http.url ~= “.*\.vbs” or xdm.network.http.url ~= “.*\.wsf”) or resp_mime_types in (“application/vnd.microsoft.portable-executable”, “application/x-bat”, “application/x-dosexec”, “application/x-elf”, “application/x-iso9660-image”, “application/x-java-archive”, “application/x-ms-shortcut”, “application/x-msdos-program”, “application/x-msdownload”, “application/x-python-code”, “application/x-sh”))

// name: HTTP Request to Low Reputation TLD or Suspicious File Extension
// uuid: 68c2c604-92ad-468b-bf4a-aac49adad08c
// author: @signalblur, Corelight
// licence: DRL 1.1

AI Output (click to show the text)
This Cortex XQL query is designed to detect and identify potentially malicious HTTP requests based on two main criteria:

  1. Low Reputation Top-Level Domains (TLDs): The query filters for HTTP requests where the host domain ends with certain TLDs that are commonly associated with malicious activities or have a low reputation. These include, but are not limited to, .bid, .by, .cf, .click, .cm, .ga, .gq, .ir, .kp, .loan, .ml, .mm, .party, .pw, .ru, .su, .sy, .tk, .top, .tv, .ve, .work, and .xyz. The use of these TLDs does not necessarily indicate malicious activity, but they are often seen in the context of spam, phishing, or malware distribution.
  2. Suspicious File Extensions: In addition to filtering by domain, the query also looks for HTTP requests that involve URLs with suspicious file extensions or MIME types that could be indicative of executable or potentially malicious files. The file extensions and MIME types targeted include those commonly associated with executables, scripts, or archives that could contain malware, such as .bat, .bin, .cmd, .cpl, .dll, .dylib, .elf, .exe, .hta, .iso, .jar, .js, .lnk, .msi, .pif, .ps1, .py, .reg, .scr, .sh, .so, .vbs, and .wsf. The MIME types specifically mentioned, like application/vnd.microsoft.portable-executable or application/x-bat, further narrow down the search to include files that are explicitly marked as executables or scripts.

The purpose of this query is to help security analysts identify potential security threats by highlighting HTTP traffic that matches these criteria. It can be particularly useful in detecting early stages of a malware infection or data breach, where attackers might use less common TLDs and suspicious file types to evade traditional security controls. By monitoring for such patterns, organizations can proactively investigate and mitigate potential threats before they cause significant harm.
This query is part of a broader set of tools used in threat hunting and incident response, leveraging the Cortex XDR platform’s capabilities to analyze network traffic and endpoint data for signs of malicious activity. The authors, @signalblur and Corelight, have made this query available under the DRL 1.1 license, contributing to the community’s efforts to combat cyber threats through shared knowledge and resources.

But for many analysts, especially under pressure, this kind of query can appear dense and time-consuming to interpret. That’s where Full Summary steps in.

What Full Summary Delivered

Uncoder AI instantly generated a complete and plain-language breakdown of the rule’s logic, intent, and threat context. It not only explained which patterns were being checked, but why—linking specific file types and domain patterns to known adversary tradecraft.

Real Value: Time Saved, Risk Reduced

This isn’t just about translation. It’s about accelerating detection validation and enabling faster response by giving security teams immediate insight into what a query actually detects.

In this case, the team was able to:

  • Rapidly confirm that the detection would catch suspicious download attempts tied to known bad TLDs
  • Identify gaps in their coverage for additional risky MIME types
  • Confidently roll the rule into production without back-and-forth interpretation

Conclusion: Context at the Speed of Threat

By decoding complex Cortex XQL logic, Uncoder AI’s Full Summary bridges the gap between detection logic and operational action. For defenders, especially during fast-paced threat response or purple teaming, this context isn’t just helpful—it’s essential.

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