Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects anomalous, high-volume file access (creation, modification, or renaming) targeting sensitive extensions (e.g., .key, .pem, .pfx) within document directories by unsigned processes. It also detects similar high-volume file access behavior that occurs immediately following the mounting of a removable USB device, consistent with CornFlake RAT data exfiltration patterns.
Detects unauthorized file system access to sensitive Gitea configuration files (app.ini) or internal directories (custom/conf, data/tmp) by the gitea/git service account. This pattern is indicative of post-exploitation credential or secret harvesting following remote code execution vulnerabilities, such as the diffpatch git hook RCE (CVE-2026-60004).
Detects multiple stages of the ChocoShell credential and token theft framework, including SYSTEM token impersonation for DPAPI key access, VSS shadow copy abuse to extract browser databases, malicious use of browser remote debugging ports (CDP) for cookie exfiltration, and unauthorized access to browser profile files or Token Broker cache (.tbres) files.
SYSVOL / NETLOGON Enumeration
Cortex XDR
Every authenticated principal in the domain can read SYSVOL, and SYSVOL routinely stores logon scripts, scheduled-task XML, and GPP files that contain plaintext or reversibly-encrypted credentials. Adversaries recursively enumerate SYSVOL to harvest these — either grepping file contents (Select-String 'password') or matching file names (dir *password*). This rule detects the read-access footprint of that enumeration on the DC itself, independent of the tool or command line used, by counting distinct SYSVOL paths accessed by a single non-machine account in a short window. Default threshold is 100, change to a high volume for your environment
Detects suspicious processes that repeatedly enumerate installed software via registry Uninstall keys and access sensitive browser data (History, cookies, and web-data files) over a period of multiple days, while simultaneously generating consistent outbound network beaconing. This behavior is indicative of data-stealing malware, such as the Dolphin X 'AI Profiler', which profiles victims based on their application usage and browsing habits.
Detects instances where processes other than common Chromium-based web browsers attempt to access sensitive files within the browser's User Data directory, such as Login Data, Cookies, or Web Data. This behavior is often indicative of credential theft or session hijacking attempts by unauthorized software or adversaries.
Detects a process accessing browser credential-related files (Login Data, Cookies) across three or more distinct browser profiles within a 5-minute window, followed by the creation of a ZIP archive by the same process within 10 minutes. This behavior is indicative of credential theft activities where browser profiles are harvested and bundled for exfiltration, consistent with techniques used by infostealers like Dolphin X.
Detects file access/read of the in-cluster Kubernetes service account token path or a user's kubeconfig file by a non-standard binary (e.g. scripting interpreter, shell, or network utility) rather than known Kubernetes/orchestration tooling. Narrowed to reduce false positives from legitimate kubectl, helm, kubelet, and container runtime processes that routinely read these credentials.
Detects unauthorized processes attempting to access sensitive browser files containing credentials (e.g., 'Login Data', 'logins.json') and cookies (e.g., 'store.json.mozlz4', 'Browser_Cookies.json'). This behavior is commonly associated with credential harvesting malware such as TOKYOCORE, which seeks to steal stored browser secrets before they can be encrypted or for immediate use in session hijacking.
This rule detects unauthorized access or modifications to hardware watchdog device files (/dev/watchdog, /dev/watchdog0, /dev/misc/watchdog, /dev/FTWDT101_watchdog) on Linux systems. Adversaries may manipulate these files to disable the hardware watchdog's automatic reboot functionality, potentially to maintain persistence or to ensure system stability while performing malicious actions without the risk of an unexpected forced reboot.
Detects file access requests to the the Windows Data Protection API Master keys by an uncommon application.
This can be a sign of credential stealing. Example case would be usage of mimikatz "dpapi::masterkey" function
This can be a sign of credential stealing. Example case would be usage of mimikatz "dpapi::masterkey" function
Detects instances where a process other than the legitimate Discord client or its updater accesses the Local Storage/leveldb directory. This path contains cached authentication tokens and session data, making it a frequent target for credential theft malware such as SectopRAT in the FakeAgent campaign.
Detects suspicious processes based on name and location that access the windows credential manager and vault.
Which can be a sign of credential stealing. Example case would be usage of mimikatz "dpapi::cred" function
Which can be a sign of credential stealing. Example case would be usage of mimikatz "dpapi::cred" function
Detects unauthorized processes accessing Discord's local storage (LevelDB) or extracted Discord_Tokens.json files, a technique commonly used by malware like TOKYOCORE to hijack user authentication sessions.
Detects unauthorized access or staging operations on the C:\Windows\debug\NetSetup.log file. This activity is often associated with the NightLedger tool, which collects this diagnostic log alongside process listings for potential exfiltration. The rule filters out legitimate system processes that typically interact with this file during domain or workgroup join operations.
Detects the BlueNoroff VBScript implant discovering Telegram Web session files (IndexedDB LevelDB storage) via wscript/cscript, narrowed to invocations referencing the campaign's known implant filenames or numeric-suffixed staging variables to exclude unrelated browser or automation activity.
Detects processes accessing files within a user's ~/.ssh directory, excluding standard files like known_hosts and .pub files. This pattern is characteristic of malware attempting to exfiltrate SSH private keys, similar to the behavior observed in the mrmustard stealer.
Detects an unsigned process performing a specific behavioral sequence indicative of AI-assisted target profiling: high-volume software enumeration via registry keys, unauthorized access to browser data (history, cookies, bookmarks), and periodic C2-like network beaconing. The rule requires these actions to correlate within a tight 6-hour burst and exhibit persistent beaconing behavior over at least 3 days.
Detects an unsigned or unrecognized process performing read or modification operations on multiple types of credential stores (SSH keys, cloud CLI configurations, and environment variable files) within a short time frame. This behavior is indicative of credential harvesting or unauthorized looting of secrets from a developer workstation, similar to techniques used by stealer malware.
Detects SSH private key file access by scripting interpreters while excluding legitimate SSH/git tooling, matching the mrmustard stealer's SSH key theft behavior.
This rule aggregates SharePoint activity logs for a specific user within a defined timeframe. It groups activities by operation and file, intended to support investigation into potential mass file access or data staging behavior by a specific user in SharePoint.
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