Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
735 detections
Filters
Last updated
All Time
Detection languages
363
124
60
58
36
Contributors
222
123
75
46
32
Categories
336
254
143
140
102
Platforms
526
385
292
80
34
Products / Services
161
158
114
72
47
MITRE Techniques
18,116
15,638
12,585
8,395
6,225
735
CVEs
4
3
3
2
2
IDS Classtypes
17
2
1
1
IDS Protocols
15
2
2
1
Detects Git clone or fetch operations targeting bitbucket.org where the requested branch or ref is formatted as a SHA hash. This specific pattern is identified as a potential precondition for exploitation attempts targeting vulnerabilities like Plugin4Shell, where malicious branch names are used to trigger code execution on the server-side.
Detects the download of a dataset from an unapproved source that lacks hash verification, followed shortly by the launch of a model training process on the same host. This behavior is indicative of potential supply chain compromise or poisoning of the training data used in machine learning workflows.
This rule detects installation of known malicious npm packages associated with the PhantomSub campaign, which abuse WhatsApp spam channels. It also identifies network connections to known remote C2 channel-list URLs or domains used by these packages to automate channel joining.
This rule detects installation of known malicious npm packages associated with the PhantomSub campaign, which abuse WhatsApp spam channels. It also identifies network connections to known remote C2 channel-list URLs or domains used by these packages to automate channel joining.
This rule detects installation of known malicious npm packages associated with the PhantomSub campaign, which abuse WhatsApp spam channels. It also identifies network connections to known remote C2 channel-list URLs or domains used by these packages to automate channel joining.
This rule detects installation of known malicious npm packages associated with the PhantomSub campaign, which abuse WhatsApp spam channels. It also identifies network connections to known remote C2 channel-list URLs or domains used by these packages to automate channel joining.
Detects anomalous outbound network connections initiated by security and developer tooling (Trivy, Checkmarx, LiteLLM, Telnyx). This behavior is consistent with supply-chain attacks, such as those attributed to the UNC6780/TeamPCP threat group, where compromised tooling is leveraged to exfiltrate sensitive cloud credentials or API keys to external, non-vendor infrastructure.
Detects outbound HTTP GET requests for common AI model file formats (.pkl, .pt, .bin) from non-authorized or untrusted external domains. This activity is indicative of downloading potentially malicious pre-trained AI models, which can execute arbitrary code during the unpickling process (Supply Chain Compromise).
Detects the execution of a specific, tampered version (12.5.1) of the DAEMON Tools Lite installer, which has been identified as a distribution vector for malicious activity linked to Storm-3069.
Detects the use of package installation commands (pip, npm, npx) where the package name resembles popular AI/ML libraries (tensorflow, torch, openai, langchain) but is not an exact match for known legitimate versions. This is a common indicator of a typosquatting supply chain attack, where attackers attempt to trick users into installing malicious packages with similar names to trusted software.
Detects activity associated with the MALFEX npm supply-chain campaign (also known as Overlord/movinlike). This rule performs a sweep for known malicious file hashes, suspicious process command lines, outbound network connections to malicious domains or IPs, and email communications from identified adversary-controlled accounts.
Detects the installation or execution of known MALFEX malicious packages via npm or Node.js, which are used as entry points for polyglot or disguised payload delivery.
Detects an abnormally high volume of POST requests to the RubyGems API endpoint used for publishing packages. A sudden burst of these requests from a single source may indicate a mass upload campaign, commonly associated with software supply chain poisoning attempts.
Detects HTTP traffic attempting to download specific known malicious RubyGems packages from rubygems.org associated with the GemStuffer campaign. This indicates a supply chain compromise attempt where attackers distribute malicious code via public package repositories.
This rule detects the installation of common AI agent and orchestration frameworks (e.g., LangChain, AutoGen, CrewAI) using command-line package managers (pip, npm, conda) that point to non-standard or unverified package indexes. This behavior is a common indicator of a potential software supply-chain compromise, where an adversary attempts to inject malicious code by forcing the installation of packages from an attacker-controlled source.
Detects network activity associated with a malicious NPM package (typosquatting) beaconing to a known C2 server (69.48.229.140) and performing command and control operations, including command polling and data exfiltration, specifically from a Node.js environment.
Detects potential supply chain or local configuration tampering involving the Model Context Protocol (MCP). The rule identifies modification of common MCP configuration files, followed by the execution of a tool runner (node, python, npx) that spawns a scripting shell, which subsequently initiates an unusual outbound network connection.
Detects anomalous GitHub audit log activity indicative of a potential supply-chain compromise. The rule identifies workflows or automated entities (Bot/App) that access sensitive Action secrets followed by the publishing of packages to public registries like PyPI or npm. This activity can represent the precursor steps of token-theft-driven malicious package injection.
Detects the use of package installation commands (pip, npm, npx) where the package name resembles popular AI/ML libraries (tensorflow, torch, openai, langchain) but is not an exact match for known legitimate versions. This is a common indicator of a typosquatting supply chain attack, where attackers attempt to trick users into installing malicious packages with similar names to trusted software.
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
This rule detects scenarios where a node-based package manager (npm or npx) executes an 'install' command, which is immediately followed by the spawning of unexpected child processes such as interpreters (python, powershell, cmd, wscript, cscript, mshta) or network tools (curl). This behavior is characteristic of malicious npm packages used in supply chain attacks to deliver secondary payloads like BeaverTail, InvisibleFerret, or similar threats.
Page 2 of 35