Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
4,308 detections
Filters
Last updated
All Time
Detection languages
2,033
733
398
298
243
Contributors
1,048
740
503
372
239
Categories
20,437
11,828
5,871
5,150
4,864
4,308
Platforms
2,570
792
615
382
319
Products / Services
767
739
659
512
348
MITRE Techniques
2,347
917
847
814
635
CVEs
9
6
6
6
5
IDS Classtypes
81
66
44
14
14
IDS Protocols
136
79
11
7
4
Detects a service principal or user account performing a burst of 'ListKeys' operations across multiple Azure Storage accounts. This behavior is indicative of an attacker attempting to dump access keys for multiple storage accounts after initial compromise or privilege escalation to gain further access to sensitive cloud data.
Detects a service principal or user account performing a burst of 'ListKeys' operations across multiple Azure Storage accounts. This behavior is indicative of an attacker attempting to dump access keys for multiple storage accounts after initial compromise or privilege escalation to gain further access to sensitive cloud data.
Detects the behavior of LunexStealer targeting cryptocurrency wallets. The rule monitors for the enumeration of known crypto wallet files and browser-stored extension data, followed closely by the creation of a 'wallet.zip' archive by the same process, which is indicative of staged data collection for exfiltration.
Detects the enumeration of Wi-Fi profiles followed by the extraction of clear-text credentials for those profiles using the Windows 'netsh wlan' command. This pattern is commonly observed during post-exploitation activities to retrieve stored Wi-Fi passwords.
Detects the creation of a 'roblox_cookies.txt' file within a Roblox-related directory, correlated with a process command line containing the '.ROBLOSECURITY' token. This behavior is indicative of malware harvesting session tokens for credential theft.
Detects potential pass-the-hash activity through NTLM network logons (Type 3). The rule identifies anomalies such as mismatched workstation names relative to the source computer or NTLM authentication using zero key length material, which is often indicative of automated lateral movement attempts bypassing standard authentication processes.
This rule detects potential quishing (QR code phishing) attempts by identifying emails with one or more image attachments and minimal URL content, followed by a risky user authentication event within a 3-hour window. This behavior is indicative of an attacker attempting to harvest credentials via a malicious QR code that redirects the user to a phishing site.
Detects anomalous AWS IAM and Secrets Manager API activity indicative of credential abuse following suspected exposure. The rule identifies suspicious patterns such as unauthorized enumeration of access keys, secret retrieval, and creation of new keys by non-service principals, reflecting activity associated with ShinyHunters-style operations where compromised credentials are abused.
Detects anomalous network traffic patterns characteristic of the PetitPotam exploit, specifically targeting the MS-EFSRPC interface on Windows SMB pipes. This behavior is used to force a remote system to initiate authentication to a specified target, facilitating NTLM relay attacks.
Detects autonomous behavior where AI-driven processes attempt multiple network connections (SMB, SSH, HTTP, RDP, WinRM) to diverse targets following failed access attempts. The rule correlates initial failure activity with subsequent credential-related events and new lateral movement traffic from the same agent process within a short window, suggesting adaptive adversary pivoting.
Detects autonomous behavior where AI-driven processes attempt multiple network connections (SMB, SSH, HTTP, RDP, WinRM) to diverse targets following failed access attempts. The rule correlates initial failure activity with subsequent credential-related events and new lateral movement traffic from the same agent process within a short window, suggesting adaptive adversary pivoting.
Detects autonomous behavior where AI-driven processes attempt multiple network connections (SMB, SSH, HTTP, RDP, WinRM) to diverse targets following failed access attempts. The rule correlates initial failure activity with subsequent credential-related events and new lateral movement traffic from the same agent process within a short window, suggesting adaptive adversary pivoting.
Detects an suspicious progression of activities originating from identified AI assistant/agent processes. The rule monitors for a chain of behaviors occurring within a 15-minute window: execution of an AI agent/tool, followed by host discovery (network/system enumeration), access to sensitive local credential files, archival of data, and outbound network communication to common file-sharing/exfiltration platforms.
Detects anomalous, rapid enumeration of system, network, process, and software configuration settings by AI coding assistant processes (e.g., Claude, Cursor, ChatGPT). This behavior often signifies an AI agent being coerced or configured to perform environment discovery or credential gathering, indicating potential compromise or abuse of the LLM-integrated development environment.
Detects anomalous, rapid enumeration of system, network, process, and software configuration settings by AI coding assistant processes (e.g., Claude, Cursor, ChatGPT). This behavior often signifies an AI agent being coerced or configured to perform environment discovery or credential gathering, indicating potential compromise or abuse of the LLM-integrated development environment.
Detects anomalous, rapid enumeration of system, network, process, and software configuration settings by AI coding assistant processes (e.g., Claude, Cursor, ChatGPT). This behavior often signifies an AI agent being coerced or configured to perform environment discovery or credential gathering, indicating potential compromise or abuse of the LLM-integrated development environment.
Detects unauthorized processes reading Chromium or Firefox cookie and login stores that specifically reference Claude.ai or Anthropic domains. This rule is designed to identify infostealer malware attempting to hijack active Claude sessions by analyzing process file access and command-line arguments, filtered against known-legitimate security, sync, and development tools. The detection logic mandates corroborating evidence of suspicious execution paths or subsequent outbound network activity.
Detects high or medium severity security alerts related to Kerberos Golden Ticket forgery, as identified by Microsoft Defender for Identity. This rule filters for specific alert names indicative of forged TGTs minted from the krbtgt account, excluding generic or informational ticket anomalies.
Detects SectopRAT (Arechclient2) .NET RAT binaries deployed via tampered libcef.dll / JetBrains helper DLL sideloading that harvest browser credentials, cookies, credit card data, and files. Tightened to require combined sideload+family+target indicators plus .NET CLR import evidence, avoiding standalone generic strings.
Detects a sequence of potentially malicious actions indicative of credential theft, including LSASS memory access for dumping, followed by the acquisition of browser or cryptocurrency wallet files, and the subsequent staging of data in temporary directories, all correlated by the same process and device within a 15-minute window.
Detects the execution of known browser credential harvesting tools (WebBrowserPassView, WebBrowserBookmarksView, or WS_Password variants) from ScreenConnect temporary directories. This behavior often indicates post-compromise activity where an adversary uses an existing Remote Monitoring and Management (RMM) session to extract credentials from browsers.
Page 15 of 206