Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
2,294 detections
Filters
Last updated
All Time
Detection languages
1,284
250
199
142
138
Contributors
660
544
375
96
95
Categories
20,456
11,839
5,876
5,155
4,865
2,294
Platforms
1,505
929
381
70
51
Products / Services
743
407
225
164
140
MITRE Techniques
856
683
604
437
302
CVEs
12
10
9
9
8
IDS Classtypes
24
4
3
1
1
IDS Protocols
20
6
6
1
This rule detects potentially malicious activity involving LNK files being modified, created, or renamed in common locations (Desktop, Quick Launch, Start Menu) correlated with the creation of files ending in '.backup' within 30 minutes. It also independently detects the presence or execution of 'VLCAssistant.exe', which may indicate unauthorized launcher activity or masquerading.
Detects a suspicious sequence of events where a process modifies a Windows Registry Run key to ensure execution at logon, followed shortly by the same executable file being copied into the current user's startup folder. This pattern is often used by malware to establish persistence.
Detects patterns associated with the TeamFiltration backdoor module, specifically unauthorized OneDrive/SharePoint file modifications occurring shortly after an authentication from a previously unseen device/IP. The rule flags file changes (modifications, deletions, or renames) involving potential executable or script extensions, or those performed by the OneDrive SyncEngine app from a new source.
Detects a multi-stage attack pattern involving a non-MFA login to a Citrix portal, followed by lateral movement, and concluding with ransomware deployment behaviors such as shadow copy deletion, recovery disablement, or the dropping of ransom notes associated with ALPHV/BlackCat ransomware.
Detects a multi-stage attack pattern involving a non-MFA login to a Citrix portal, followed by lateral movement, and concluding with ransomware deployment behaviors such as shadow copy deletion, recovery disablement, or the dropping of ransom notes associated with ALPHV/BlackCat ransomware.
Detects a multi-stage attack pattern involving a non-MFA login to a Citrix portal, followed by lateral movement, and concluding with ransomware deployment behaviors such as shadow copy deletion, recovery disablement, or the dropping of ransom notes associated with ALPHV/BlackCat ransomware.
Detects a multi-stage attack pattern involving a non-MFA login to a Citrix portal, followed by lateral movement, and concluding with ransomware deployment behaviors such as shadow copy deletion, recovery disablement, or the dropping of ransom notes associated with ALPHV/BlackCat ransomware.
Detects unauthorized or suspicious modifications to AI model artifacts, including fine-tuned weights and LoRA adapters. This rule identifies tampering attempts via unsigned uploads, bypassing CI/CD production deployment approvals, or mismatches in hash/checksum compared to known-good versions.
Detects potential poisoning of an AI agent's RAG knowledge base or vector store through suspicious bulk ingestion or injection of imperative instruction-style content. The rule correlates these ingestion events with subsequent anomalous tool usage patterns by the same agent session, indicating a possible prompt injection or data manipulation attack aimed at altering agent behavior.
Detects malicious JavaScript injection on e-commerce checkout or payment pages designed to harvest sensitive payment card information (PAN, CVV, expiry) and exfiltrate the stolen data to an external endpoint. This behavior is indicative of a digital credit card skimmer (e.g., Magecart style) deployed following a compromise of the web application.
Detects the initialization of cloud synchronization tools like rclone or MEGAsync, accompanied by the creation of configuration files and subsequent high-volume network activity directed toward common cloud storage providers, indicative of unauthorized data exfiltration.
This rule monitors for unauthorized changes to GitHub Actions workflows that involve release or publishing processes, specifically looking for indicators of supply chain compromise like the introduction of 'id-token: write' or changes to publish scripts. It correlates these modifications with subsequent workflow executions and npm registry publish events attributed to the same actor to identify potential account takeover and malicious package distribution.
KQL Query
Detects potential poisoning of machine learning training data by identifying unauthorized file modifications to datasets, DVC repositories, or model registries that occur outside of authorized ETL or CI/CD windows.
Detects the creation or writing of disk image files (.iso, .img, .vhd, .vhdx) followed by the execution of suspicious file types (.lnk, .exe, .js, .vbs, .cmd, .bat, .scr) by explorer.exe within a 10-minute window. This behavior is indicative of an adversary using container files to bypass security controls or execute malicious payloads from mounted images.
Detects anomalous mass file modification or renaming activity often associated with ransomware, combined with the deletion of Windows Volume Shadow Copies using native utilities like vssadmin or wmic, and the presence of suspicious ransom note filenames.
Detects instances where a process not associated with standard web browsers accesses, copies, or modifies sensitive browser files such as cookies, local storage, or session tokens. This behavior is highly indicative of information-stealer malware (e.g., Vidar, Lumma, RedLine) attempting to exfiltrate session data for account hijacking.
Detects a multi-stage attack chain where an AI-agent's configuration file (e.g., mcp.json) is modified, followed immediately by the execution of a tool process (node/python) spawned by the agent, the subsequent spawning of a shell process, and finally an outbound network connection to a rare, non-standard domain. This sequence is indicative of AI agent tool poisoning, where malicious tool definitions are introduced to execute arbitrary commands and exfiltrate data. Coverts T1195.002, T1565.001, T1059
Detects a multi-stage attack chain where an AI-agent's configuration file (e.g., mcp.json) is modified, followed immediately by the execution of a tool process (node/python) spawned by the agent, the subsequent spawning of a shell process, and finally an outbound network connection to a rare, non-standard domain. This sequence is indicative of AI agent tool poisoning, where malicious tool definitions are introduced to execute arbitrary commands and exfiltrate data. Coverts T1195.002, T1565.001, T1059
Detects a multi-stage attack chain where an AI-agent's configuration file (e.g., mcp.json) is modified, followed immediately by the execution of a tool process (node/python) spawned by the agent, the subsequent spawning of a shell process, and finally an outbound network connection to a rare, non-standard domain. This sequence is indicative of AI agent tool poisoning, where malicious tool definitions are introduced to execute arbitrary commands and exfiltrate data. Coverts T1195.002, T1565.001, T1059
Detects a multi-stage attack chain where an AI-agent's configuration file (e.g., mcp.json) is modified, followed immediately by the execution of a tool process (node/python) spawned by the agent, the subsequent spawning of a shell process, and finally an outbound network connection to a rare, non-standard domain. This sequence is indicative of AI agent tool poisoning, where malicious tool definitions are introduced to execute arbitrary commands and exfiltrate data. Coverts T1195.002, T1565.001, T1059
Page 12 of 110