Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
4,311 detections
Filters
Last updated
All Time
Detection languages
2,035
733
398
299
243
Contributors
1,048
740
503
372
239
Categories
20,453
11,838
5,876
5,154
4,865
4,311
Platforms
2,572
793
615
382
319
Products / Services
767
740
659
513
348
MITRE Techniques
2,350
917
848
814
635
CVEs
9
6
6
6
5
IDS Classtypes
81
66
44
14
14
IDS Protocols
136
79
11
7
4
Detects suspicious access attempts by non-native processes to sensitive application data such as Discord local state, Telegram tdata, Steam SSFN tokens, and Roblox session cookies. This activity could indicate an attempt to steal session tokens or credentials for unauthorized access.
This rule detects suspicious script execution on macOS devices that targets known cryptocurrency wallet applications or data files. It looks for command-line executions involving common shell interpreters (sh, bash, zsh, osascript) that contain strings associated with Ledger Live, Trezor Suite, Exodus, Electrum, or generic 'wallet.dat' files. This activity could indicate an attempt to steal cryptocurrency wallet information or funds.
Detects unauthorized access (File Creation, Modification, Rename, or Deletion) to common web browser cookie and credential storage files by non-browser processes. This behavior is a common indicator of information-stealing malware attempting to extract session cookies and credentials for later use.
Detects cross-process access to the Local Security Authority Subsystem Service (LSASS) memory by processes that are not standard Microsoft-signed system components. This behavior is often indicative of credential dumping activity using tools like Mimikatz or other custom malicious utilities attempting to read sensitive memory contents.
Detects instances where non-browser processes access sensitive browser-related files (Login Data, Cookies, or Local State) located within AppData directories. This behavior is highly indicative of credential and session token theft, often associated with AiTM or post-compromise information harvesting activities.
This rule detects unauthorized processes attempting to create, modify, rename, or delete the 'Login Data' SQLite database files used by Google Chrome and Microsoft Edge to store saved credentials. By excluding legitimate browser processes and updates, the rule identifies potential credential theft attempts by infostealer malware or unauthorized actor tools.
This rule detects attempts by unsigned or suspicious processes (often residing in temporary directories) to access sensitive cryptocurrency wallet files such as 'wallet.dat', keystore JSON files, and files containing recovery seed phrases. This behavior is indicative of credential theft commonly associated with infostealer campaigns like DeepLoad and DeerStealer.
Detects instances where a process not associated with a known web browser attempts to read sensitive browser credential stores (such as Chrome Login Data, Edge Login Data, or Firefox key4.db/logins.json). This behavior is characteristic of infostealers attempting to extract saved browser credentials.
Detects anomalous script interpreters referencing CredUIPromptForWindowsCredentialsW to spoof password prompts. This function is typically used to display a credential prompt to the user. Its presence in the command line of scripting tools like PowerShell, cmd, or Python suggests an attempt to spoof legitimate credential prompts, potentially for phishing or credential harvesting.
Detects the use of 'sshpass' to supply a password to SSH while simultaneously disabling strict host key checking. This combination is often indicative of automated lateral movement or credential stuffing attempts where an attacker is trying to bypass host key verification to connect to multiple systems without manual intervention.
Detects access to Kubernetes secret objects containing sensitive configuration data
This rule looks for sensitive-looking values appearing in a publicly visible Claude workflow summary on GitHub. Public summaries can be indexed and copied quickly. Revoke any exposed credentials and restrict summary visibility. Ask the person who used the computer whether they installed new software or clicked a link, and involve your security team if the answer is unclear.
Detects LDAP queries enumerating user accounts in Active Directory
Detects attempts to invoke 'sudo' for privilege escalation or enumeration from non-interactive contexts such as scripts, scripting language interpreters, or web/application service processes. It also identifies 'sudo -l' or '--list' commands used for privilege enumeration.
Detects Microsoft 365 sign-ins using the device code grant flow from an unusual location or a previously unseen/unmanaged device. This behavior is consistent with OAuth phishing abuse, specifically 'device code phishing' where an adversary tricks a user into entering a code/credentials on a malicious authorization page to obtain a device token.
This rule detects POST requests to the URI '/api/device/start' which is indicative of the EvilTokens phishing kit attempting to initiate a Microsoft OAuth Device Code flow. This technique is often used in phishing campaigns to gain unauthorized access to user accounts by tricking them into authenticating through a device code.
Detects the 'strings' command being run directly against /proc/*/mem to extract human-readable strings (such as tokens or passwords) from raw memory. This activity is often associated with credential dumping techniques on Linux systems.
Detects the explicit execution of the 'gh-token-monitor.sh' script, which is indicative of active credential harvesting on the endpoint. This script is designed to monitor and potentially exfiltrate GitHub tokens.
Detects the use of the 'watch' command in conjunction with 'cat' to continuously poll the GitHub CLI config file (.config/gh/hosts.yml). This activity could indicate an attacker attempting to capture new GitHub tokens as soon as a developer logs in.
Detects the Pony malware credential harvesting sequence where a non-system process performs repeated interactive logon attempts (LogonUserA dictionary brute force via NetUserEnum) against local accounts and achieves at least one successful logon, after which LoadUserProfileA is used to impersonate the compromised account and harvest profile-scoped credentials.
This rule detects attempts to extract authorization headers (Bearer, Basic, or X-API-Key) over FTP (port 21) by inspecting network signatures. This could indicate an attempt to exploit a vulnerability or misconfiguration where sensitive credentials are being transmitted in an insecure manner.
Page 124 of 206