Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
1,588 detections
Filters
Last updated
All Time
Detection languages
830
199
132
116
107
Contributors
608
289
249
88
63
Categories
20,440
11,814
5,871
5,145
4,864
1,588
Platforms
1,255
461
295
76
68
Products / Services
530
382
139
113
85
MITRE Techniques
629
565
504
437
297
CVEs
5
4
3
3
3
IDS Classtypes
5
2
2
1
IDS Protocols
5
3
2
Detects anomalous credential access behavior by identifying instances where a compromised explorer.exe process (injected with the Kothamine payload DLL MicrosoftEdgeUpdateCore.dll) accesses browser cookie stores and gaming credential files in quick succession.
Detects the use of ntdsutil.exe with arguments indicative of Install From Media (IFM) backup creation, correlated with subsequent access to the ntds.dit file on the same device within a one-hour window. This behavior is commonly associated with attackers attempting to harvest the Active Directory database for offline credential cracking.
Detects the creation of a Volume Shadow Copy followed by an attempt to copy the NTDS.dit file from the shadow copy device path. This sequence is a common technique used by attackers to bypass file locks and extract Active Directory credential databases.
Detects high-frequency access to the root of the C:\ drive using access rights typically required for directory change notification. This behavior is a heuristic proxy for processes attempting to monitor the entire file system (e.g., via ReadDirectoryChangesW), often associated with post-compromise reconnaissance or surveillance of file operations.
This rule detects unauthorized processes attempting to read sensitive configuration files (secrets.json, config.yaml) associated with AI coding assistants like Cline and Continue. By filtering out known, legitimate IDE and development processes, the rule highlights potential file-grabber activity indicative of an infostealer attempting to exfiltrate plaintext LLM API keys.
Detects suspicious process activity or file access patterns originating from within a container that indicate an attempt to escape to the host. This includes monitoring for the use of tools like nsenter, unshare, and mount, attempts to interact with the docker.sock, or attempts to access critical host paths like /proc/1/root or /host/etc, excluding events originating from authorized container management processes.
Detects interpreter processes (e.g., python, node, bash) typically used for AI agents accessing sensitive system files or directories outside of defined sandbox or workspace paths. This behavior suggests potential sandbox escape or unauthorized path traversal by an AI agent.
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 a burst of file access or download operations from SharePoint or OneDrive by a single user account within a short timeframe. This behavior is indicative of bulk data collection or exfiltration, often associated with post-credential-theft activity where an attacker stage and extract organizational data.
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 anomalous AI-assisted activity where multiple processes (specifically AI developer tools) appear to be chaining operations by writing to and reading from shared local directories (e.g., 'handoff', 'artifacts') followed by network requests to known AI service APIs. This pattern may indicate automated task chaining or unauthorized exfiltration of context/code to AI platforms.
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.
This rule detects potential data exfiltration by correlating access to files labeled as sensitive (confidential, restricted, etc.) with a subsequent upload to a personal cloud storage provider or transmission via email to a personal domain within a two-hour window.
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 use of PowerShell or Windows Script Host to access files within common web browser cache directories combined with indicators of Base64 decoding and pixel data manipulation. This behavior is indicative of extracting malicious payloads hidden via steganography within browser-cached image files, a technique often used in phishing campaigns to evade detection.
Detects potential insider threats or departing employees by correlating abnormal bulk data downloads from cloud repositories (SharePoint/OneDrive/Box/etc.) with subsequent sensitive data access and exfiltration signals. It uses baseline behavioral profiling, requires a threshold of sensitive file interactions, and mandates high-confidence exfiltration indicators (removable media usage, suspicious external domains, or multiple combined destination events) to reduce noise. Archive/staging activity is used as an optional confidence booster.
Detects potential insider threats or departing employees by correlating abnormal bulk data downloads from cloud repositories (SharePoint/OneDrive/Box/etc.) with subsequent sensitive data access and exfiltration signals. It uses baseline behavioral profiling, requires a threshold of sensitive file interactions, and mandates high-confidence exfiltration indicators (removable media usage, suspicious external domains, or multiple combined destination events) to reduce noise. Archive/staging activity is used as an optional confidence booster.
Detects potential insider threats or departing employees by correlating abnormal bulk data downloads from cloud repositories (SharePoint/OneDrive/Box/etc.) with subsequent sensitive data access and exfiltration signals. It uses baseline behavioral profiling, requires a threshold of sensitive file interactions, and mandates high-confidence exfiltration indicators (removable media usage, suspicious external domains, or multiple combined destination events) to reduce noise. Archive/staging activity is used as an optional confidence booster.
Page 5 of 76