T1595.003: Wordlist Scanning
Adversaries may iteratively probe infrastructure using brute-forcing and crawling techniques. While this technique employs similar methods to Brute Force, its goal is the identification of content and infrastructure rather than the discovery of valid credentials. Wordlists used in these scans may contain generic, commonly used names and file extensions or terms specific to a particular software. Adversaries may also create custom, target-specific wordlists using data gathered from other Reconnaissance techniques (ex: Gather Victim Org Information, or Search Victim-Owned Websites).
For example, adversaries may use web content discovery tools such as Dirb, DirBuster, and GoBuster and generic or custom wordlists to enumerate a website’s pages and directories.[1] This can help them to discover old, vulnerable pages or hidden administrative portals that could become the target of further operations (ex: Exploit Public-Facing Application or Brute Force).
As cloud storage solutions typically use globally unique names, adversaries may also use target-specific wordlists and tools such as s3recon and GCPBucketBrute to enumerate public and private buckets on cloud infrastructure.[2][3] Once storage objects are discovered, adversaries may leverage Data from Cloud Storage to access valuable information that can be exfiltrated or used to escalate privileges and move laterally.
Security context for executives and security teams
T1595.003: Wordlist Scanning describes Adversaries may iteratively probe infrastructure using brute-forcing and crawling techniques. While this technique employs similar methods to [Brute Force](https://attack.mitre.org/techniques/T1110), its goal is the identification of content and infrastructure rather than the discovery of valid credentials. Wordlists used in these scans may contain generic, commonly used names and file extensions or terms specific to a particular software. Adversaries may also create custom, target-specific wordlists using data gat...
Executive priority
T1595.003: Wordlist Scanning is an official MITRE ATT&CK technique. Glexia treats it as defensive behavior context for prioritizing monitoring, control validation, and response planning without using the object by itself as an attribution claim.
Technical view
Security teams should validate T1595.003: Wordlist Scanning by reviewing the official ATT&CK relationships, mapped tactics (reconnaissance), supported platforms (PRE), and available local telemetry before making detection or mitigation decisions.
Likely telemetry
- Official ATT&CK relationships and object metadata
Detection direction
- Validate whether T1595.003: Wordlist Scanning appears in your detection coverage and tabletop scenarios.
- Use the object to align executive risk language with SOC, incident response, and detection engineering work.
- Do not treat ATT&CK relationship context as attribution without corroborating evidence.
Mitigation priorities
- Map the object to existing controls and identify missing telemetry or response ownership.
- Prioritize mitigations that reduce exposure on the listed platforms and tactics.
- Review adjacent ATT&CK relationships before changing policy, detections, or reporting language.
Additional notes and limits
Baseline Glexia take generated from the official MITRE ATT&CK STIX object, source hash, tactics, platforms, and detection fields. It is safe to replace with a richer model-generated take for the same source hash later.
This baseline take is source-grounded and schema-validated, but it does not include environment-specific telemetry, incident evidence, or threat-intelligence corroboration.
Generated from the cited source records. This long-tail analysis has not been individually reviewed by a named human.
Wordlist Scanning
Adversaries may iteratively probe infrastructure using brute-forcing and crawling techniques. While this technique employs similar methods to Brute Force, its goal is the identification of content and infrastructure rather than the discovery of valid credentials. Wordlists used in these scans may contain generic, commonly used names and file extensions or terms specific to a particular software. Adversaries may also create custom, target-specific wordlists using data gathered from other Reconnaissance techniques (ex: Gather Victim Org Information, or Search Victim-Owned Websites).
For example, adversaries may use web content discovery tools such as Dirb, DirBuster, and GoBuster and generic or custom wordlists to enumerate a website’s pages and directories.[1] This can help them to discover old, vulnerable pages or hidden administrative portals that could become the target of further operations (ex: Exploit Public-Facing Application or Brute Force).
As cloud storage solutions typically use globally unique names, adversaries may also use target-specific wordlists and tools such as s3recon and GCPBucketBrute to enumerate public and private buckets on cloud infrastructure.[2][3] Once storage objects are discovered, adversaries may leverage Data from Cloud Storage to access valuable information that can be exfiltrated or used to escalate privileges and move laterally.
How security teams should use this page
Treat this object as behavior context, not an attribution claim. Validate the related groups, software, data sources, and mitigations against official ATT&CK relationships and your own telemetry before making control-coverage decisions.
Related techniques
This mirrors the MITRE pattern of making group, software, campaign, and technique relationships scannable. Relationship notes come from mirrored ATT&CK relationship text when available.
| Domain | ID | Name | Relationship / procedure |
|---|---|---|---|
| Enterprise | T1595 | Active Scanning | This object subtechnique of Active Scanning. |
Groups, software, and campaigns
G0096: APT41
APT41 is a threat group that researchers have assessed as Chinese state-sponsored espionage group that also conducts financially-motivated operations. Active since at least 2012, APT41 has been observed targeting various industries, including but not limited to healthcare, telecom, technology, finance, education, retail and video game industries in 14 countries.[1] Notable behaviors include using a wide range of malware and tools to complete mission objectives. APT41 overlaps at least partially with public reporting on groups including BARIUM and Winnti Group.[2][3]
G0123: Volatile Cedar
Volatile Cedar is a Lebanese threat group that has targeted individuals, companies, and institutions worldwide. Volatile Cedar has been operating since 2012 and is motivated by political and ideological interests.[1][2]
All related ATT&CK context
Mitigation direction
Object version and sync metadata
The fields below describe the current mirrored snapshot. When Glexia retains multiple ATT&CK source imports, you can open the table to compare the same object across releases (hashes and MITRE timestamps). For MITRE’s own release notes and roadmap, see ATT&CK resources — Updates.
Imported snapshots across ATT&CK releases(2)
| Release | Bundle imported | Object version | Modified | Status | Raw hash |
|---|---|---|---|---|---|
| 19.2 | 1.0 | Current bundle | c53817697a08… | ||
| 19.1 | 1.0 | Older bundle | c53817697a08… |
Mirrored ATT&CK source object
The raw object is retained through the mirrored ATT&CK source bundle and object hash. The raw endpoint returns the exact object from the mirrored bundle when available.
External references and citations
MITRE external references are preserved separately from Glexia analysis so citations remain traceable to their original source records.
- [1]ClearSky Lebanese Cedar Jan 2021
ClearSky Cyber Security. (2021, January). “Lebanese Cedar” APT Global Lebanese Espionage Campaign Leveraging Web Servers. Retrieved February 10, 2021.
Open source URL - [2]S3Recon GitHub
Travis Clarke. (2020, March 21). S3Recon GitHub. Retrieved March 4, 2022.
Open source URL - [3]GCPBucketBrute
Spencer Gietzen. (2019, February 26). Google Cloud Platform (GCP) Bucket Enumeration and Privilege Escalation. Retrieved March 4, 2022.
Open source URL - [4]mitre-attackT1595.003Open source URL
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